Google is still helpful, obviously, but AI Mode in Google is already solving most of my tasks without the need to click blue links and open multiple tabs.

And it’s only been around for several years.

So despite all the popular myth-busting around “LLM traffic is less than 5% of overall traffic,” the shift toward LLMs feels inevitable to me. The adoption will surely grow further, and as marketers, we have to embrace it and adapt.

What’s interesting is that we’re already seeing this in our own business.

When I ask potential clients where they heard about us, many of them say:
“ChatGPT” or “other LLMs.”

For an agency that helps SaaS brands get cited by AI, that’s quite a strong signal that this channel is working.

At Quoleady, we research LLM visibility from a SaaS B2B angle. We focus specifically on high-intent prompts, the kind buyers use when they’re comparing options or looking for alternatives to tools they already use.

This article is everything we’ve learned. It’s built on our own research and data from thousands of AI citations, combined with direct input from experts: SEO specialists, content strategists, agency founders, and GEO consultants, who shared what’s actually working in their client work right now, what they’ve tested, and what they’ve stopped doing. 

Want AI to Cite You? Start With Listicles

I said it before: “It’s rare to find a marketing effort that can compete with listicles on ROI.”

Quoleady is featured in 90+ listicles across the internet for B2B SaaS content marketing. Getting featured in those listicles is what built our AI visibility, and it’s the same work we do for our clients. From our own data, we’ve found a strong correlation between appearing in top-ranking listicles and showing up in high-intent AI-powered answers.

Top-ranking listicles are the dominant citation source across AI tools. Our LLM citations study of 10,000 citations found that they account for 50% of all citation sources.

So if you’re a SaaS company or agency looking to get cited by AI, you need to publish listicles on your own website and get featured on third-party sites through outreach. The more listicles you appear in, the better your LLM visibility.

The goal is to target top-ranking listicles for your core business keywords, like: “tools”, “software”, “alternatives”. Headlines like: “Top 11 LLM Visibility Tools in 2026” and “Best Profound Alternatives for Agencies” are exactly the format you’re after.

But why does this matter so much? 

In SaaS, potential customers rarely make quick decisions. Before committing, they research thoroughly and compare options. They look for brands that understand their problem and feel equipped to solve it. 

Listicles meet that need perfectly. They offer structure, a clear breakdown of options, and obvious next steps, making them one of the most effective formats for reaching high-intent buyers. These are the articles people read right before making a purchase decision.

Sreeram Sharma, SEO Consultant at Angleout, puts it well: “I mainly work with SaaS websites, and whenever we begin an SEO campaign, the very first thing I suggest is to publish more BOFU and MOFU content. This primarily includes listicles, alternatives, and comparison guides. We have also checked it on Google Analytics, and these are the pages that have brought us the most traffic from AI overviews, apart from the homepage.”

If you want practical tips on how to write listicles that get cited by AI, this article is worth a read.

Not All Listicles Get Cited: What Separates the Ones That Last?

Listicles may be the top format for AI citations, but they’re also hard to write well. A 10-item listicle is essentially 10 mini-articles in one. Each entry needs its own description, use case, features, pros, cons, and pricing. It takes real time and research to get right.

Most listicles online skip that work. They rehash the same points, rely on keyword stuffing, and hope nobody notices. The ones that actually get cited are built on real experience: tested tools, honest opinions, and specific details that readers can’t find anywhere else.

We stand by this at Quoleady. Our Director of Operations, Iryna Kutnyak, puts it this way: “A key part of the process was actually testing each competing tool ourselves rather than compiling information from existing articles. We created accounts, set up campaigns, explored features, and documented differences between products. This allowed us to include concrete details and real comparisons instead of repeating the same information already circulating online.”

She also noted that publishing large volumes of generic, AI-optimized content tends to underperform: “Listicles based on real product testing are much more likely to appear in AI answers than articles that simply compile information from secondary sources.”

Ugljesa Djuric, Founder and CEO at ContentMonk, echoes this: “Write deep, honest listicles and comparison articles. Cover the real questions buyers have. Include genuine comparisons and opinions. Do it well, and AI doesn’t just cite you—it starts forming opinions about your entire category based on your content.”

Content Depth and Expertise Matter More Than Volume

Most advice about AI visibility defaults to the same playbook: publish more, optimize harder, scale faster. Amit Raj, Founder and Link Building Consultant at The Links Guy, ran an experiment that challenges all of that.

Over the past year, he has published zero new content. Instead, he took six existing blog posts and refreshed them heavily with practitioner-only observations and specific examples. “After refreshing them, I started appearing in more AI Overviews for relevant US keywords.”

That’s what actually made the difference. Forget about the word count or publishing frequency and focus on making sure your content adds something that doesn’t already exist online.

He also noticed something interesting: “My ‘Manual Link Building’ page wasn’t refreshed and wasn’t ranking highly, but it still appeared in AI Overviews. It has strong internal linking through my mega menu and footer. Google may be treating it as a topical hub, which seems to compensate for recency.”

It’s a reminder that topical authority and domain authority, which AI systems understand through smart site structure and internal linking, can carry weight, even when individual pages haven’t been recently updated.

Katya Rozenoer, Co-founder of Blastra, tells the same story from a different angle. Starting with an unknown domain and zero reputation, her team skipped generic SaaS content entirely and went deep on their niche: B2B software directories. They built a knowledge hub around it, a directories database, guides for each directory, a catalog of software badges and awards.

The results came faster than expected. “We started this content work just about a month ago, and it’s already bringing results. Try asking an LLM how to earn Capterra badges, how Trustpilot recognition works, whether Gartner Peer Insights is worth it, or how to get ranked on G2—we will be listed among the sources, often above the original sources themselves.”

She says: “There are no hacks or easy recipes. We ensure our brand has the authority of directories that explain to LLMs what we do, and then our content speaks for itself.”

Using Stats, Data, and Expert Citations Makes Your Content More Visible to AI

Did you know that adding statistics, expert citations, and quotes from relevant sources can boost your content’s visibility in AI-generated responses by up to 40%? Researchers from Princeton University and IIT Delhi confirmed this after testing nine generative engine optimization strategies against 10,000 real-world queries to find out what actually gets content cited by AI.

The reason these elements have such a strong impact comes down to how AI platforms are built. When you ask ChatGPT or Perplexity a question, the platform doesn’t just generate an answer from memory. It first retrieves relevant content from the web, evaluates it, and uses it to construct a response. 

This process is called Retrieval-Augmented Generation, or RAG, and it’s what makes the quality of your content so important for AI visibility. 

AI systems look for sources that appear credible and trustworthy. A named expert with a title, a company, and something specific to say is one of the clearest trust signals they can detect. Original statistics, proprietary research, and concrete numbers work the same way. They give AI something solid to anchor its answers to.

Expert quotes also help you reach a wider audience (if you make sharing easy). When you feature someone in your article, they have a reason to share it. Their name is on it, their insight is in it, and most contributors will post it on LinkedIn without you even having to ask. Tools like RoundupHero make this easier by turning each contribution into something contributors can quickly share.

So one article with ten expert quotes can turn into ten LinkedIn posts, each reaching a completely different audience, boosting citation odds, improving AI retrieval, and driving direct traffic back to your site.

Third-Party Source Coverage Is the Gap Most SaaS Brands Miss

Listicle outreach has become very competitive and expensive. Some of the listicles can go as high as $9,000, based on our experience. But it’s also very important to do it, and the prices actually signal how much the market values these placements. 

When you launch a new SaaS product, nobody knows you exist. You can publish the best content in the world, but if the only place talking about you is your own website, AI systems have very little to work with. 

That’s why third-party source coverage is so important. The more places that mention you, describe what you do, and associate you with the right keywords, the clearer the picture AI has of your brand. For new businesses, especially, it’s how you get on the map.

Iryna Kutnyak, Director of Operations at Quoleady, has seen this play out directly with clients: “AI answers tend to aggregate information from multiple sources, so when a product appears both on its own comparison pages and across independent listicles, it becomes easier for models to recognize and reference it.”

The results back this up. “On one client campaign, the comparison content cluster alone generated 30–40% of new organic traffic within a few months, with conversion rates two to three times higher than standard blog posts.” That’s the compounding effect of showing up in multiple places at once: better AI visibility, more organic traffic, consistent brand signals across the web, and higher-intent visitors when they land.

That said, not all placements deliver the same return. Evelina Milenova, Senior SEO Specialist at Hostinger, secured 67 listicle features for a single brand in late 2025 and tracked exactly which ones held up. One placement stood out: a listicle on a direct competitor’s site targeting the main comparison keyword for that category. It drove AI Overview appearances for four months! Most other placements: affiliate sites, guest posts, and industry news sites disappeared from AI citations within two weeks.

Her conclusion: “If your link-building resources are limited, exploring ways to get mentioned on competitor listicles will give better and longer-lasting results. LLMs have a strong recency bias, so if you stop producing new content or updating existing content, citations will sharply drop within a month.”

The same logic applies to Digital PR. Matthew Foster, Senior Digital PR Manager at Distinctly, found that not all media coverage carries equal weight when it comes to AI visibility. His team analyzed 6,780 data points across 452 UK publications to understand which ones AI crawlers can actually access, so they could target those when building media lists for clients. 

It worked: “By prioritizing AI-open publications, we secured coverage in The Independent for a client, and that coverage began appearing in AI results for related keyphrases. Digital PR can influence AI visibility in two ways: directly, through coverage in publications AI crawlers can cite, and indirectly, by building the brand’s authority and expertise over time.”

The takeaway across all three examples is the same: third-party coverage matters, but where you appear matters just as much as how often.

Are Review Platforms Still Worth It for AI Visibility?

Review platforms like G2, Capterra, and GetApp are just as important to get right.

At Quoleady, we tested dozens of high-intent “alternatives” keywords to find out how G2 and Capterra Reviews Influence ChatGPT Rankings. Think “Trello alternatives,” “Zapier alternatives,” and “Slack alternatives”. Then we asked ChatGPT for its recommended tools for each and analyzed how much weight review platforms carried in shaping those responses.

The results were clear: 100% of the tools mentioned in ChatGPT answers had reviews on Capterra, and 99% had reviews on G2. These platforms act as a basic inclusion signal. If you’re not listed, you’re likely invisible to AI altogether.

That said, review presence alone won’t get you ranked. We also found that some tools with strong review profiles still ranked lower than others with fewer reviews and weaker ratings. G2 and Capterra help establish legitimacy in the eyes of AI, but they don’t determine placement. Being on review sites is just the starting point. 

Be sure to check out our entire LLMO research.

Reddit and LLMs: How Much Do They Overlap?

Review platforms are not the only place where AI picks up signals about your brand. User-generated content (UGC) platforms like Reddit and Wikipedia matter too, and the data backs it up.

Reddit’s dominance in Google search grew significantly between 2023 and mid-2025, making it the second-most visible domain in Google Search in the US, according to Sistrix. But the picture has shifted since then. Reddit’s search visibility began declining after August 2025, dropping to the fourth-most visible domain in Google USA. 

We ran an experiment to find out whether Reddit influences what AI tools recommend. We took dozens of high-intent “alternatives” keywords where Reddit ranks in Google’s top five search results, analyzed which tools Reddit was recommending, then asked ChatGPT, Claude, Perplexity, and Gemini the same questions and compared the results.

The overlap between Reddit SEO and LLM optimization was real but partial. Perplexity aligned with Reddit most often at 39%, followed by Gemini at 38%, ChatGPT at 33%, and Claude at 31%.

Well-known tools like Notion, Slack, and ClickUp showed up everywhere. But some tools that dominated Reddit threads never appeared in a single AI response, usually because their mentions were buried in posts Google couldn’t crawl or index properly.

Reddit does influence AI answers, but indirectly. If Reddit ranks for your keywords, being part of those conversations is worth it. But it’s just one piece of a much broader source coverage picture.

Rokas Stankevicius, Founder of AIclicks, has seen this firsthand: “The biggest insight from tracking brands: the gap between you and your top competitor is almost always source coverage, not content quality. AI engines pull info from Reddit threads, blog posts, directory listings. Once you know which sources aren’t mentioning you, the actions become obvious. Brands that follow this process consistently see 20–25% visibility gains within a few weeks of taking action.” 

Andrii Shum, Head of SEO at SeoProfy, agrees that source coverage matters more than any single platform: “The key thing is trying to get your brand mentioned on the pages that are cited in AI answers, whether it’s Clutch or Reddit is secondary.”

He also flags something worth keeping in mind: tracking the impact of these efforts is genuinely difficult. In most cases, LLMs don’t provide a direct link—users copy the brand name and search for it separately. “We monitor prompts in Ahrefs Brand Radar, but the data is still far from accurate.”

Google Rankings and AI Citations Go Hand in Hand

Optimizing for AI visibility builds on the same fundamentals that have always driven good SEO: authoritative content creation, strong backlinks, clear site structure, and topical depth.

AI systems and traditional search engines are both trying to solve the same problem: find the most credible, relevant content chunks for a given query. They often reach the same answer.

Stanislav Farkas, SEO Specialist at FASTrategies, tested this directly. He compared the sources cited by Perplexity with Google Search results for the same query and found a strong overlap: most of the pages Perplexity cited were already ranking on page 1.

His conclusion is practical: “Ranking in top Google positions is still the biggest driver of appearing in AI answers. Focus on BOFU and money pages, build strong topical authority and brand presence. Use an 80:20 approach: a solid SEO foundation making up around 80%, complemented by AI-focused optimization for the remaining 20%. Once pages reach top positions and demonstrate real expertise, they start getting picked up more consistently in AI answers.”

The takeaway is simple. You don’t need to choose between traditional SEO and GEO. Chasing AI visibility in the AI era without a strong SEO foundation is building on sand. Get the fundamentals right first, and AI visibility tends to follow.

Amit Raj, Founder and Link Building Consultant at The Links Guy, saw the same pattern play out through his own link-building activity. Over nine months, he built around 46 backlinks to his site, seven of which carried branded anchors, including a placement on an SEO courses listicle. 

The result: LLM traffic nearly doubled, going from 89 clicks in the previous six months to 169 in the following six.

In his own words: “Small sample, but hard to call a coincidence given how competitive my target keywords are.”

It’s another example pointing in the same direction. Stronger SEO signals: backlinks, brand mentions, and authoritative placements feed into the same credibility framework that AI systems rely on.

Matt Emgi, Founder at Emgi, ran a controlled experiment to dig deeper into how backlinks improve AI visibility. 

He built content pages targeting high-intent BOFU keywords, then ran those queries through ChatGPT and AI Overviews before writing, aiming to capture the exact phrasing AI systems use to describe product features, category benefits, and model advantages. 

He wrote the pages using that same vocabulary, then built backlinks with anchor text and surrounding context that matched the same language and framing. Within four weeks, the pages were being cited in both Google AI Overviews and ChatGPT.

His conclusion: “AI systems cite the page that says what they already think the answer is, in the words they already use. If you write in your own internal product vocabulary, you’re invisible. If you mirror the language the model is producing, you become an easy citation.”

That said, the overlap isn’t total. Do you have an article that gets zero traffic and ranks for no keywords? Most people do. Well, it can still be cited by ChatGPT.

Ahrefs research found that 76% of AI Overview citations come from pages ranking in Google’s top 10. But ChatGPT behaves differently: it cites content outside the top 10 far more often. So while Google rankings are still the strongest predictor of AI visibility overall, they’re not the only path in. Some content that never wins in search is quietly doing its job inside AI conversations.

As I’ve put it in a recent LinkedIn post: “Content doesn’t ‘not work.’ It works in different places now.”

Jared Carrizales, Fractional SEO Manager at JaredCarrizales.com, adds a technical dimension relevant to marketing teams and brand marketers working on AI visibility: “AI platforms need credibility and confirmation when considering what to include in answers. Authoritative mentions and high-quality backlinks drive credibility. The best place for confirmation, in my opinion, is relational structured data on the website itself.”

He tested this with a large manufacturer that had solid credibility and decent site architecture, but wasn’t showing up in any AI platform. After implementing schema markup across their materials pages and elaborating on the chemical makeup of their products, many of those pages began appearing on multiple AI platforms within days. 

Rick Tousseyn, GEO & Content Strategist at OtterlyAI, adds an important nuance here. His own experiments found that schema markup’s impact on AI search is more limited than many assume: “Many AI search platforms strip the schema code when converting webpages to markdown. Research data shows that the impact of structured data is largely limited to the Google ecosystem: classic search, AI Mode, and AI Overviews, rather than broader LLM visibility.”

The pattern both experts point to is the same: structured data works as a confirmation layer, but only when external credibility signals are already in place.

What Practitioners Have Stopped Doing

LLM visibility is still new enough that many tactics get hyped before anyone has properly tested them. Practitioners who have been doing this long enough have started to notice what moves the needle in digital marketing and what just sounds good on LinkedIn.

Iryna Kutnyak at Quoleady was direct about one of the most common mistakes: “Publishing large volumes of generic ‘AI-optimized’ content rarely moves the needle unless the page already has strong structure, topical authority, and external mentions.” Writing specifically for large language models (LLMs), without the fundamentals in place, doesn’t work.

Stanislav Farkas at FASTrategies echoes this: “Shortcuts and overusing listicles don’t work as well as people think. Publishing large volumes of ‘best tools’ articles outside your niche and inserting your product everywhere might work short-term, but it’s not sustainable. AI systems increasingly favor relevance, authority, and consistency over volume.”

Andrii Shum at SeoProfy adds that classic guest posts, where the brand is barely mentioned, don’t help with LLM rankings either. And Sreeram Sharma at Angleout tested llms.txt files across multiple client sites and saw no improvement in visibility whatsoever.

The honest conclusion belongs to Ugljesa Djuric at ContentMonk: “There are no universal rules here. What works for one set of prompts in one industry might not work in another, or even for different prompts in the same industry. That’s why tracking daily data with an AEO tool matters. You need to see what’s actually happening and act on it.”

What Gets Tracked Gets Improved

Ugljesa’s point holds true across every channel: if you’re not measuring what’s happening, you’re optimizing in the dark. With LLM visibility, that problem is even harder to ignore. 

Unlike traditional SEO metrics like Google rankings or search volume, which you can check daily in Google Search Console, AI citations are harder to see. Your brand might be surfaced by AI assistants dozens of times a day or completely ignored. There is no way to know for sure without the right tools.

AI monitoring tools track where and how often your brand gets mentioned across major LLMs, the sentiment surrounding those mentions, which competitors are being cited instead of you, and how you can improve your AI visibility. 

There are many LLM tracking tools on the market, such as Peec AI, Profound, Scrunch AI, and Otterly. They all track brand visibility across AI platforms in a similar way, but differ in features, depth, and pricing. 

We built Allmond out of our own frustration. Working with SaaS brands on AI visibility, we kept running into the same problem: no easy way to show clients where they actually stood. Which prompts surfaced their content, which didn’t, and where the real gaps were.

So we built the tool we needed. Allmond is very easy to use, with a clean, no-fuss interface that shows you only what you need to act on. It tracks brand visibility across major LLMs, covers 67+ countries, and generates shareable reports in seconds. 

There are no limits on prompts, projects, or clients. That keeps it affordable and accessible for teams of any size, whether you’re a small SaaS just getting started or an established product keeping tabs on competitors in AI-powered search.

Where to Start With LLM Visibility in 2026

Every practitioner we spoke to for this article came back to the same three pillars: original content, third-party presence, and LLM tracking. The tactics vary by industry, and as Ugljesa Djuric put it, there are no universal rules. But these fundamentals hold across the board. Here’s how to put them into action.

1. Audit where you currently stand. Before doing anything else, run your core keywords as prompts across ChatGPT, Claude, Perplexity, and Gemini. Note which competitors appear, which sources get cited, and where your brand is missing. This gives you a baseline to work from and makes the gaps immediately obvious.

2. Build content that adds something new. Generic, AI-optimized content published at volume doesn’t work. What works is content built on real experience, first-hand observations, and specific examples that don’t already exist online. Refresh existing posts with practitioner-level insight before creating new ones. Before writing, run your target keywords through ChatGPT and AI Overviews and mirror the exact phrasing they use. AI systems cite the page that already sounds like their answer.

3. Prioritize BOFU content formats. Listicles, comparison articles, and alternatives pages are the formats AI engines cite most. They’re also what high-intent buyers read right before making a purchase decision. If you don’t have them, start there. And mind the placements: competitor listicles tend to last months in AI citations, while affiliate sites and guest posts often disappear within two weeks. For digital PR, prioritize publications that AI crawlers can actually access.

4. Get listed on review platforms. Our research found that 100% of the tools cited by ChatGPT for alternatives queries had reviews on Capterra, and 99% had reviews on G2. Being listed is the minimum requirement for AI inclusion. If you haven’t claimed and optimized your profiles, do it now.

Beyond the major platforms, independent review sites are increasingly cited as sources in AI-driven discovery. CheckMySaaS is one worth knowing: you can submit your tool for independent researchers to review based on real usage, covering genuine pros, cons, and day-to-day experience. That kind of unbiased coverage carries weight with AI systems.

5. Get featured in third-party listicles. Outreach to high-ranking listicles in your category is one of the highest-ROI activities to win LLM visibility, even if it’s competitive and sometimes expensive. Focus on getting featured on competitor sites and established players in your niche first, as those placements tend to last longer and carry more weight. Remember that source coverage matters more than any single platform.

To find the right opportunities and manage the process, ListicleManager helps you discover relevant listicles in your category, identify which ones are worth targeting, and run the outreach.

That way, your product gets in front of buyers who are already looking for a solution like yours, including those arriving via zero-click searches who never visit a traditional results page.

6. Be active where your buyers talk. Reddit still influences AI answers, particularly for tools with consistent, genuine community presence. If relevant subreddits exist for your category, participate authentically. Buried, link-free mentions don’t carry weight, but visible, active participation does.

7. Add expert quotes and original data to your content. Research from Princeton University and IIT Delhi found that adding statistics, citations, and expert quotes can boost AI visibility by up to 40%. Every original data point and named expert you include is an additional signal for AI systems to work with.

8. Implement structured data. Schema markup helps AI systems understand and confirm what your content is about, but only when the off-site credibility is already there. As Jared Carrizales put it, off-site factors are the trigger, and the website is the confirmation. That said, keep expectations realistic: research shows that schema markup’s impact is largely limited to the Google ecosystem and has less influence on broader LLM visibility.

9. Don’t abandon SEO strategy. Google rankings and AI citations overlap more than most people expect. A strong SEO foundation, topical authority, quality backlinks, and well-structured pages remain the most reliable path to AI visibility. Use an 80/20 approach: solid SEO fundamentals first, with answer engine optimization layered on top.

10. Measure LLM visibility and adjust. Use an AI tracking tool to monitor brand visibility across AI models, track which prompts surface your content, and identify where competitors are showing up instead of you. Platforms like Peec AI, Profound, Otterly, and Allmond help with this—find the one that fits your team’s size and workflow.

If you’d rather have experts handle this, Quoleady works with SaaS brands to build content, secure placements, and systematically improve LLM visibility. Everything covered in this article is what we do every day. Schedule a call and let’s talk.

LLM Visibility FAQs

How do I get my SaaS brand cited in AI-generated answers?

Focus on three things: publish original, experience-based content in formats AI search engines favor (listicles, comparisons, alternatives pages); build third-party presence through review platforms, listicle features, digital PR, and community mentions; and earn quality backlinks and brand mentions on trusted sites. The more places that reference your brand in the right context, the more likely AI systems are to cite you.

Do strong Google rankings still influence AI citations in 2026?

Yes, significantly. Research shows a strong overlap between pages that rank on Google’s first page and pages cited by AI tools. That said, informational queries tend to show less overlap. ChatGPT in particular cites content outside the top 10 more often than Google’s AI Overviews do. A strong SEO foundation remains the most reliable path to AI visibility, but it’s not the only one.

How do I track whether my brand is appearing in AI answers?

You need an LLM monitoring tool. These tools run prompts across major AI platforms and show you where your brand appears in AI-driven search, where competitors are cited instead, and which topics you’re missing entirely. Tools like Allmond make this straightforward: you can track visibility across ChatGPT, Gemini, Claude and Perplexity, spot gaps, and generate shareable reports without needing a dedicated analyst.

The post LLM Visibility in 2026: 10+ GEO Experts Share What’s Working appeared first on Quoleady.

That still happens. But more and more often, the process starts somewhere else entirely.

A founder opens ChatGPT and types, “What’s the best tool for X?” A VP of Marketing asks Perplexity to compare two platforms before booking a demo. The AI gives them an answer. They start their evaluation from that response. If your product isn’t in that answer, you’ve already missed the shortlist.

This is why so many SaaS companies want Answer Engine Optimization for their B2B product. And if you’ve been trying to make sense of what AEO means and how it’s different from SEO, GEO, LLMO, and the rest of the acronyms floating around LinkedIn, you’re not alone. The terminology is all over the place right now. Every agency seems to have their own name for roughly the same idea, and the definitions shift depending on who you ask.

Know more about Top 9 Generative Engine Optimization (GEO) Agencies of 2026

How I selected these AI optimization agencies

Over the past few weeks, I spent time looking at agencies working in this space. The goal was to figure out who is doing meaningful work and who just updated their website copy to sound more “AI-focused.”

• SaaS experience: SaaS buying behavior is very different from ecommerce or local services. The agencies included here either focus heavily on B2B SaaS and technology or have enough experience in the space that their strategies clearly reflect how SaaS tools are researched and evaluated.
• Evidence of real AEO work: Not just in how they talk about AI visibility, but in the services they offer and the work they publish.
• Proof of results: Case studies, prompt tests, screenshots of enterprise brands appearing in AI answers. Concrete examples carry far more weight than claims.
• Authority building across the web: Agencies that actively pursue placements in listicles, comparison pages, review platforms, and respected publications tend to have a stronger foundation here.
• Thought leadership SEO: Teams that share original research, frameworks, and honest observations about how AI discovery is changing are usually genuinely close to the work.

Top 8 Best AEO Agencies for B2B SaaS

1. Quoleady

Overview

Quoleady is a B2B SaaS SEO and AI visibility agency helping software companies grow through organic search, digital PR, and expert-driven content marketing. With over six years of experience working exclusively with SaaS and technology brands, the team helps clients improve visibility in Google and AI search, build authority in their category, and drive qualified pipeline and revenue growth.

Why they’re relevant for AEO

Quoleady builds AEO into strategy from the start. Their work covers the full picture: content positioning, listicle placements, brand mentions in trusted publications, and technical implementation, all mapped to what actually influences AI recommendations in a given category. They monitor AI visibility for clients using dedicated tooling, so the work is measured against real AI appearances, not just keyword rankings.

What they do that affects AI answers

• Develop combined SEO + LLM content optimization strategy and positioning
• Create BOFU comparison, alternatives, and category-defining content (both blog articles and landing pages)
• Place notable client brands in relevant industry listicles via their Listicle Manager tool (the type of content answer engines pull from heavily)
• Produce deep research content that gets cited by LLMs
• Monitor AI visibility across ChatGPT, Perplexity, and Google AI Overviews
• Build high-quality links and secure brand mentions in publications AI systems reference

Strengths

• Deep, specialized B2B SaaS focus with 6+ years in the space
• Content, authority-building, and technical SEO treated as one strategy rather than separate services
• Actively monitors and tracks AI answer appearances for clients
• Proven case studies showing real AI visibility improvements

Limitations

• Boutique team with selective client capacity, so not a fit for every budget stage
• Focused on SEO and content strategy rather than full-funnel paid or performance marketing

Best for

B2B SaaS companies that want to grow both traditional organic traffic and visibility inside AI-generated answers at the same time.

Not ideal for

Companies looking for a full-service marketing agency covering paid media, branding, and outbound.

Key services

• AI & SaaS LLM search optimization
• SaaS SEO optimization (content + technical)
• Content creation strategy and production
• Digital PR and link building
• Listicle placements
• Content refresh and ongoing optimization

Notable client results

• Legal Nodes: 47.5% increase in booked meetings and top 5 AI answer placements within three months
• Oneflow: featured in AI Overviews for 7,000+ keywords
• Expandi: significant growth in organic clicks and conversions

2. Darkroom

Overview

Darkroom is a technology-driven growth agency focused on consumer brands across DTC, Amazon, Walmart, TikTok Shop, and other commerce channels. Unlike most agencies on this list that primarily focus on B2B SaaS, Darkroom specializes in helping consumer brands scale across paid media, marketplaces, retention, social commerce, creative, and AI-driven marketing.

The major differentiator is Shadow, Darkroom’s proprietary AI commerce layer that supports execution across media buying, creative workflows, marketplaces, and growth operations. The agency has managed more than $100M in ad spend and contributed to over $5B in attributable client revenue.

Why they’re relevant for AEO

Darkroom approaches AEO and GEO from a commerce and performance marketing perspective. Their work focuses on increasing brand visibility across AI-driven discovery environments while strengthening authority across marketplaces, retail media, social commerce, and search.

Their GEO offering combines AI visibility optimization with paid acquisition, content, retention, and marketplace strategies, making them especially relevant for brands that depend on multi-channel product discovery.

What they do that affects AI search answers

• Run GEO campaigns designed to improve visibility across AI-driven search and recommendation systems
• Strengthen brand authority signals through paid media, marketplaces, and social commerce
• Optimize product and brand presence across Amazon, Walmart, TikTok Shop, and retail platforms
• Combine performance creative, retention, and CRO with AI marketing strategies
• Use AI-assisted workflows through their Shadow platform to support execution and scaling

Strengths

• Strong expertise in consumer brands and multi-channel commerce
• Integrated growth model combining AI marketing, marketplaces, paid media, and retention
• Experience managing large-scale media budgets and enterprise growth campaigns
• Proprietary AI commerce infrastructure supporting execution workflows

Limitations

• Primarily focused on consumer brands rather than B2B SaaS
• Pricing and engagement structure may not suit smaller companies or startups
• Full-service growth approach may be broader than needed for brands seeking SEO-only support

Best for

Mid-market and enterprise consumer brands that want integrated growth across AI visibility, paid acquisition, marketplaces, and commerce channels.

Not ideal for

Early-stage startups or companies looking for lightweight SEO or standalone AEO consulting.

Key services

• Growth strategy
• Paid media and retail media
• TikTok Shop management
• Marketplace management
• AI marketing and GEO
• Performance creative and brand studio
• Retention marketing
• CRO and web support

Results

• J3x revenue growth for Olipop through a national media campaign across Connected TV, YouTube pre-roll, and paid social
• ~100% business growth in one quarter for Sculpd through Meta, Google, and TikTok acquisition campaigns
• 43% YoY Q4 revenue growth for Nécessaire during the iOS 14 attribution transition

3. Skale

Overview

Skale is a B2B SaaS SEO and AI search optimization agency focused on helping software companies grow pipeline and revenue through organic visibility. Unlike generalist agencies, Skale works exclusively with SaaS businesses, combining technical SEO, content strategy, authority building, and AI search optimization into a unified growth model.

As AI-powered search becomes a larger part of the B2B buying journey, Skale has expanded its approach beyond traditional rankings to help brands increase visibility across ChatGPT, Perplexity, Google AI Overviews, Claude, and other answer engines. The agency focuses on strengthening the signals AI systems rely on when selecting sources, recommendations, and citations.

Why they’re relevant for AEO

Skale approaches answer engine optimization through the lens of category authority. Rather than treating AI visibility as a separate channel, they focus on building the underlying factors that influence whether a company becomes a trusted source in both traditional search and AI-generated answers.

Their methodology combines technical SEO, strategic content creation, digital PR, and high-authority link acquisition to improve a brand’s presence across the web. This is especially important because answer engines frequently rely on trusted third-party mentions, comparison content, industry publications, and authoritative websites when generating recommendations.

What they do that affects AI search answers

• Build category authority through strategic content and topical coverage
• Create comparison, alternatives, and solution-focused content aligned with buyer intent
• Execute digital PR campaigns that generate mentions in authoritative industry publications
• Acquire high-quality backlinks and entity signals that strengthen brand credibility
• Improve technical SEO foundations that support AI crawler accessibility and content understanding
• Optimize content structures and information architecture for machine readability
• Monitor evolving AI search trends and adapt content strategies accordingly

Strengths

• Exclusive focus on B2B SaaS companies
• Strong emphasis on revenue generation rather than traffic alone
• Combines content, technical SEO, digital PR, and authority building under one strategy
• Deep experience operating in highly competitive SaaS categories
• Well-positioned to support both traditional SEO and emerging AI search visibility goals

Limitations

• Primarily focused on SaaS and technology companies rather than broader industries
• Best suited for companies investing in long-term organic growth rather than short-term demand generation

Best for

B2B SaaS companies that want to improve both organic search performance and visibility within AI-generated recommendations while maintaining a clear connection to pipeline and revenue growth.

Not ideal for

Consumer brands, ecommerce businesses, or organizations looking for a broad full-service marketing partner outside of organic growth.

Key services

• AI search optimization and GEO strategy
• SaaS SEO (technical and content)
• Digital PR and authority building
• Link building and entity development
• Content strategy and production
• Content refreshes and optimization
• Revenue-focused SEO consulting

Results

• Helped multiple SaaS brands achieve significant increases in organic traffic, demo requests, and pipeline growth through SEO-led acquisition strategies
• Generated placements and brand mentions across high-authority industry publications
• Improved visibility for competitive commercial-intent SaaS keywords through combined content and authority-building campaigns

4. Omniscient Digital

Overview

Omniscient Digital is an organic growth agency founded in 2019 and based in Austin, Texas, focused on B2B SaaS. Their leadership team comes from in-house roles at HubSpot, Shopify, and Workato. They operate as growth partners along with being content producers. They’ve worked with companies like Jasper, Loom, Hotjar, Asana, Adobe, and SAP, and are known for tying organic efforts directly to pipeline, qualified leads, and ARR.

Why they’re relevant for AEO

Omniscient has expanded beyond SEO into Generative Engine Optimization (GEO), aligning their approach with how AI systems evaluate and recommend brands. Their focus on authority, topical depth, and high-quality content is combined with their proprietary OmniscientX research framework. This helps position clients for visibility in both search engines and AI-generated answers.

What they do that affects AI search answers

• Build deep topic clusters and strong semantic coverage to reinforce category authority
• Create research-backed, high-authority content designed to lead a topic
• Offer GEO as a dedicated service
• Align content with comparison-driven queries commonly used in AI recommendations
• Use digital PR and link building to strengthen brand authority signals

Strengths

• Exceptional editorial quality with strong in-house standards
• Leadership team with real in-house SaaS experience at scale
• Clear reporting tied to pipeline and revenue, not just traffic

Limitations

• Full-service engagements start at $10,000/month, making them less accessible for early-stage startups
• Quality-first approach means slower content scaling

Best for

Growth-stage and enterprise B2B SaaS companies that want a long-term organic growth partner with depth in both SEO and generative search.

Not ideal for

Early-stage startups that need fast output or are working with tighter budgets.

Key services

• SEO strategy and GEO
• Content creation and optimization
• Technical SEO
• Link building and digital PR
• Analytics and attribution

Results

• Jasper: 810% growth in organic sessions and 400x increase in product signups
• Order.co: 2117% growth in blog organic sessions and 39x conversions
• Smartling: $3.7M in pipeline generated through organic search
• GatherContent: 867% growth in organic blog sessions and 62% increase in leads

5. Discovered Labs

Overview

Discovered Labs is a pure-play AEO and GEO agency built for B2B SaaS companies focused on visibility in AI-generated answers. It was co-founded by Liam Dunne (who scaled Instantly.ai to $20M ARR) and Ben Moore, an AI/ML engineer with experience at Stripe, Coinbase, and Brex. This blend of growth and technical expertise gives them a strong understanding of how LLMs retrieve and cite content in practice.

Why they’re relevant for AEO

Discovered Labs is purpose-built for AEO. Their proprietary CITABLE framework structures content for AI extraction, verification, and recommendation. They actively test content against AI systems to see what gets cited and run Reddit campaigns to influence category narratives on platforms LLMs rely on.

What they do that affects AI answers

• Apply the CITABLE framework (entity structure, intent, validation, answer grounding, RAG-friendly formatting, freshness, and entity relationships)
• Produce high-volume content (20–60+ articles per month with daily publishing)
• Run Reddit marketing to build citations and category authority
• Monitor AI visibility across ChatGPT, Claude, Perplexity, and Google AI Overviews
• Combine technical SEO with content to ensure AI crawlability
• Use digital PR and backlinks to strengthen third-party validation signals

Strengths

• Highly technical, AEO-specific methodology
• Strong AI/ML expertise at the founder level
• Scalable, AI-assisted content production
• Flexible month-to-month contracts

Limitations

• Heavy focus on content volume over editorial depth
• Newer agency with a shorter track record

Best for

B2B SaaS companies ($2M–$50M ARR) that want to quickly improve AI answer visibility with a technically focused approach.

Not ideal for

Enterprise companies needing complex governance, compliance, or a full-service marketing partner.

Key services

• Answer Engine Optimization (AEO)
• CITABLE framework content production
• Reddit marketing and narrative shaping
• Technical SEO
• AI visibility monitoring and reporting
• Digital PR and backlink building

Results

• One B2B SaaS client went from 550 AI-referred trials to 2,300+ in four weeks (4x growth), with 600% citation uplift across ChatGPT, Claude, and Perplexity
• 340% increase in AI citations within 90 days for a client using the CITABLE methodology
• 3x–4x better traditional SERP performance alongside AEO gains for the same client

Related: LLM Optimization vs SEO: What’s Changing in Search Discovery

6. NoGood

Overview

NoGood is a New York–based growth marketing agency with a strong AEO practice embedded in its full-service offering. They work across SaaS, fintech, healthcare, and consumer brands, including ByteDance, Intuit, Spring Health, and Inflection AI. A key differentiator is Goodie, their proprietary platform for tracking AI visibility across ChatGPT, Gemini, Claude, and Perplexity, which they use directly in client work.

Why they’re relevant for AEO

NoGood treats AEO as part of a broader growth system, not a standalone channel. Their approach combines schema, structured content, entity optimization, and prompt-gap audits to make content machine-readable and more likely to be surfaced by AI. With Goodie, they measure real AI visibility instead of relying on assumptions.

What they do that affects AI answers

• Run AEO audits and build strategies aligned with AI search behavior
• Implement schema and structured content for clear entity signals
• Optimize entities and conduct prompt-gap audits
• Track AI visibility in real time via Goodie
• Apply technical SEO to improve AI crawlability and citation
• Build links and brand authority to strengthen recommendation signals

Strengths

• Proprietary Goodie platform for real-time AEO tracking
• Strong full-funnel approach with AEO integrated into growth strategy
• High client retention (84%)
• Experience across multiple high-growth industries

Limitations

• Less SaaS-specific focus than niche agencies
• Full-service model may include unnecessary services if only SEO/AEO is needed

Best for

Growth-stage SaaS and B2B companies that want AEO integrated into a broader, data-driven growth program.

Not ideal for

Brands looking for a lean, SaaS-only agency focused purely on organic and AI search.

Key services

• Answer Engine Optimization (AEO)
• SEO and technical optimization
• Content marketing
• Paid media and growth campaigns
• AI visibility monitoring (Goodie)
• CRO and lifecycle marketing

Results

• Inflection AI: over 1 million daily active users within 3 months
• Spring Health: 113% increase in provider qualified leads
• SteelSeries: AI search scaled as a revenue channel through dedicated AEO work

7. MADX Digital

Overview

MADX Digital is a London-based SEO agency focused on B2B SaaS, working with funded startups and mid-market scale-ups. They prioritize revenue outcomes like demos, signups, and ARR over vanity traffic metrics. As AI search has evolved, they’ve expanded into Generative Engine Optimization (GEO) and were recognized in 2026 with a Global Recognition Award for innovation in AI-driven search.

Why they’re relevant for AEO

MADX has introduced GEO as a core service alongside SEO, content, and link building. They use proprietary tracking to monitor visibility across both traditional and AI search. They focus heavily on authority-building through PR, backlinks, and brand mentions, which directly influences AI recommendations.

What they do that affects AI answers

• Offer GEO as a dedicated service
• Build brand mentions and backlinks across authoritative sites and forums
• Create SaaS-focused content (including comparisons and alternatives) aligned with buyer intent
• Implement technical SEO and schema for better AI crawlability
• Track AI visibility across ChatGPT, Perplexity, and Google AI Overviews
• Run digital PR to secure presence in AI-referenced sources

Strengths

• Deep specialization in B2B SaaS growth
• Revenue-focused reporting tied to pipeline metrics
• Strong execution in link building and digital PR
• Clear processes and transparent project management

Limitations

• Smaller team may limit scalability
• GEO is newer, with less historical track record than their SEO work

Best for

Funded B2B SaaS startups and scale-ups looking to combine organic growth with AI visibility, backed by clear pipeline reporting.

Not ideal for

Enterprise companies needing large-scale content operations or deep paid media capabilities.

Key services

• SaaS SEO strategy
• Generative Engine Optimization (GEO)
• Content writing and strategy
• Digital PR
• Link building
• Technical SEO

Results

• Gleemo: 2,537.7% SEO growth and 140+ new rankings
• Thunes: #1 rankings for cross-border payments terms
• UPSTIX: 0 to 8,000 organic visitors in under six months
• Maekersuite: 10,000+ end users acquired through SEO
• Good Annotation: 365,000 page views and 5,000 email signups in the first twelve weeks

8. Team 4

Overview

Team 4 is a London-based B2B SaaS marketing agency focused on early- and growth-stage companies in niche tech markets. They operate as a small, senior-only team with no account managers, so strategy and execution stay with the same people. Founded by former in-house SaaS marketers, they prioritize pipeline impact over traffic and understand board-level reporting expectations.

Why they’re relevant for AEO

Team 4 has integrated LLM optimization into its core offering alongside SEO, with a structured, measurable approach. Their focus on building Webflow sites with AI-readable architecture from day one, combined with practical AEO content like FAQ optimization, positions clients well for AI visibility.

What they do that affects AI answers

• Develop LLM optimization strategies for B2B SaaS
• Build Webflow sites with schema, strong architecture, and AI-readable structure from the start
• Create buyer-intent content aligned with comparison-driven AI queries
• Structure content for how AI systems interpret and summarize categories
• Use AI-assisted workflows for scalable content production and optimization
• Run integrated SEO campaigns that include AI visibility (ChatGPT, Perplexity)

Strengths

• Senior-only team with direct founder involvement
• Strong understanding of early-stage SaaS constraints (budget, speed, pipeline)
• Technical SEO and content handled as one integrated discipline
• Proven results with SaaS clients like AirDNA

Limitations

• Limited capacity due to small team
• Less suited for enterprise-scale programs or complex governance needs

Best for

Early- and growth-stage B2B SaaS companies in niche markets looking for a hands-on, strategic partner focused on pipeline impact.

Not ideal for

Enterprise SaaS companies needing large-scale production or a full multi-channel agency.

Key services

• LLM optimization and AEO strategy
• SaaS SEO (integrated with web development)
• Webflow website design and build
• Content marketing and strategy
• PPC and paid media
• AI-powered inbound marketing workflows

Results

• Forecast AI: inbound engine website built using search data
• AirDNA: praised by client for being organized, strategic, and delivering tangible SEO results
• Multiple SaaS clients: improved keyword rankings, inbound lead generation, and organic traffic growth tied to pipeline metrics

Comparison table: AEO agencies at a glance

Read also: Does Reddit influence LLMs responses? (The 2026 Research)

Why SaaS companies specifically need AEO agencies

Right now, AI-referred traffic still makes up a tiny slice of overall website sessions, which is well under 1% in most datasets. Organic search still drives 30% or more of traffic for most companies, and around 95% of Americans still use search engines regularly. That number has barely moved even as AI tool usage has exploded.

If someone’s telling you to cut your SEO budget and go all-in on AI visibility, I’d push back on that hard.

At the same time, something interesting is happening underneath those numbers.

According to the Previsible State of AI Discovery Report, traffic from AI tools grew 527% year over year between January and May of 2025. The total volume is still small, but that growth rate is hard to ignore.

What makes this especially interesting for SaaS is the conversion data.

In that same report, LLM traffic converted at 6.69%, while organic search converted at 6.71%. In other words, visitors coming from AI tools converted almost exactly the same as visitors coming from Google.

No other industry category came close to that parity.

This combination of small total volume, massive growth rate, and conversion parity with organic search is why this is worth paying attention to now rather than waiting until the channel is crowded.

Rand Fishkin and Patrick O’Donnell ran a large experiment where they tested nearly 3,000 prompts across tools like ChatGPT, Claude (AI assistant), and Google Gemini.

They wanted to see how consistent AI recommendations actually are. The headline result was surprising. The chance of getting the exact same list of brands twice was less than 1 in 100.

Image Source: Search Engine Land

But in tighter B2B SaaS categories, the same brands kept appearing even when the question was phrased completely differently. The AI wasn’t pulling random results, it had a default shortlist. And once a company becomes part of that default set in a category, it tends to stay there. 

That compounding effect is exactly what early AEO work is trying to capture. Iit’s one of the strongest arguments for getting into this now rather than once the category is already settled.

You might be interested in LLM SEO: Guide to ranking in AI search results

What makes an Answer Engine Optimization agency worth hiring

Almost every SEO agency now claims they “do AEO.” The real signal usually appears during a conversation, not on a homepage. Here’s what I look for.

1. They focus on evaluation-stage content

AI tools often pull from comparison pages, “best of” lists, and alternatives content. Strong agencies prioritize getting you featured in these and building similar assets on your site. They don’t just focus on top-of-funnel blogs.

2. They track prompts mainly

AEO agencies test real buyer prompts in tools like ChatGPT or Perplexity, then track how often your brand appears, how it’s described, and how that changes over time.

3. They treat brand mentions as key signals

Beyond backlinks, AI systems look at how your brand is discussed across the web. Strong agencies actively build mentions in review sites, publications, comparison pages, and community platforms.

4. They connect content, authority, and technical SEO

Consistent AI visibility comes from three things working together: high-quality, well-structured content; strong brand authority across credible sources; and a technical setup that makes content easy for AI to access and interpret.

Keep this framework in mind as you evaluate any agency, it’s a useful lens for every conversation you’ll have.

How to choose the right AEO agency for your SaaS business

AEO messaging is everywhere now, so focus on how agencies answer, not what they claim.

Questions to ask on a sales call:

“How do you help brands appear in AI answers?”

Look for specific tactics: comparison content, prompt tracking, placements in roundups, and building presence in sources AI tools reference. Vague answers are a red flag.

“How do you measure AEO performance?”

They should go beyond keyword rankings and track real prompts across tools like ChatGPT, Perplexity, or Gemini while measuring visibility, positioning, and changes over time.

“Can you show real examples?”

Ask for proof like screenshots, live prompts, or case studies. If they can’t show actual results, they’re likely still early in their AEO maturity.

Closing thoughts

AI answers are now a part of how SaaS buyers research tools. For many of them, it’s becoming the first step; a quick prompt to get a starting point before they open anything else. 

Someone opens ChatGPT, asks for recommendations, sees three or four tools mentioned, and begins their shortlist from there.

AEO agencies help SaaS companies become part of that moment.

The teams doing this well are not abandoning SEO. They are evolving how the work is done so that strong content, credible authority signals, and clear site structure support both traditional search and AI-generated answers at the same time..

If you are evaluating agencies right now, start with one simple test. Ask them to show you real examples of their clients appearing in AI answers. That question alone filters out a lot of noise. If an agency passes it, the conversation is worth having.

If you want to explore how Quoleady approaches AEO for B2B SaaS, book a call and we’ll walk you through it.

FAQ

1. What is Answer Engine Optimization (AEO)?

AEO (Answer Engine Optimization) is a strategy to make your brand appear in AI-generated answers from tools like ChatGPT, Perplexity, or Google Gemini. Instead of competing for clicks and impressions on search engines, AEO focuses on being one of the few products mentioned when users ask high-intent questions related to your product.

2. How is AEO different from SEO?

SEO drives clicks to your website through search engine results. AEO directly recommends your product in AI-generated responses. While SEO earns visibility on search pages, AEO earns visibility inside answers.

3. What’s the difference between SEO, AEO, and GEO?

SEO, AEO, and GEO are not separate strategies but they work together. SEO builds strong foundations through content and authority. AEO focuses on getting your brand mentioned in AI-generated answers. GEO (Generative Engine Optimization) goes deeper by optimizing how AI models interpret, group, and recommend brands. Together, they increase your chances of being surfaced in AI-driven results.

4. How much does AEO typically cost?

It varies a lot depending on the agency and scope. Boutique SaaS-focused agencies tend to start somewhere between $3,000 and $8,000 per month for a combined SEO and AEO program. More established full-service agencies can start at $10,000 or higher.

5. How competitive is my category in AI answers?

Run a few prompts yourself before you talk to any agency. Open ChatGPT, Perplexity, and Google’s AI Overviews and ask the questions your buyers would ask. See who shows up, how consistently, and how your product is described if it appears at all. That gives you a baseline and makes every agency conversation more grounded.

6. What’s a realistic timeline for seeing results?

If you already have decent SEO foundations, some categories start showing movement within two to three months. More competitive categories take longer, and there’s no reliable shortcut. Any agency giving you a specific guarantee on timing is oversimplifying how these systems work.

7. What content types actually influence AI recommendations the most?

Comparison pages, alternatives content, and category roundups tend to carry the most weight, both on your own site and as third-party placements. Deep, well-structured articles that directly answer the questions buyers ask also perform well. Short blog posts optimized for top-of-funnel awareness tend to have less influence on what AI tools actually recommend.

8. Do I need a separate agency for AEO or can my current SEO agency handle it?

Ask your current agency the same question you’d ask any new one. Can they show you a client appearing in AI answers? If they can, they’ve adapted. If they can’t, it’s worth having an honest conversation about whether they’re actively building that capability.

The post 8 Best AEO Agencies in 2026: Who’s Actually Optimizing for AI Answers appeared first on Quoleady.

What has shifted is the level of seriousness around it. Twelve months ago, most SaaS marketing teams knew AI search was something to keep an eye on. Now it’s in the budget conversation. It’s in the agency brief. It’s the thing a VP of Marketing brings up on a strategy call because their CEO just asked about it.

As a result, a flood of agencies are claiming to specialize in AI SEO.

Some of them have genuinely adapted how they work by building AI visibility into their strategies, tracking prompt appearances, placing clients in the sources AI tools actually reference. Others updated their website copy and called it a pivot.

Over the past few weeks, I spent time looking at who’s actually doing meaningful work in this space. I looked at methodologies, client results, and whether agencies could demonstrate real AI visibility for the brands they work with.

This list is what came out of that process. 

How I evaluated these AI Search Optimization agencies for ChatGPT

I want to be upfront about how I put this list together, because “AI SEO agency” has become a label a lot of people are slapping on existing services right now.

Here’s what I actually looked for:

Experience with B2B SaaS:

• Do they understand SaaS buying journeys, long sales cycles, and the difference between driving traffic and influencing a shortlist?
• Have they worked with software companies in competitive categories, not just content-light niches?

Genuine understanding of AI search behavior:

• Can they explain how LLMs source and surface information in a way that informs their actual strategy?
• Are they tracking prompt-based visibility, or repackaging keyword rankings?

Approach to content and authority:

• Is their content strategy built around entity authority and AI citation, or is it still fundamentally a traffic play?
• Do they have a clear point of view on brand presence building across third-party sources?

Proof of visibility in AI tools:

• Do they show up themselves when you ask ChatGPT or Perplexity about AI SEO agencies?
• Can they demonstrate that their clients appear in relevant AI answers?

Strategic thinking beyond “publish more content”:

• Are they thinking about information architecture, entity consistency, and external brand signals?
• Do they have a measurement framework that goes beyond organic traffic?

Clear, honest positioning:

• Are they specific about what they do and who they serve, or is it generic SEO rebranded with “AI” added to the headline?

The agencies on this list passed most of these criteria. None of them are perfect, and I’ll note where I see limitations, but all of them are worth a serious look if AI search visibility is a priority for your team.

Top 8 AI Search Optimization Agencies for ChatGPT 

1. Quoleady

Overview

Quoleady is a B2B SaaS SEO and AI visibility agency that helps software companies grow through organic search, digital PR, and expert-driven content marketing. The company works with both startups and enterprise software brands. Quoleady helps clients improve visibility in Google and AI search, create research-backed content, build authority in their category, and drive qualified pipeline and revenue growth. Today, they operate with a distributed team of 30+ specialists across 10+ countries and have worked with more than 200 SaaS brands, including PandaDoc, Monday.com, and Expandi.

Why they’re relevant for ChatGPT SEO services

Quoleady’s approach naturally aligns with AI search optimization because it focuses heavily on entity authority, category depth, and brand credibility across the web. Their work combines traditional SEO with LLM optimization (LLMO), helping SaaS brands show up not just on Google, but inside AI-generated answers across platforms like ChatGPT, Perplexity, Gemini, and Google AI Overviews. 

Because the agency specializes entirely in B2B SaaS, the strategies are tailored to how SaaS buyers research and compare tools, which is exactly the type of behavior AI search now shapes.

What they do that affects AI answers:

• Content strategy built around topic authority and entity depth, not only keyword clusters
• LLM Optimization (LLMO) to improve brand visibility inside AI-generated responses
• Digital PR and link building to strengthen third-party brand mentions
• Strategic listicle inclusions and content partnerships to expand citation footprint
• Technical SEO including site architecture, internal linking, schema, llms.txt, and indexation
• Analytics setup to track LLM-driven traffic and conversions

Strengths

Deep SaaS specialization is the biggest advantage here. Because Quoleady works exclusively with B2B software companies, the content and strategy tend to reflect real category expertise, which is increasingly important for AI citations. After working with 200+ SaaS companies, the team also has strong pattern recognition around what actually drives sign-ups and product adoption from organic channels.

Limitations

Their specialization is also their biggest constraint. Quoleady isn’t designed for ecommerce brands, local SEO campaigns, or consumer products. If you’re outside the B2B SaaS ecosystem, you’ll probably want an agency with a broader focus.

Best for

Growth-stage and established B2B SaaS companies that want a partner who understands both traditional SEO fundamentals and emerging AI visibility.

Key services:

• SEO content strategy and creation
• LLM Optimization (LLMO)
• Digital PR and link building
• Content partnerships and listicle inclusions
• Technical SEO and site audits
• Content refresh and optimization

Results / case studies

Helped LegalNodes reach #1 organic position (just beneath the AI Overview) within 24 hours of going live with one article. Four other articles secured top 10 organic rankings. You can read the full LLMO case study here.

2. Embarque

Overview

Embarque is a performance-focused SEO and AI search agency led by Jules Canlas. They help scaling companies (SaaS, HealthTech, FinTech, EdTech, LegalTech, marketplaces) turn organic search into a measurable revenue channel. Clients include VEED, Riverside, MentorCruise, SignWell, and Klap.

Why they’re relevant for AI SEO

Early adopters of AI search optimization, Embarque focuses on making content citation-ready for platforms like ChatGPT, Perplexity, and Gemini. They test hundreds of prompts per client to track where brands appear, and where they don’t.

What they do that affects AI answers

• LLM SEO with dashboards tracking AI citations and brand mentions
• Content structured for AI parsing, plus semantic markup and entity mapping
• Prompt-based visibility testing across buyer queries
• Manual link building to boost authority
• Technical SEO (schema, metadata, crawlability)

Strengths

Strong measurement infrastructure that ties AI visibility directly to revenue. Transparent about timelines and realistic expectations.

Limitations

Broad industry focus may limit deep specialization in SaaS compared to niche agencies.

Best for

Tech and SaaS companies that want a data-driven AI search partner with strong visibility tracking.

Not ideal for

Teams looking specifically for deep B2B SaaS specialization or agencies focused on paid demand generation.

Key services

• LLM SEO and AI citation optimization
• Technical SEO and on-page optimization
• Content strategy and creation
• Link building and digital PR
• Custom AI visibility dashboards
• Prompt testing and brand monitoring

Results / case studies

• Generated 25,000 monthly visits from LLM traffic converting at 17% for a video downloader platform
• Helped MentorCruise increase revenue 1,600% year over year
• Clients like SignWell and Klap now receive thousands of monthly visits from AI search platforms

3. SayNine

Overview

SayNine is a link building agency that has expanded into AI search optimization, helping brands move from “ranking” to getting recommended by AI-driven platforms. They work with SaaS and tech companies through a mix of link building, listicle placements, brand mentions, and content services. Clients include EasyDMARC, SuperAnnotate, GroWrk, Airfocus, Deliverect, Elementor, 1up.ai, and more.

Why they’re relevant for AEO

SayNine’s core work, listicle placements, brand mentions, and link building, is exactly what shapes AI answers. Tools like ChatGPT and Perplexity lean on third-party roundups and comparison content when deciding which brands to recommend, and SayNine drops clients directly into those sources.

What they do that affects AI search engines

• Strategic listicle writing and publishing on high-authority websites
• Brand mention building across trusted sources to strengthen third-party signals
• SaaS-focused link building to grow DR and citation footprint
• SEO audits and technical fixes that keep sites clean and easy for AI models to parse
• SEO content writing and on-page optimization built around topic depth and entity clarity

Strengths

Consistently praised for exceptional link building and AI SEO services, on-time delivery, transparent communication, and real lifts in LLM visibility, rankings, and traffic, all at affordable pricing.

Limitations

SayNine works on a monthly retainer model, so it’s not the right fit for teams that just want a one-off audit or a short consulting project.

Best for

Brands that want to be recommended (not just ranked) by AI tools like ChatGPT, Gemini, and Perplexity, and reach the pre-qualified, high-intent users who rely on AI to shortlist solutions

Not ideal for

Not ideal for e-commerce, D2C, and local businesses.

Key services

• AI SEO services 
• SaaS link building
• Niche guest posts
• Full SEO
• SEO audits
• SEO content writing
• Web development

Results / case studies

• Increased GroWrk’s AI citations by 60% in just one month through a targeted listicle campaign, while also pushing DR from 57 to 60, and organic traffic from 5.6K to 7.2K.
  
• Improved EasyDMARC’s ranking from 77 to 1 through a strategic link building campaign, increased domain rating from 69 to 77, and organic traffic from 14K to 80K.

4. First Page Sage

Overview

First Page Sage is a San Francisco–based SEO and Answer Engine Optimization (AEO) agency founded in 2009. They specialize in B2B lead generation via thought leadership and long-term SEO. Clients include Salesforce, Okta, Microsoft, Cadence Design Systems, Verisign, and SoFi.

Why they’re relevant for AEO

They build category authority through original thought leadership, aligning with how LLMs rank credible sources. Their Generative Engine Optimization (GEO) framework creates content that shapes AI answers while influencing decision-makers.

What they do that affects AI search engines

• Lead-generation that is focused on strategic planning before content creation
• Thought leadership content with subject matter experts
• GEO strategies for ChatGPT, Perplexity, Gemini, and Claude visibility
• Keyword strategies mapped to buyer journeys
• Technical SEO and site architecture improvements
• AI-friendly content structures for generated search results

Strengths

15+ years of B2B SEO experience; thought leadership approach naturally aligns with AI credibility. Known for detailed ROI reporting.

Limitations

Premium pricing ($8K–$20K+/month) and intensive onboarding requiring substantial client SME input.

Best for

Established B2B SaaS or enterprise companies with clear positioning and internal expertise to contribute to thought leadership.

Not ideal for

Early-stage startups or teams looking for fast execution without heavy strategic collaboration.

Key services

• SEO strategy and thought leadership content
• Generative engine optimization (GEO)
• SEM and paid search
• Web design and conversion optimization
• Lead generation funnel development

Results / case studies

• Achieved a 934% increase in keyword rankings for Cadence Design Systems
• Reports an average 702% ROI over three years across B2B SaaS campaigns

5. e intelligence

Overview

e intelligence is a UK-based AI-driven digital marketing agency helping businesses across industries dominate the next generation of search. With a full-spectrum approach that spans Generative Engine Optimization (GEO), SEO, PPC, content marketing, outreach, and web development, e intelligence works with brands to drive measurable revenue through both traditional and AI-powered channels. The agency serves clients across sectors including FinTech, Real Estate, Health & Wellness, Law, eCommerce, and more that makes them one of the most versatile GEO agencies in the UK market.

Why they’re relevant for LLM search tools

e intelligence has built its Generative Engine Optimization services around the idea that search extends far beyond Google. As AI platforms like ChatGPT, Google SGE, Bing Copilot, and Perplexity become key research tools, they help clients increase visibility, citations, and recommendations across these channels while integrating GEO into a broader digital marketing strategy that strengthens overall authority and growth.

What they do that affects AI generated answers

• GEO strategies for visibility across ChatGPT, Google SGE, Perplexity, and Bing Copilot
• AEO and ChatGPT marketing for AI-driven discovery
• Content marketing, outreach, and third-party citation building
• Digital PR and link building to strengthen authority signals
• Technical SEO, schema markup, and structured data implementation
• Reddit marketing to increase brand presence in AI-referenced communities
• White-label AI SEO services for agencies

Strengths

e intelligence stands out by combining dedicated GEO expertise with a full-service digital marketing offering, allowing brands to build AI visibility without relying on multiple agencies. Their experience across industries, including highly regulated sectors such as FinTech, Law, and Pharma, helps them develop credible content strategies that strengthen authority and AI-driven discoverability.

Limitations

Their full-service approach may be more comprehensive than necessary for organizations looking for a narrowly focused AI visibility project.

Best for

Businesses of all sizes – from SMEs to enterprise brands – that want to build AI search visibility as part of a full-funnel digital marketing strategy, particularly in competitive sectors like FinTech, eCommerce, Real Estate, and Professional Services.

Not ideal for

Not ideal for businesses that only need limited GEO support rather than an integrated digital marketing strategy.

Key services

• Generative Engine Optimization (GEO)
• Answer Engine Optimization (AEO)
• ChatGPT Marketing & Reddit Marketing
• SEO, PPC, and Content Marketing
• Outreach and Digital PR
• Link Building
• White Label AI SEO
• Technical SEO and Schema Optimization

Results / case studies

• E intelligence has a proven track record of driving revenue through AI-integrated digital marketing strategies. Their client work spans significant organic growth, improved AI citation footprints, and revenue-focused campaigns across industries. Detailed case studies are available directly through the agency upon consultation.

6. iPullRank

Overview

iPullRank is a New York–based SEO and content strategy agency founded by Mike King. They work with enterprise brands across finance, technology, media, and ecommerce, including SAP, American Express, HSBC, and Zillow.

Why they’re relevant for LLM search tools

Using their “Relevance Engineering” framework, iPullRank applies technical SEO, knowledge graphs, and machine learning principles to improve brand visibility in both traditional search and AI-driven discovery.

What they do that affects AI generated answers

• Relevance Engineering linking AI retrieval mechanics to SEO strategy
• Knowledge graph and entity optimization for AI-recognizable brand associations
• Conversational search optimization for RAG-based AI engines
• Structured data and schema for AI comprehension
• Content strategies targeting micro-intents and Q&A patterns
• AI-powered dashboards tracking conversational search trends
• Advanced technical SEO including JavaScript rendering and site infrastructure optimization

Strengths

Unmatched technical depth for large, complex sites; infrastructure-level SEO expertise.

Limitations

Enterprise-focused pricing and processes that are designed for large organizations with complex digital environments.

Best for

Enterprise SaaS and technology companies with large sites and significant technical SEO needs.

Not ideal for

Early-stage startups or smaller SaaS companies looking primarily for content-led growth.

Key services

• Relevance Engineering and AI search strategy
• Enterprise technical SEO
• Content strategy and AI-powered content generation
• Knowledge graph and entity optimization
• Digital PR and authority building
• AI-powered SEO analytics

Results / case studies

• Delivered $4B+ in organic search results across the client portfolio
• One enterprise client reported the agency’s deliverable-based model was 800% more efficient than a previous engagement

7. Avenue Z

Overview

Avenue Z is a New York-based AI optimization agency that blends digital PR, SEO, and content strategy to help brands build visibility across both traditional search engines and generative AI platforms. They work across industries including FinTech, B2B SaaS, emerging tech, HealthTech, and venture-backed startups. Clients include companies like Acorns, Better, Dave, and e2open.

Why they’re relevant for AEO

Avenue Z approaches AI visibility through a PR-first lens. Their strategy focuses on getting brands mentioned in authoritative media sources, on which LLMs rely on when building training data and retrieving citations.

What they do that affects AI answers

• AI Optimization (AIO) combining content, PR, and technical SEO
• Media placements designed to increase third-party AI citations
• Brand monitoring across ChatGPT, Perplexity, Gemini, and Google AI Overviews
• Structured data and schema for entity recognition
• AI-native content optimized for generative search platforms
• Competitive AI visibility tracking

Strengths

Their strongest advantage is the integration of PR and SEO. Since AI systems often reference authoritative editorial sources, building media coverage can significantly improve citation likelihood.

Limitations

Because Avenue Z works across multiple verticals, including FinTech, HealthTech, DTC, and SaaS, their B2B SaaS specialization may be less deep than agencies focused exclusively on that category.

Best for

Brands that want to build authority and AI visibility through media coverage and PR.

Not ideal for

Companies looking primarily for high-volume content production or deep SaaS content expertise.

Key services

• AI Optimization (AIO) and GEO
• Digital PR and media placement
• AI-native content strategy
• Technical AI SEO and schema optimization
• Brand monitoring across AI platforms
• Performance media services

Results / case studies

• Built AI-ready PR and SEO content strategies for Acorns
• Positioned Better for high visibility in homeownership-related AI search results
• Helped e2open build optimized enterprise content for AI and traditional search

8. Toopixels

Overview

Toopixels is a digital marketing agency that has recently expanded into AI search optimization. Their service is designed to help brands get recognized, cited, and surfaced across AI platforms like ChatGPT, Gemini, and Perplexity. The agency focuses on companies that already produce strong content but struggle to get cited by AI tools.

Why they’re relevant for AEO

Toopixels takes a practical, step-by-step approach to AI search optimization. They start with a full audit of a brand’s visibility across both traditional search and AI answers, then work through improvements to content structure, schema, and authority signals that influence LLM recommendations.

What they do that affects AI answers

• AI visibility audits across Google, Bing, ChatGPT, Gemini, and Perplexity
• Topic, author, and brand entity alignment
• Content structure optimization for machine readability
• Schema deployment and factual clarity improvements
• Internal linking and topical citation building
• Ongoing monitoring of AI citations and search trends
• Traditional SEO improvements including technical optimization

Strengths

They’re particularly good at breaking down AI search concepts into practical steps for marketing teams. Their structured approach makes them a good entry point for companies just starting to explore AI search optimization.

Limitations

Toopixels is a smaller and more generalist agency compared with others on this list. Their AI optimization offering is relatively new, and publicly available case studies specific to AI visibility are still limited.

Best for

Small to mid-sized businesses looking for a practical introduction to AI search optimization without a large agency engagement.

Not ideal for

Enterprise SaaS companies with complex SEO environments or teams looking for deep SaaS specialization.

Key services

• AI search optimization audits
• Content structure and schema optimization
• Brand entity and citation building
• AI citation monitoring
• Technical SEO
• Paid advertising and digital marketing

Results / case studies

• Reports 90% client retention, with most clients staying longer than a year
• Detailed public case studies specifically tied to AI visibility are currently limited

What is AI Search Optimization for ChatGPT?

A lot of people assume AI search works like Google. They think there’s some kind of ranking system and you’re trying to move your page up the results.

That’s not how it works.

How ChatGPT and AI answers source information

Unlike Google, ChatGPT doesn’t constantly crawl the web and maintain a live index of pages (unless you’re using a browsing-enabled version). The base model was trained on a massive snapshot of the internet like blog posts, documentation, review sites, forums, research papers, and a lot more.

When someone asks a question, the model generates an answer based on patterns it learned during that training process.

Newer versions also use retrieval-augmented generation (RAG). In simple terms, that means the model can pull in relevant external sources at the moment a question is asked, almost like doing a quick reference check before responding. You can learn more about howretrieval-augmented generation (RAG) impacts AI recommendations and what SaaS marketers should do to stay ahead.

ChatGPT synthesizes answers. Brands, tools, and sources that show up in those answers tend to be the ones the model has encountered repeatedly across credible, well-structured content. Over time, it learns to associate certain companies with certain categories, use cases, or problems.

So if your product consistently appears in discussions about a particular topic across trusted sources, the model is far more likely to mention your brand when someone asks about that topic.

What “showing up on ChatGPT” really means

So “showing up” usually means your brand gets mentioned when someone asks something like:

• What’s a good tool for X?
• Which platforms do SaaS companies use for Y?
• What are the best alternatives to [competitor]?

That kind of visibility generally comes down to three things:

1. How often your brand appears in the content the model has been trained on or retrieved from.
2. How credible those sources are where your brand is mentioned.
3. How clearly your product is associated with specific categories or use cases.

If your brand shows up consistently in discussions about a particular problem space, the model starts to treat you as a natural example when that topic comes up.

You might be interested inA Study of 10,000 LLM Citations: Where AI Pulls Data From (SaaS High-Intent Prompts)

Where traditional SEO still matters (and where it doesn’t)

Technical SEO still matters. Crawlability and indexation determine whether your content gets discovered, while page speed and mobile experience affect performance in search and user engagement.

E-E-A-T also plays a role here. Clear authorship, expertise, and credible content make it easier for AI systems to trust what they’re seeing.

If your site has thin content, very few authoritative mentions, and almost no presence outside your own domain, AI systems don’t have enough signal to treat you as a trusted source.

Where classic SEO starts to fall short is in how content is written. A lot of SEO content is built to rank for a keyword. It’s structured around search queries and optimized for Google’s signals.

But large language models don’t think in keywords.

They look for content that explains things clearly, compares options, and adds context. Content that’s easy to understand and easy to reuse in an answer.

That doesn’t mean you need a completely different type of content. The fundamentals are the same. What changes is the priority.

Instead of writing primarily to rank, you’re writing so your content can be quoted, summarized, and referenced when someone asks a relevant question.

That also means your presence can’t live only on your own site. You need to show up across third-party articles, review platforms, and industry content. That’s what gives AI systems enough confidence to mention you when it matters.

Why B2B SaaS companies care about ChatGPT visibility 

The B2B SaaS buying journey has always been research-heavy. Buyers take their time comparing tools, reading reviews, and asking peers for input. In many cases, this process stretches over several weeks before any conversation with a sales team begins.

What’s changing is where that research starts.

AI-driven search platforms like ChatGPT are becoming a common starting point for research, which means traditional SEO alone is no longer enough for visibility.

AI tools provide quick, synthesized answers, often summarizing multiple sources into a single response. As a result, many brands are left out of these answers because their content does not align with how AI systems evaluate and select information.

According to a study by Bain & Company, a significant portion of consumers now prefer AI chatbots over traditional search engines for certain tasks, particularly when they want fast, summarized insights. This trend is especially pronounced among experienced and time-constrained decision-makers who value efficiency in early-stage research.

In practical terms, AI-generated responses are shaping the earliest phase of the buying journey. This is the stage where potential customers are identifying the type of solution they need, exploring available vendors, and forming initial impressions of credibility.

When a product appears consistently in these AI-generated answers, it often secures a place on the buyer’s shortlist before formal evaluation even begins. On the other hand, brands that are absent from these responses may only enter consideration later, when preferences have already started to form based on earlier AI recommendations.

Another important point is that visibility in AI search does not always correlate with traditional SEO performance. A company may rank well and generate steady traffic, yet still fail to appear in AI responses if it lacks the right signals across the sources these systems rely on.

At the same time, a competitor with less organic traffic but stronger presence in AI-generated answers can gain a meaningful advantage. By consistently appearing in relevant responses, they capture early attention and influence perception at a critical stage.

Over time, this advantage tends to build. Buyers who discover a solution through an AI recommendation often treat it as a trusted starting point. They are less likely to explore a wide range of alternatives, which makes it increasingly difficult for other vendors to break into consideration later in the process.

What AI Search Optimization agencies for ChatGPT actually do

Traditional SEO agencies focus on rankings. AI search optimization agencies focus on getting your brand recommended. This changes how everything is approached.

Instead of chasing the top spot for a keyword, the goal is to make sure AI tools mention your company when users ask relevant questions.

In practice, this starts with content. The emphasis is on clear, structured, and specific information that AI models can easily understand and reference. Content that explains concepts, compares options, and gives direct answers tends to perform better than generic or overly cautious writing.

Brand presence also plays a bigger role. Your company should be mentioned across credible sources. Reviews, comparisons, and industry roundups all help build the signals that AI systems rely on.

Technical SEO still supports everything. A well-structured site, strong internal linking, and proper indexing make it easier for both search engines and AI models to interpret your content.

Finally, strong agencies track how brands appear inside AI answers. They test real prompts, monitor mentions, and adjust strategy based on what shows up. This allows them to improve visibility faster, without waiting months for traditional SEO results.

How AI Search Optimization agencies differ from traditional SEO agencies

Traditional SEO agencies and AI-focused agencies may use similar tools, but their objectives are quite different.

A traditional SEO agency is centered on keywords, rankings, and organic traffic. The goal is to improve positions on Google and drive clicks to a website. This approach has been effective for years and still plays an important role today. However, it does not directly address whether a brand is recommended by AI systems.

AI search optimization agencies focus on visibility within AI-generated answers. Their aim is to get your brand mentioned when your target users ask relevant questions to LLM tools like ChatGPT for recommendations, comparisons, or solutions.

Many traditional SEO agencies find this transition challenging because their measurement frameworks rely heavily on metrics such as impressions, clicks, and rankings. These indicators do not fully capture what happens inside AI-generated responses. To understand performance in this space, agencies need to look at prompt-based testing, analyze how brands are mentioned, and review AI-generated answers directly.

Working with an agency focused on AI search often changes both planning and measurement. Content strategies move away from keyword clusters toward broader topic coverage and entity-level authority. Success is evaluated not only by traffic, but also by how often and how effectively a brand appears in AI responses.

This approach tends to take time, as it involves building a presence that AI systems recognize and trust. When done well, the result is consistent inclusion in the recommendations that shape early buying decisions.

How to choose the right ChatGPT SEO agency

The market for AI search optimization agencies is still very early. There are some genuinely smart teams doing interesting work.

But plenty of agencies have started adding “AI SEO” to their website without really changing how they operate. So if you’re evaluating partners in this space, it helps to know what to look for.

Here are a few things I’d pay attention to.

1. Do they understand AI search and Generative Engine Optimization (GEO)?

A simple way to test this is to ask them how tools like ChatGPT source information and decide what to include in answers. Ask what signals they believe influence whether a brand gets recommended.

If the explanation basically turns into a keyword strategy with a different label, that’s a sign they haven’t adapted their approach yet.

2. Do they think beyond ChatGPT?

ChatGPT gets most of the attention right now, but it’s not the only place where AI-powered answers are happening. LLM tools like Perplexity, Gemini, and Claude aren’t much different. All these tools surface information slightly differently and have different user bases.

An agency that only focuses on one platform is probably leaving visibility gaps open.

3. What is their technical approach?

AI powered content optimization still depends on strong SEO fundamentals. Your site needs clean architecture, proper indexation, and thoughtful internal linking so both search engines and AI powered tools can understand your content. The right agency should provide clear reporting on how your visibility in ChatGPT answers translates into real outcomes.

If an agency dismisses technical SEO as outdated or irrelevant, that’s a red flag.

4. Do they have proof of AI citations?

Can they show examples of their clients appearing in AI answers for relevant prompts? Can they demonstrate that their own brand shows up when you ask tools like ChatGPT about AI SEO or AI search optimization?

Look for agencies that have a proven track record of delivering measurable growth for their clients in AI search visibility. If they can’t show that kind of visibility for themselves, it’s hard to trust they can deliver it for you.

5. Are they integrating AI search with traditional SEO?

Initial improvements in AI search visibility can typically be seen within 2-3 months, with significant changes appearing around 4-6 months. The companies building durable visibility right now aren’t treating SEO as a separate strategy. They’re doing both. The agencies worth working with understand that the old signals and the new signals reinforce each other.

Final thoughts

For a long time, most discoveries happened on a Google results page. Now, more of those early questions are getting answered directly inside AI tools.

Yet the fundamentals haven’t changed. Content quality still matters. Domain authority still matters. Technical health still matters. Brand presence across the web still matters.

All of those things now need to be built with AI consumption in mind.

The companies that figure that out early will slowly build a visibility advantage that compounds over time. The ones that wait will eventually find themselves competing against companies that already dominate the recommendation layer in their category.

At Quoleady, we’ve been building toward the B2B SaaS companies we work with. We’re not the right fit for every company, but if you’re trying to understand how AI search fits into your growth strategy, it’s a conversation we’re always happy to have.

You can book a strategy call today. 

The post Top 8 AI Search Optimization Agencies for ChatGPT appeared first on Quoleady.

• best procurement software for manufacturing companies
• tools to manage supplier relationships for enterprises
• contract lifecycle management software for legal teams
• best sales enablement platforms for B2B teams
• best employee onboarding software for HR teams

We scanned them all with Allmond.app and structured the results. We looked at the Sources – the Citations sections that shows which URLs an LLM used to deliver an answer to a prompt. The findings have been fascinating. Let me just share this screenshot:

One of the main conclusion of the research has been around Listicles and their impressive % in the overall Citation sources structure. I couldn’t believe that 50% of the sources are actually listicles – mostly blog articles with lists of software tools like

“Top X %industry% software in 2026”
“Best X %industry% tools in 2026”
“X alternatives to %competitor software% in 2026”
etc.

But let me explain all our findings step by step.
First, let’s take a look at the:

Share of All SaaS Citations by LLM.

Gemini dominates citation volume with nearly half of all citations in our dataset, followed by Perplexity at 33%. Claude and ChatGPT produce significantly fewer citations per query.

What this means for SaaS:

For SaaS companies, this means visibility in AI answers doesn’t work the same across models. Some models rely heavily on external sources when generating responses, while others answer more often from internal knowledge.

As a result, being cited across the web becomes much more important for models like Gemini and Perplexity, where citations directly shape which brands appear in answers. With models that cite less frequently, brand presence in training data and widely referenced sources becomes more important.

However, it doesn’t mean online content isn’t imporatnt for LLMs that rely less on web search and more on internal knowledge base. For SaaS companies, appearing in widely referenced web sources is still critical. Much of the knowledge LLMs learn about software tools originates from publicly available web content (blog posts, comparison pages, and listicles).

When brands are consistently mentioned in these sources, they are more likely to appear in the datasets used to train future models. As a result, they become more likely to surface in AI-generated answers.

How Much Each LLM Relies on Listicles for SaaS Citations?

Claude relies on listicles for over 70% of its citations — far more than any other model. Even the least listicle-dependent model (Gemini) still pulls 40% from listicles.

What this means for SaaS:

Getting featured on listicle pages (“best X tools”, “top Y software”) is the single most impactful thing you can do for AI visibility of your SaaS. It’s the primary citation source across all LLMs.

Share of SaaS Citations Coming From Blog Pages

30–45% of citations come from blog pages across all models. Gemini and Claude lead, while ChatGPT is lowest at 30.6%.

What this means for SaaS:

Blog content is a major citation source. Publishing in-depth comparison posts and category reviews on your blog (or getting featured on others’ blogs) directly increases your chances of being cited by AI.

Citation Volume by LLM

Gemini produces 13x more citations than ChatGPT per query. This gap is largely explained by how each model handles web search: ChatGPT only produces citations when it triggers a web search — if it answers from training data, the response contains zero citation URLs, even if it mentions your brand. Perplexity, by contrast, always searches and cites on top of using internal knowledge base.

Which LLMs Cite Wikipedia When Answering High-Intent SaaS Prompts

Wikipedia is almost irrelevant for high-intent SaaS queries. Only ChatGPT cites it meaningfully (53 citations), and Claude and Perplexity ignore it entirely.

What this means for SaaS:

For high-intent SaaS queries, having a Wikipedia page won’t move the needle. LLMs prefer specialized, up-to-date content over encyclopedic entries when answering software buying questions. Wikipedia may still matter for brand awareness or informational queries — but not for purchase-intent prompts when choosing a software provider.

Which LLMs Cite YouTube When Answering High-Intent SaaS Prompts

YouTube citations are rare overall. Perplexity is the only model that cites YouTube with any consistency (64 citations). Claude and ChatGPT ignore it completely.

What this means for SaaS:

YouTube content is not a primary driver for AI citations today (again, we are talking about high-intent SaaS prompts). However, Perplexity’s usage suggests video may become more relevant. Don’t prioritize it for AI visibility — but don’t abandon it either.

Nearly Half of AI Citations for SaaS Queries Come from Listicles

Across all queries and all models combined, nearly half of every citation points to a listicle page. This is the single dominant content format in AI-generated answers.

What this means for SaaS:

If your product isn’t appearing on listicle pages, you’re invisible to almost half the AI citation pipeline. Prioritize listicle placements — through outreach, guest posts, or creating your own. Check out my LinkedIn post on how to get featured on top-ranking SaaS listicles, it should help a lot.

Where AI Models Pull Their SaaS Citations From

Not to get our readers confused we’ve broken down the Citations formats:

First, into

1. listicles and

2. non-listicles (other) and

Second, into

1. Blogs/resource pages

2. YouTube

3. Wikipedia,

4. Forums (Reddit, Quora and community platforms.) and

5. Other.

Our research shows that Blog/Resources are the most common sources of LLM citationts while YouTube, Wikipedia, and forums are marginal.

What this means for SaaS:

Are you still wondering whether or not you should invest in your blog? How about this – blog posts account for more than 42% of the LLM sources. Forums and community content are nearly invisible to LLMs despite often containing genuine user insights. So unless for your SaaS niche specifically Reddit is a big thing, I wouldn’t advise to bet on this channel for your AI visibility despite all the “Reddit is on the rise” rumours.

But wait, here comes my favorite – ‘Alternatives’ prompts.

70% of AI Citations for ‘Alternatives’ Queries Come from Listicles

For “alternatives to X” queries, listicle dominance jumps to 70% — the highest of any query type. These queries are inherently list-shaped, and LLMs reflect that.

What this means for SaaS:

If competitors’ users are searching for alternatives, you MUST appear on listicle pages to capture that demand. This is the highest-ROI content format for competitive positioning in AI answers.

Where AI Models Pull Citations From for ‘Alternatives’ Queries

Now let’s look at each LLM and see which sources it relies on.

Claude Sources 71.5% of Citations from Listicles

Claude is the most listicle-dependent model at 71.5%. Nearly three out of four Claude citations come from listicle pages.

What this means for SaaS:

Claude users get overwhelmingly listicle-sourced answers. If your product is on the right listicles, Claude will cite you. If not, you’re essentially invisible to Claude’s audience.

Where Claude Pulls Citations From for SaaS Brands

Claude cites zero YouTube, Wikipedia, or forum sources but relies heavily on blog articles.

55% of Perplexity Citations Come from Listicles

Perplexity cites listicles 55% of the time — second only to Claude. As a search-first product, Perplexity pulls from a wider range of sources but still leans heavily on listicles.

What this means for SaaS:

Perplexity combines search behavior with AI — its users are actively looking for answers. Being on listicles that rank well traditionally also increases your Perplexity visibility.

Where Perplexity Pulls Citations From for SaaS Brands

Perplexity is the only model that cites YouTube with any regularity (64 citations) and has the strongest blog/resource citation rate after Gemini.

40.5% of Gemini Citations Come From Listicles for SaaS Brands

Gemini has the lowest listicle reliance at 40.5% but produces the most citations overall. It draws from a more diverse set of sources than other models.

What this means for SaaS:

Gemini rewards diverse content strategies. While listicles still matter, having strong blog content, documentation, and web presence gives you more pathways into Gemini’s citations.

Where Gemini Pulls Citations From for SaaS Brands

Gemini leads in blog citations (44.9%) and is the only model besides ChatGPT to cite Wikipedia. Its source diversity makes it the most accessible model for multi-format content strategies.

44% of ChatGPT Citations Come From Listicles for SaaS Brands

ChatGPT cites listicles 44% of the time — middle of the pack. However, it produces far fewer citations overall (402 total), making each citation placement more competitive.

What this means for SaaS:

ChatGPT has the smallest citation footprint but the largest user base. Every citation slot is high-value.

Where ChatGPT Pulls Citations From for SaaS Brands

ChatGPT is the only model that meaningfully cites Wikipedia (53 citations, 13.2%). It also cites forums more than other models, suggesting it values diverse, community-sourced information.

Summary

Listicle vs Non-Listicle Citations Across All LLMs

Listicles dominate across every model — Claude relies on them most (71.5%), while Gemini is the least dependent (40.5%). No matter the LLM, getting featured on listicle pages is the single most impactful way to earn citations.

Blog / Resources Citations % Across LLMs

Blog and resource pages are the largest citation source. Gemini leads with 2,347 blog citations (44.9% of its total), while ChatGPT pulls the fewest at just 123. Consistent, high-quality blog content pays off — especially for Gemini and Perplexity visibility.

YouTube Citations Across LLMs

YouTube is almost invisible as a citation source — only 77 citations total (0.7%). Perplexity is the only model that meaningfully surfaces YouTube content. For SaaS companies, video alone won’t drive AI visibility.

Forum Citations (Reddit, Quora & Community Platforms) Across All LLMs

Forum content (Reddit, Quora, etc.) accounts for just 33 citations across all models (0.3%). Despite being rich in authentic user opinions, community platforms are nearly invisible to LLMs when generating SaaS recommendations.

Key Takeaways for SaaS Companies

1. Listicles are the #1 citation source across every LLM.

40–72% of citations come from listicle pages. If you’re not on listicles, you’re not in AI answers. Invest in listicle placements — create them, pitch them, sponsor them.

2. Blog content is the most important channel.

30–45% of citations come from blog pages. Publish detailed comparison posts and category overviews that position your product alongside competitors.

3. Traditional SEO sources (Wikipedia, YouTube, forums) barely matter.

These sources account for less than 2% of citations combined. Don’t rely too much on them for AI visibility.

4. Different models need different strategies.

Claude is almost entirely listicle-driven. Gemini rewards diverse content. Perplexity bridges search and AI. ChatGPT has few but high-value citation slots.

5. “Alternatives” queries are the highest-leverage opportunity.

70% of “alternatives to X” citations come from listicles. If competitors’ users are exploring options, you need to be on those pages.

Looking to get your SaaS into listicles?

Book a call and let’s discuss how to get your product cited by AI models.

The post A Study of 10,000 LLM Citations: Where AI Pulls Data From (SaaS High-Intent Prompts) appeared first on Quoleady.

For years, G2 and Capterra were key platforms for SaaS discovery. Comparing software tools meant visiting review platforms, sorting by ratings, and weighing user feedback.

But the discovery process has changed. Increasingly, buyers now turn to ChatGPT, Claude, Gemini, or Perplexity to find and evaluate SaaS tools directly – skipping the search results and review aggregators.

That shift raises an important question:

Do G2 and Capterra reviews influence ChatGPT results?

We tested it.

The experiment: Review platforms vs. LLMs

We picked dozens of high-intent SaaS “alternatives” keywords like:

• “Trello alternatives”
• “Zapier alternatives”
• “Slack alternatives”

Then we asked ChatGPT for its recommended alternatives for each keyword.

For every tool that showed up in the results, we collected:

• Capterra & G2 review scores and counts
• Domain Rating (Ahrefs)
• Number of referring domains
• Global brand search volume

Do G2 and Capterra reviews influence ChatGPT results?

After collecting the data, we set out to understand how much weight review platforms carry in influencing ChatGPT responses.

After analyzing the data, we found that 100% of the tools mentioned in ChatGPT answers had reviews on Capterra, 99% had reviews on G2, and 78.8% had a Wikipedia page. This shows that these platforms serve as a basic inclusion signal – if your tool isn’t listed on them, you’re likely to be excluded altogether.

But visibility alone doesn’t determine placement. Review platforms help LLMs recognize a tool’s legitimacy by providing structured and frequently updated information.

However, our research shows that even with strong review presence, some tools ranked lower than those with far fewer reviews, weaker ratings, or less brand visibility.

Example:

• OnlyOffice (Rank #3 for Google Workspace alternatives): Capterra: 325, G2: 64, Brand search: 9.9K
• Slack (Rank #8): Capterra: 23,992, G2: 34,992, Brand search: 3.4M

This suggests that while G2 and Capterra reviews help establish trust, they aren’t the primary criteria for ranking.

Takeaway: Yes, G2 and Capterra reviews do influence ChatGPT results,  but only at the inclusion level. Review platform presence is almost always required to show up.

But it’s not enough to rank high. If you want to show up in ChatGPT results, being on review sites is the starting point, not the strategy.

Does high review count boost ChatGPT rankings?

Once we established that review platforms help with inclusion, we dug deeper: does having more reviews actually boost your position in ChatGPT’s rankings?

The data shows only a weak connection between the number of reviews and a tool’s placement:

• Capterra reviews vs. ChatGPT ranking: –0.21
• G2 reviews vs. ChatGPT ranking: –0.16

Some tools with just a few hundred reviews outranked tools with tens of thousands.

Example: Coda, with just 97 reviews on Capterra and 472 on G2, ranked #3 for Notion alternatives. In contrast, ClickUp had 4,490 reviews on Capterra and 10,331 on G2 but ranked lower at #4. This reinforces the insight that review volume does not directly determine position in LLM answers.

Takeaway: Weak correlation between review count and ChatGPT rank. More reviews give a small edge, but don’t guarantee top visibility. In many cases, LLMs seemed to prioritize contextual relevance over raw quantity.

Do high review scores boost ChatGPT rankings?

After reviewing review counts, we asked: do higher average scores help tools rank higher?

We checked whether tools with better G2/Capterra scores ranked higher.

• Correlation with Capterra score: +0.02
• Correlation with G2 score: –0.11

These values are close to zero, suggesting average review scores have little to no impact on tool position in LLM answers.

Example:

• Twist (Rank #9 for Slack): Capterra: 36, G2: 29
• Still ranked despite low scores and minimal reviews

Conclusion: Ratings help, but don’t significantly influence rank. Tools with low scores were still included, indicating that review quality isn’t a strong ranking factor.

Does high brand search volume boost ChatGPT ranking?

Brand popularity is often assumed to correlate with visibility. We explored whether tools with high global search volumes

We analyzed whether tools with higher search volumes consistently ranked better in ChatGPT answers. The result we got showed a weak correlation of –0.20.

Example: For ClickUp Alternatives:

• Monday.com (88K brand searches) ranked #2
• Outranked Trello (2.5M), Notion (4.4M), Jira (1.1M)

Example: Monday.com, with a brand search volume of just 88K, ranked #2 for ClickUp alternatives. In contrast, Trello (2.5M), Notion (4.4M), and Jira (1.1M) – all significantly more searched – were ranked lower. This emphasizes that brand visibility isn’t a strong predictor of placement.

While brand popularity may help a tool get included in LLM-generated answers, it doesn’t determine where it will rank. In our findings, lower-profile tools often outranked those with millions in brand search volume.This suggests LLMs prioritize topical relevance and authoritative context over sheer name recognition.

Takeaway: Brand popularity helps, but low-visibility tools still outrank major names.

Does having a Wikipedia page help?

We also tested whether having a structured, credible presence on Wikipedia influenced tool rankings. Wikipedia is one of the most cited sources on the web, often referenced directly or indirectly in LLM training data.

Takeaway: Having a Wikipedia page moderately improves your chances of ranking better – but isn’t required. It likely supports LLM inclusion by reinforcing brand legitimacy and structured context. In simple words, tools with Wikipedia pages tend to rank higher in ChatGPT answers.

Does high DR mean high ChatGPT ranking?

Backlink authority is a core signal in traditional SEO but does it matter in LLM rankings? 

Our analysis of Domain Rating (Ahrefs) showed a clear pattern: tools with stronger backlink profiles consistently ranked better in ChatGPT responses.

Takeaway: Moderate correlation: Higher DR is linked to better ChatGPT ranking, but not guaranteed. High DR is linked to higher ChatGPT placement – more than any other signal. Strong backlink profiles may increase a tool’s perceived authority during LLM training and retrieval, making them more likely to appear in AI-generated answers, but they’re just one part of the equation.

7 Key Takeaways for SaaS Brands

Here’s what we uncovered after analyzing hundreds of SaaS tools and dozens of “alternatives” prompts across ChatGPT and other LLMs:

1. High review scores help – but don’t guarantee top placement.
While strong G2 and Capterra ratings may boost trust, they don’t heavily influence ranking within ChatGPT. LLMs prioritize more than just a 4.7 average.

2. More reviews give a slight edge – but low-review tools can still outrank giants.
Tools with just a few hundred reviews often appeared above those with tens of thousands. It’s not about volume alone – it’s about how and where you’re mentioned.

3. Having a Wikipedia page improves chances of ranking higher.
We found a moderate correlation between Wikipedia presence and better placement. Structured, well-referenced entries may help tools appear more legitimate to LLMs.

4. Brand search volume doesn’t strongly influence rankings.
Popular tools aren’t always prioritized. In several cases, lower-search-volume tools ranked higher, suggesting LLMs care more about authority and relevance than mass awareness.

5. Domain Rating (DR) is the most reliable SEO signal for LLMs.
DR showed the strongest correlation with ChatGPT placement. Tools with strong backlink profiles were more likely to rank higher – reinforcing the power of high-authority mentions.

6. Some tools still rank high despite weak visibility.
Even products with low DR, minimal reviews, or no Wikipedia presence made it into ChatGPT’s top results – likely due to well-placed, context-rich mentions in trusted content.

7. Relevance and coverage may outweigh popularity.
LLMs aren’t just scanning for the loudest voices. They prioritize tools that are well-described, linked in reputable sources, and aligned with the specific prompt.

Final Word

G2 and Capterra reviews help with buyer credibility but they don’t guarantee LLM visibility.

Our research shows that what influences ChatGPT results is broader and more nuanced: backlink strength, mention relevance, consistent messaging, and machine-readable formatting. For a detailed guide, check out how you can get your brand into ChatGPT answers.

If you’re a B2B SaaS brand trying to show up in ChatGPT, Perplexity, or Google AI Overviews, you need more than reviews – you need LLM Optimization.

At Quoleady, we help SaaS brands:

1. Get featured in comparison listicles and curated roundups
2. Strengthen domain authority through quality content and backlinks
3. Structure your content and metadata to make it LLM-friendly

This research gives you clarity. Now it’s time to act on it.

If you’re ready to improve your chances of being recommended by ChatGPT, Gemini, or Claude…

Let’s talk.

Book a strategy call today.

The post Do G2 and Capterra Reviews Influence ChatGPT Rankings? [2026 Research] appeared first on Quoleady.

Honestly, I get annoyed every time I see “RIP SEO, welcome AEO/GEO/LLMO.” I’m hearing the same thing on sales calls too:

“We’re going to stop all our SEO activities and focus only on LLM visibility.”

Everyone is confused. I understand. But let’s not forget a few simple facts.

1. Traffic from LLMs is still only a fraction of overall acquisition. In one study of 20 SaaS companies, organic search drove 38% of traffic, while AI sources accounted for just 0.3%. Read that again: zero. point. three.
   
2. You want to show up in LLM answers. I want to show up there too. Everyone does. But burying SEO isn’t the way to get there. If anything, it’s the opposite. Without a solid SEO foundation, appearing in AI answers is close to impossible.

This huge demand for AI SEO services has led to an explosion of “experts” online. Suddenly, there are hundreds, maybe thousands, of GEO gurus, AEO pros, and LLMO advocates.

Everyone seems to have a custom approach, a unique framework, and “vast” expertise.

So I decided to sit down, review the agencies that actually get the job done, and help confused SaaS founders and marketers separate the wheat from the chaff.

But first, let’s start with the basics.

What is an AI SEO agency?

An AI SEO agency helps your brand show up in AI-generated answers while continuing to build strong organic visibility in Google.

At its core, it’s still SEO.

You still need:

• Clear site structure with page optimization
• High-quality, intent-driven content
• Relevant authority and mentions
• Technical foundations

What’s changing is how that work is shaped. Agencies need to think about how AI tools interpret content, which sources they rely on, and how SaaS brands are referenced across the web.

Instead of replacing SEO, we should focus on evolving it.

How is an AI SEO agency different from a traditional SEO agency?

Traditional SEO agencies focus on keyword research, organic traffic growth, backlinks and domain authority, and a few technical fixes. All of that still plays an important role.

But AI-focused agencies go further in how they think about visibility inside AI answers. They look at:

• How AI models summarize your category
• Which pages influence LLM recommendations
• How your brand is described across different sources
• Whether your product is consistently included in content LLMs cite

AI recommendations aren’t always easy to explain. But a good agency should be able to show the signals that influence them and what they’re doing to improve your position.

How is an AI SEO agency different from content or link-building agencies?

Content agencies are usually measured by output. How many articles were published. How much traffic grew. Link-building agencies are measured by placements. How many links were secured. What the domain ratings look like.

An AI SEO agency is measured differently.
The main question is: does your brand show up in AI answers or not?

That changes how the work is approached.

Instead of counting blog posts or backlinks, the focus shifts to a different question:

Does your brand actually show up in AI answers?

From there, the conversation becomes more practical:

• Are we present in the comparison pages people actually read?
• Are reputable sites mentioning us alongside competitors?
• Is our positioning clear and consistent everywhere?
• Do we appear in the same places as the category leaders?

AI SEO agencies still create content and build links but they don’t treat them as the end result.

What really matters is this: when someone asks AI about your category, does your product show up in the answer or not?

Why do SaaS companies need an AI SEO agency?

SaaS buying journeys are research-heavy and comparison-driven. Most B2B SaaS users are tech-savvy folks using LLMs on a daily basis to look for information. 

So they go to ChatGPT or other AI assistants to look for:

• “Best alternatives to X”
• “Top tools for Y use case”
• “Is X or Y better for B2B SaaS?”

AI tools now summarize these comparisons in a single response.

If your product isn’t included in that response, you miss a high-intent moment. For B2B and product-led SaaS companies, those moments often drive pipeline.

Many SaaS brands don’t have an AI SEO expert in-house. So they need agencies that understand how AI tools surface recommendations, alongside traditional traffic growth strategies.

How to tell if an agency can actually get you into AI answers

Anyone can say they offer AI SEO services. The real difference shows up in how they work and what they measure.

Here’s what to look for.

1. They understand how listicles influence AI answers

AI tools frequently pull answers from “best tools,” alternatives, and comparison-style content when forming recommendations.

A strong AI SEO agency will:

• Create high-quality list-style content strategically
• Work to get your brand featured in authoritative roundups
• Make sure your brand mentions appear on sites people and AI actually trust

Getting into relevant authoritative listicles helps your brand appear alongside the right tools in your category and as a result show up in AI answers.

2. They track prompts (on top of keywords)

Ranking #1 for a keyword doesn’t automatically mean your brand appears when someone asks an AI tool a related question. Although it does help a lot. According to a recent study by Ahrefs shows that 76% of AI Overview answers are pulled from URLs that rank Google top-10. 

AI SEO agencies:

• Focus on high-intent prompts like “best [category] tools” or “[product] alternatives”
• Check how AI tools respond to those prompts
• Track whether and how your brand is mentioned

AI answers are very dynamic. You can see one thing now and something slightly different in a minute. But if you track the same prompts over time, you start to notice which brands appear again and again. According to a recent study by Rand Fishkin, the SaaS industry shows relatively high consistency in AI answers. In his research, the same brands appeared in about 70–85% of responses within the SaaS niche.

3. AI SEO agencies work on brand mentions across trusted sources

Backlinks still matter. And as AI becomes a bigger part of search, brand mentions matter too, even when there’s no link.

AI systems often rely on:

• Recognized industry publications
• Review sites
• Comparison pages
• Repeated brand references in relevant contexts

That’s why these mentions matter. They affect which brands keep appearing in recommendations. A big part of the work of an AI SEO agency is helping your brand show up in the sources AI systems tend to rely on.

4. They structure content for AI interpretation

Search intent answered immediately, clean structure, clear headings, concise explanations, and strong internal linking all make it easier for AI tools to interpret your content.

AI SEO agencies work on:

• Logical page hierarchy
• Clear definitions and positioning
• Focused sections that directly address comparison search queries

It’s still great content for humans (not AI first). At the same time it’s organized in a way that’s easier for AI systems to understand and reference.

5. They specialize in SaaS

SaaS is different from e-commerce or local SEO.

Agencies should understand:

• Free trials and freemium models
• Integrations and ecosystems
• Feature-led comparisons
• Alternative and competitor-driven intent

AI answers reflect real buying behavior. If an agency doesn’t understand SaaS buying psychology, their strategy won’t align with how recommendations are formed.

6. They can show proof of AI visibility

This is quite simple. They should be able to show examples of the client brands mentioned in LLM responses (ChatGPT, Perplexity, Gemini, etc.). Examples showing that the brand they’ve worked with started appearing more frequently in AI answers would be also a great sign. 

7. They combine content, authority, and technical structure

AI visibility is usually the outcome of several things working together: relevant content, trusted mentions, and a solid site structure.

When each of these is handled separately, results tend to be inconsistent. Agencies that look at the full picture tend to do better.

How we selected the agencies for this list

I didn’t compile this list casually.

There are plenty of agencies now claiming to do AI search optimization. On LinkedIn, it feels like everyone suddenly became an AI SEO, GEO, AEO, or LLMO expert. But the group that actually understands how to build visibility for SaaS brands inside AI answers is still quite small.

Here’s how we evaluated them.

1. SaaS specialization: SaaS SEO is its own discipline. We prioritized agencies that work primarily with B2B SaaS and product-led companies, rather than generalist firms splitting focus across unrelated industries.
2. AI visibility expertise (AEO, GEO, LLMO): Each agency demonstrates a working understanding of how AI tools generate answers, how list-style and comparison content influences recommendations, and how brand mentions and authority shape visibility. We looked for practical applications.
3. Case studies: We looked for real examples of brands appearing in AI-generated answers, before-and-after improvements in visibility, and documented results tied to SaaS growth.
4. Authority-building approach: AI visibility is closely tied to authority. We evaluated whether agencies actively secured placements in relevant listicles and roundups, strengthened brand mentions across trusted publications, and built contextual, high-quality links. 
5. Thought leadership in AI SEO: The agencies shaping this space tend to share what they’re learning. We looked at whether they publish frameworks, insights, and original thinking around AI SEO. The strongest teams are actively refining their approach as SEO is evolving.
6. Ability to execute across the full stack: Finally, we assessed whether each agency can combine strategic content creation, authority and brand reinforcement, and technical and structural optimization. AI SEO works best when those areas are aligned. 

Top 10 Agencies Helping SaaS Brands Show Up in AI Answers

1. Quoleady

Overview

Quoleady is a B2B SaaS SEO and AI visibility agency that helps software companies grow through organic search, digital PR, and expert-driven content marketing. The company works with both startups and enterprise software brands. Quoleady helps clients improve visibility in Google and AI search, create research-backed content, build authority in their category, and drive qualified pipeline and revenue growth.

They work primarily with product-led and B2B SaaS teams that rely on organic search as a core growth channel.

Why this agency is relevant for AI SEO

Quoleady builds strategies around helping clients appear in AI answers, while continuing to grow performance in traditional Google search.

Their approach is grounded in classic SEO, but adapted to how AI tools summarize, compare, and recommend SaaS products.

What they do that affects AI visibility

• Develop content strategy and positioning to get clients brands into AI answers
• Create BOFU comparison and alternatives content aligned with real SaaS buying queries (blog articles and landing pages)
• Get SaaS clients into relevant industry listicles via their Listicle Manager tool
• Produce high-quality deep-research content that gets cited by LLMs
• Monitor AI visibility for clients to check whether clients brands are being referenced

Strengths

• Deep 6+ years of specialization in B2B SaaS
• AI visibility frameworks tailored to each client’s stage and niche
• Proven results: clients appearing in top AI answers, with public case studies
• A combined approach to content, authority, backlinks, brand mentions, and technical SEO

Limitations

• Boutique team with selective client capacity
• Focused primarily on SEO and content strategy rather than full-funnel paid or performance marketing

Best for

B2B SaaS companies that want to grow both traditional organic traffic and visibility inside AI-generated answers.

Not ideal for

Companies looking for a full-service marketing agency covering paid media, branding, and outbound.

Key services

• AI & SaaS LLM search optimization
• SaaS SEO (content + technical)
• SaaS content strategy & production
• Digital PR & link building
• Content refresh & optimization

Example clients / results

• Featured in AI Overviews for thousands of keywords (e.g., OneFlow with 7,000+ placements)
• Increased organic clicks and conversions for multiple SaaS clients
• Read their LLMO case study showing how they helped Legal Nodes drive a 47.5% increase in booked meetings and secure top 5 answer placements within three months

If you want to explore how your SaaS brand can increase both organic growth and AI answer visibility, book a strategy call with Quoleady.

2. Graphite

Overview

Graphite is a B2B SaaS SEO agency known for its editorial depth and focus on building topical authority in competitive categories. They typically work with growth-stage and enterprise SaaS companies that need to compete in crowded markets.

Why this agency is relevant for AI SEO

Graphite doesn’t position itself purely as an AI SEO agency. However, their emphasis on high-quality, authoritative content aligns well with how AI tools evaluate and reference brands.

Strong editorial standards and deep topic coverage increase the likelihood that a brand becomes a recognized authority in its category.

What they do that affects AI visibility

• Build topic clusters that strengthen semantic coverage
• Develop in-depth, research-backed content
• Focus on authority signals across competitive SaaS verticals
• Align content with real buyer journeys and comparison intent

Strengths

• Exceptional editorial quality
• Clear understanding of complex SaaS products
• Strong strategic thinking around topical authority

Limitations

• Quality-first approach can mean slower scaling
• Less emphasis on rapid experimentation

Best for

Mid-market and enterprise SaaS companies operating in competitive niches.

Not ideal for

Early-stage startups looking for fast content output or short-term experimentation.

Key services

• SaaS organic SEO strategy
• Editorial content and authority development
• Technical SEO and performance monitoring
• Topic authority and semantic mapping

Example clients / results

They’ve worked with companies like Notion, Webflow, and Hinge Health, delivering significant growth in organic traffic and search visibility.

3. Omnius

Overview

Omnius is a B2B SEO and Generative Engine Optimization agency working mainly with SaaS and fintech brands. They combine traditional SEO strategy with specific processes aimed at improving how brands are surfaced in AI-driven search tools.

Why this agency is relevant for AI SEO

Omnius openly integrates traditional SEO with strategies designed for how AI tools discover and cite content. Their positioning centers on helping brands strengthen visibility across both search engines and generative AI platforms.

What they do that affects AI visibility

• Content scoring and rewrites tailored for AI interpretation
• Schema and technical optimization that support AI crawlers
• AI search tracking and monitoring
• Content clustering aligned with semantic intent

Strengths

• Strong blend of technical SEO, content, and AI-focused practices
• Documented results across organic and AI-driven visibility

Limitations

• Focused mainly on SaaS and fintech
• Works best with brands that already have a solid SEO foundation

Best for

B2B SaaS and fintech companies ready to invest in both SEO growth and AI visibility.

Not ideal for

Companies with minimal content or no existing SEO baseline.

Key services

• Generative Engine Optimization (GEO)
• AI and LLM-focused SEO
• Content clustering and semantic strategy
• Technical SEO and answer optimization
• Analytics and reporting

Example clients / results

Case studies include large-scale growth, such as scaling from 0 to 2.7M clicks in 13 months and achieving 110% growth in six months for select clients.

4. Position Digital

Overview

Position Digital is an AI SEO agency that helps B2B SaaS startups get more qualified leads from organic and AI search. The agency sits at the intersection of traditional SEO and generative engine optimization (GEO), treating both as inseparable disciplines rather than competing strategies.

Why this agency is relevant for AI SEO

The agency combines on-page optimization to make it easy for AI systems to cite your content, and off-site brand building to make sure LLMs can recognize, trust, and recommend your software.

What they do that affects AI visibility

• Technical GEO audit to improve LLM crawability
• Regular content refreshes and optimization
• Entity optimization and brand positioning
• Securing placements in listicles that AI models already cite

Strengths

• Small, agile team that moves quickly and executes efficiently
• Deep understanding of the B2B SaaS landscape
• Proprietary listicle outreach tool called ListBrew

Limitations

• Fully focused on SEO and GEO rather than full-packaged digital marketing services 

Best for

B2B SaaS and tech startups that want to build a strong SEO foundation while also optimizing for AI search.

Not ideal for

Large organizations with multi-layer stakeholder alignment and slower implementation cycles.

Key services

• SaaS SEO strategy and technical audits
• AI search optimization
• Content marketing
• Link building and digital PR

Example clients / results

Their AI SEO results include a 435% increase in LLM citations for Decentriq (data collaboration platform) in 10 months, and growing AI citations from zero to 37 alongside an 80% YoY increase in free trial signups for HR Datahub (salary benchmarking tool).

5. Rank4AI

Overview

Rank4AI is a UK based AI search agency focused on improving visibility and recommendation likelihood across ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews and Google AI. Founded by Adam Parker, the agency does not run traditional SEO retainers or PPC campaigns. This work is variously described as AI SEO, GEO or AIO.

Why this agency is relevant for AI SEO

The agency focuses on how AI systems interpret, validate, and recommend brands — not just keyword rankings. Its methodology is built specifically around how platforms like ChatGPT, Copilot, Google AI Overviews, and Gemini evaluate entities and decide which businesses to mention.

What they do that affects AI visibility

• AI search audits across platforms
• Entity clarity and positioning assessment
• External ecosystem signal mapping
• Content structuring for AI extraction
• Long-term trust and authority signals

Strengths

• Founded by fintech strategy expert
• Five-signal AI evaluation model
• Hands-on implementation, not recommendations
• Education-first, transparent methodology approach
• Research across major AI platforms

Limitations

• Newer agency, but founder-led AI research with hands-on methodology across major platforms
• Smaller team, limited scale capacity, better suited for focused high-quality engagements

Best for

Businesses looking to improve how they are understood, trusted, and recommended by AI systems, especially those impacted by AI-generated recommendations or facing increasing competition in AI search results.

Not ideal for

Companies seeking full service digital marketing including traditional SEO, PPC or social media management. The focus is AI search visibility specifically.

Key services

• AI Search Audits
• Entity and Signal Alignment
• Content Structuring for AI
• External Validation and Citations
• Recommendation Readiness

Example clients / results

Ongoing research and content across LinkedIn, Medium, Substack, YouTube and podcast platforms focused on AI search visibility, entity engineering and the practical mechanics of how AI systems evaluate and recommend businesses.

6. Simple Tiger

Overview

SimpleTiger is a SaaS-focused SEO agency that blends AI-assisted workflows with traditional SEO strategy. They’ve been active in the SaaS space for years and position themselves as an AI-aligned SEO partner.

Why this agency is relevant for AI SEO

SimpleTiger incorporates AI tools into research, competitive analysis, and performance tracking, while still grounding strategy in core SEO principles.

What they do that affects AI visibility

• AI-assisted keyword research and competitive insights
• Content and technical SEO aligned with AI search trends
• Ongoing on-page optimization based on performance data

Strengths

• Clear SaaS specialization
• Integrated use of AI tools within their workflow
• Experience across a range of SaaS growth stages

Limitations

• Broader SEO focus rather than highly specialized AI answer placement tactics

Best for

SaaS companies looking for an SEO strategy that reflects both traditional search and AI-driven research behavior.

Not ideal for

Brands looking exclusively for niche AI answer optimization services.

Key services

• SaaS organic SEO
• Technical optimization and content marketing
• Link building
• AI-assisted research and strategy
• Optional paid search integration

Example clients / results

They report strong organic growth case studies, including adding 20K users in five months and 70K users in 12 months for SaaS clients.

Read about LLMs vs SEO: What’s Changing in Search Discovery

7. MADX

Overview

MADX is a SaaS-focused SEO agency known for selective client partnerships and strong authority-building strategies. They focus on tailor-made SEO strategies rather than standardized packages.

Why this agency is relevant for AI SEO

Their emphasis on authority, backlinks, and strategic content promotion strengthens overall discoverability, which plays a role in how brands are referenced across search and AI-driven environments.

What they do that affects AI visibility

• High-authority link acquisition
• Content promotion and outreach
• SEO strategies that improve relevance across competitive categories

Strengths

• Strong backlink quality
• Focused attention on a smaller number of clients
• Clear growth-stage SaaS experience 

Limitations

• Less explicit focus on AI-specific tracking or AI answer monitoring 

Best for

Growth-stage SaaS companies looking to strengthen authority and search visibility. 

Not ideal for

Startups seeking heavy experimentation around AI answer inclusion.

Key services

• Link building
• SEO strategy
• Content promotion
• Targeted outreach

Example clients / results

Examples include Thunes achieving #1 rankings, Gleemo gaining 140+ rankings, and UPSTIX growing from 0 to 8K qualified traffic.

8. Rock the Rankings

Overview

Rock The Rankings focuses on SaaS SEO and generative search visibility, with a strong emphasis on revenue impact. Their positioning ties SEO performance closely to pipeline and conversions.

Why this agency is relevant for AI SEO

They integrate AI search considerations directly into SaaS SEO strategy and connect that visibility to demos, SQLs, and revenue outcomes. 

What they do that affects AI visibility

• Structure content to improve clarity and extractability
• Monitor AI visibility across tools including ChatGPT and Perplexity AI
• Combine SEO with outreach and authority-building 

Strengths

• Revenue-first mindset
• Hands-on founder involvement
• Strong SaaS focus

Limitations

• Narrow focus on organic and AI search rather than full-stack marketing

Best for

SaaS companies that care deeply about pipeline and measurable outcomes from SEO.

Not ideal for

Brands seeking broad paid media, branding, or multi-channel campaigns.

Key services

• SaaS SEO and AI search strategy
• Revenue-focused content creation
• Outreach, link, and mention building
• Technical SEO and analytics

Example clients / results

They’ve supported companies like Toast and Bizzabo with strong traffic and conversion growth.

You might find this interesting: Top 9 Generative Engine Optimization (GEO) Agencies of 2026

9. First Page Sage

Overview

First Page Sage is an established U.S.-based SEO firm with a long track record in content-driven organic growth. They focus heavily on thought leadership and technical SEO.

Why this agency is relevant for AI SEO

Their work on topical authority and structured, high-quality content supports broader discoverability, which increasingly influences how AI search platforms evaluate trusted sources.

What they do that affects AI visibility

• In-depth SEO and technical audits
• Thought leadership content aligned with buyer intent
• Conversion optimization layered onto organic growth

Strengths

• Decades of SEO experience
• Strong lead generation focus
• Enterprise-level process

Limitations

• More traditional SEO positioning
• Less specialized around AI engines answer tracking

Best for

Enterprise and mature SaaS business with complex content and lead generation goals.

Not ideal for

Smaller startups looking for niche AI-focused experimentation.

Key services

• SEO strategy and consulting
• Thought leadership content
• Technical SEO and CRO
  Lead generation systems

Example clients / results

They’ve worked with companies such as Cadence and other technology firms in competitive markets.

10. WebFX

Overview

WebFX is a large full-service digital marketing agency with decades of SEO experience. They combine proprietary technology with traditional SEO and emerging generative search tracking.

Why this agency is relevant for AI SEO

WebFX has incorporated Generative Engine Optimization into its broader SEO offering, helping brands track and improve visibility across both classic search engines and AI-driven query environments.

What they do that affects AI visibility

• AI search visibility tracking across tools including ChatGPT and Microsoft Copilot
• Integrated SEO strategy that considers generative search
• Data-driven reporting supported by proprietary platforms

Strengths

• Large-scale infrastructure and resources
• Cross-channel expertise
• Proprietary tracking systems

Limitations

• Broad client base across industries
• Less specialized in niche SaaS compared to boutique agencies

Best for

Companies looking for full digital marketing support with SEO and AI search optimization readiness included.

Not ideal for

Brands that want a highly specialized SaaS-only SEO partner.

Key services

• On-page, off-page, and technical SEO
• Generative search optimization
• PPC and paid media
• Analytics and AI-powered insights

Example clients / results

They report over 12,000 leads influenced by AI-driven sources and offer internal dashboards for tracking LLM visibility.

Dig deeper into LLM SEO: Guide to ranking in AI powered search results

Comparison table of all 8 AI SEO agencies

How to choose the right AI SEO agency for your SaaS business?

If you’re at the stage of evaluating agencies, you already know search behavior is changing fast.

The real challenge now is figuring out which teams have actually adapted their strategy and which ones have simply updated their landing page copy.

Here’s how I’d approach it if I were hiring an agency for a SaaS company today.

Questions to ask on a sales call

Don’t get distracted by acronyms or polished slides. Ask direct questions that reveal how they think.

1. “How do you help SaaS brands appear in AI answers?”

Mention platforms like ChatGPT or Perplexity AI and see how they respond.

You’re listening for a clear explanation of:

• Comparison and alternatives content strategy
• Brand mentions across trusted sites
• Structured, scannable content
• How they test and monitor AI outputs

If the answer stays abstract or goes straight back to generic “great content and links,” they probably haven’t adjusted their approach.

2. “How do you track AI visibility?”

You should hear about:

• Prompt tracking
• Manual testing across AI tools
• Monitoring how often your brand is included in answers
• Some form of LLM visibility reporting

If the only tools mentioned are Google Search Console and keyword rank trackers, AI visibility likely isn’t part of the core strategy.

3. “What types of content influence AI recommendations?”

Strong agencies will talk about:

• “Best tools” pages
• Alternatives and competitor comparisons
• Use-case-driven content
• Clear category positioning

They’ll explain how SaaS buyers research and how that research shows up in AI-generated summaries.

4. “Can you show examples of brands you’ve helped appear in AI answers?”

This question filters out most agencies.

They should be able to show:

• Screenshots
• Real prompts
• Before-and-after examples

If they can’t demonstrate this for themselves or clients, they’re still experimenting.

Red flags to watch for

Some warning signs are subtle. Others are obvious.

• They guarantee AI answer placements: No agency controls AI outputs. Anyone promising guaranteed inclusion doesn’t understand how these systems work.
• They don’t specialize in SaaS: SaaS buying journeys are comparison-heavy and research-driven. Generic SEO frameworks often miss that nuance.
• They dismiss AI search as hype: That usually means they haven’t tested it seriously or updated their processes.

What proof you should request

Before signing anything, ask for tangible evidence.

• Screenshots or live demos of AI answers featuring client brands
• Case studies focused on visibility and positioning, alongside organic traffic
• Examples of comparison or list-style content they’ve influenced
• A clear explanation of how content, authority, and technical structure support each other

If an agency can’t show real proof, you’re likely funding their learning curve.

FAQ: AI SEO for SaaS

1. What is AI SEO?

AI SEO is the practice of strengthening your brand’s visibility inside AI-generated answers from tools like ChatGPT, Perplexity AI, and Google Gemini, while continuing to build strong organic search performance.

It builds on traditional SEO. The difference is that it also considers how AI systems summarize, compare, and recommend products.

2. What is AEO vs GEO vs SEO?

• SEO focuses on improving visibility in traditional search engines like Google Search.
• AEO (Answer Engine Optimization) focuses on improving how your content appears in direct answers and summaries.
• GEO (Generative Engine Optimization) focuses on influencing how AI models generate responses and recommendations.

There’s overlap between all three. For SaaS companies, they now work together rather than separately.

3. Do backlinks still matter?

Yes. Backlinks continue to support authority and trust. What’s changed is that broader brand recognition across trusted, relevant sources plays a bigger role in how AI systems interpret your position in a category.

Authority is built through consistent signals, not just link volume.

4. How long does it take to appear in AI answers?

There’s no fixed timeline.

Brands with strong existing authority may start appearing within a few months. In competitive categories, it can take longer. The key is consistency across content strategy, mentions, and technical clarity.

This is a long-term visibility investment, not a quick tactic.

5. Can a traditional SEO agency do this?

Some can.

The ones that succeed are those that have expanded their strategy to include prompt tracking, comparison-driven content, and broader brand positioning. Agencies that rely purely on keyword research or ranking and backlink volume will struggle to influence AI-driven recommendations.

Final thoughts

AI visibility is becoming a core part of how SaaS brands are discovered.

These days, many buyers ask AI tools for recommendations before they even open a website.

Strong SEO fundamentals still matter. Technical structure, authority, and intent-driven content remain the foundation. The difference in 2026 is that agencies also need to think about how brands are represented inside AI-generated answers.

If you’re investing in SEO, make sure the strategy reflects how SaaS buyers actually research today.

If you’d like to explore how this applies to your company, you can book a call with Quoleady and discuss your current visibility across both search engines and AI platforms.

The post 10 Best AI SEO Agencies For SaaS Companies in 2026 appeared first on Quoleady.

• LLM visibility is the new layer of search. If you’re not mentioned in AI answers, competitors win that attention first.
• In AI search, mentions and citations matter more than rankings, and brands can influence decisions without getting clicks.
• The best AI visibility tools track mentions, sentiment, citations, and competitors across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.
• Allmond offers the best mix of real AI scanning, fast setup, and affordable pricing, while Peec AI, Profound, Scrunch, and Otterly AI each fit specific needs.
• Use visibility insights to act: fix gaps, improve authority signals, and monitor competitor presence continuously.

Introduction to LLM Visibility: How Brands Appear in AI Answers

Remember when SEO alone was enough to secure online visibility? When ranking on Google meant your audience would naturally find you?

That era is quietly coming to an end. Today, customers aren’t just searching. They’re asking AI tools like ChatGPT, Claude, and Perplexity for answers, recommendations, and buying advice.

That’s why AI visibility tools have become essential. They help you understand how AI models describe and interpret your content and what you can do to improve your positioning.

In this article, we break down the top 15 LLM visibility tools in 2026. You’ll also learn how they help you stay visible in an AI-first world.

What Is LLM Visibility and Why Is It Important for Brands?

LLM visibility means how a brand appears in AI-generated answers and AI-powered search results like ChatGPT, Google AI Overviews, Google AI Mode, and Meta AI. 

Instead of competing only for search rankings, brands now compete to be included directly in AI responses that users see first.

LLM exposure goes beyond simple mentions. It includes how often a brand shows, how it’s described, and how it compares to competitors.

Without visibility into AI replies, brands risk being overlooked, misrepresented, or replaced by competitors.

What Are LLM Visibility Tools?

LLM visibility tools are platforms that help you measure, monitor, and improve how your brand shows in AI-generated answers and AI-powered search results. 

Instead of tracking rankings in traditional search engines, LLM tracking tools focus on whether – and how – large language models (LLMs) reference, describe, or recommend a brand across AI interfaces. 

In practice, the best AI visibility tools can help you:

• Track brand mentions in AI-generated answers
• Analyze sentiment and tone in AI replies
• Monitor visibility across platforms and prompts
• Compare AI mentions against competitors
• Identify gaps and optimization opportunities for AI search

Together, these insights reveal visibility gaps, explain why brands appear or disappear, and guide optimization for AI tools.

How LLMs Decide Which Brands to Mention?

LLMs decide which brands to mention by evaluating relevance, authority, and contextual fit when generating an answer. Rather than ranking brands, they aim to produce the most useful and credible response to a user’s prompt.

Content quality matters more than ever. Because LLMs synthesize information instead of ranking pages, clear, accurate, and consistent brand representation across trusted sources strongly influences whether a brand is included and how it is described.

Key factors influencing brand mentions include:

• Content relevance and clarity around specific topics or use cases
• Topical authority built through consistent coverage across trusted sources
• Entity associations that clearly link a brand to products, categories, or problems
• Source credibility and presence on authoritative websites
• Sentiment and reputation, reflected in neutral or positive brand references.

LLMs don’t intentionally select brands. They reflect patterns in the data and sources they rely on. As a result, consistent, high-quality brand representation strongly influences whether a brand shows and how it is described in AI search results.

LLM Visibility Tools, GEO and Traditional SEO: What’s the Difference?

Traditional SEO, GEO, and LLM visibility create brand exposure in different ways. Understanding this difference is essential as AI-driven discovery grows. 

SEO focuses on rankings and clicks, measuring success through traffic and SERP position. In contrast, AI-generated answers replace rankings with mentions. 

Brands gain visibility by being cited or referenced directly within AI replies, often without links or website visits.

This shift introduces zero-click exposure, where AI answers influence awareness, trust, and decisions before users ever reach a site. As a result, traffic alone no longer reflects true visibility or impact.

Generative Engine Optimization (GEO) defines how brands should adapt content, entities, and positioning so AI systems understand and select them. Best AI visibility tools then measure the outcome by tracking mentions, sentiment, and competitive presence in automated responses. 

SEO and LLM visibility work together: SEO builds authority and accessibility, while LLM exposure shows how brands are represented inside AI-generated responses.

How We Chose the Top LLM Tracking Tools

We built this list based on hands-on testing, product research, and a close review of each platform’s features and documentation.

Every tool was evaluated using the same criteria, including LLM visibility capabilities, ease of use, reporting, pricing, and overall value. We also considered who each tool is best suited for, such as agencies, SaaS teams, enterprises, or small businesses.

Rankings are not sponsored or paid. Features and pricing reflect information available at the time of writing and may change.

Top 15 LLM Visibility Tools in 2026

After reviewing and testing a wide range of platforms, we selected the top 12 LLM tracking tools in 2026 based on features, usability, and overall value.

1. Allmond: #1 LLM Visibility Tool for SaaS, E-Commerce, and Startups

Allmond is an intuitive AI visibility tracking tool built for agencies, SaaS businesses, e-commerce brands, and startups. 

It shows whether your brand is presented when ChatGPT, Claude, Gemini, and Perplexity answer industry questions across 67+ countries, with reports ready in seconds.

Designed for simplicity, Allmond delivers powerful AI exposure insights without complexity. Whether you’re managing multiple clients or growing your own brand, it offers professional visibility tracking at an affordable price.

Key Features

• Multi-LLM brand visibility tracking across ChatGPT, Claude, Gemini, and Perplexity, with coverage in 67+ countries.
• Powerful competitor intelligence showing competitors in AI answers and how they perform across the same prompts 
• Citation scans – Alllmond.app shows you whether your brand or competitor’s brand is present in the Citations URL that LLMs use to pull their answers from
• Automated visibility scoring and trend tracking with weekly scans and clear 0–100 visibility scores.
• Client-ready dashboards and reporting, custom branding, and no-login sharing. Team and agency-focused workflows supporting unlimited projects, prompts, and clients.

Pros and Cons

Allmond Pros include:

• High confidence in data accuracy, based on real AI replies rather than simulated or proxy data.
• Affordable pricing, fast time to value, with no technical setup and AI visibility monitoring starting within minutes.
• Intuitive design, making multi-brand management and instant report sharing simple and efficient.
• Clear action prioritization, highlighting visibility gaps and guiding outreach or content efforts effectively.
• Highly scalable and flexible usage, supporting unlimited projects and prompts for growing teams and agencies.

Its cons include:

• Focused on AI visibility, so teams looking for broader SEO, analytics, or marketing automation features may need additional tools.
• Simplified reporting, better suited for users who prefer clarity over deep metric customization.
• A weekly scan cadence may feel limiting for teams that prefer daily or real-time updates.
• Best value at scale, making it more attractive for agencies and multi-brand teams than for single-project users.

Allmond Pricing Structure

Allmond offers transparent, predictable pricing based on scan volume, with all features included in every plan. The website helps you estimate how many scans you need. While there’s no permanent free plan, a 7-day free trial is available.

If you need more scans mid-month, you can upgrade instantly or purchase a one-time scan pack without changing your plan.

The paid plans include:

• Starter – $29 per month for up to 240 scans.
• Growth – $99 per month for up to 825 scans.
• Pro – $199 per month for up to 1660 scans.

2. Omnia: The Best LLM Visibility Tool for Teams That Want to Act on Their Data

Omnia is an AI visibility platform built for marketing and growth teams at scale-ups who need more than monitoring. It tracks brand presence, share of voice, and competitor benchmarking across ChatGPT, Google AI Overviews, Google AI Mode, and Perplexity, then converts that data into a prioritized action backlog so teams know exactly what to do next. Where most tools stop at showing you where you stand, Omnia closes the loop between tracking and execution through its agentic layer.

Key Features

• AI Visibility Tracking across ChatGPT, Google AI Overviews, Google AI Mode, and Perplexity with daily refresh via real browser simulations, not API calls. Includes competitor benchmarking on all plans.
• AI Prompt Discovery surfaces real AI search prompts with volume and difficulty scores, giving marketers the keyword-research equivalent for AI-native search.
• Actionable Insights Engine runs citation analysis and turns findings into a prioritized backlog: what content to create, where to publish, and what gaps to fix. Insights feed directly into agents that can execute on the recommendations.
• AI Sentiment Analysis tracks how AI models describe your brand at the feature level compared to competitors, so you can spot positioning gaps before they widen.
• Unlimited countries and brands included across all plans, with no extra charges for multi-market tracking.

Pros and Cons

Omnia Pros include:

• Goes beyond monitoring by turning citation data into specific, prioritized actions your team can execute on immediately.
• Real browser simulations produce high-fidelity results that reflect what real users see in AI engines.
• Prompt discovery with volume and difficulty scoring fills a gap most visibility tools ignore entirely.
• Competitor benchmarking and sentiment analysis included on all plans, not gated behind enterprise tiers.
• Proven results: one customer (TUIO, insurtech) went from 8.55% to 11.79% global share of voice, surpassing legacy incumbents. Another (OkTicket) saw a 30% increase in AI-driven inbound leads.

Its cons include:

• The action-oriented features deliver the most value when paired with a team that has capacity to execute on recommendations.
• Pricing is in EUR, which may require currency conversion for non-European teams.
• Newer entrant compared to some competitors, so the community and third-party integration ecosystem is still growing.

Omnia Pricing Structure

Omnia offers a 14-day free trial with no credit card required, so teams can evaluate the full platform before committing.

The paid plans include:

• Starter — €79 per month, suited for teams beginning to track AI visibility across a focused set of prompts.
• Growth — €279 per month, designed for marketing teams actively working to improve their AI presence with a larger prompt volume.
• Scale — €499+ per month, built for teams running multi-brand or multi-market AI visibility programs at higher prompt volumes.

All plans include unlimited countries and brands. Pricing scales based on the number of prompts monitored.

3. OtterlyAI: The Best All-round LLM Visibility Tool

OtterlyAI is a lightweight LLM tracking tool designed to help brands understand whether and how they appear in AI-powered answers. 

It focuses on core monitoring features, making it accessible for teams that want visibility insights without complexity or high costs.

OtterlyAI is particularly useful for early-stage brands or teams new to LLM exposure. This AI monitoring tool provides a simple way to track brand references across AI search engines and identify basic visibility gaps, without deep technical analysis or advanced workflows.

Key Features

• AI search monitoring across major platforms, tracking brand citations in AI-generated responses.
• Prompt-based monitoring, translating relevant queries into conversational prompts for AI-focused strategies.
• Brand visibility and sentiment analysis, showing appearance frequency, positioning, and tone in AI-driven responses.
• Link and citation tracking, identifying source URLs, and AI-driven referral opportunities.
• Competitive visibility analysis, highlighting share of voice, competitor presence, and visibility gaps.

Pros and Cons

OtterlyAI Pros include:

• Affordable pricing, making LLM visibility tracking accessible for small teams and startups.
• Lightweight setup, enabling quick AI visibility monitoring without technical expertise or complex onboarding.
• Valuable visibility insights, supporting faster, more informed decision-making.
• Time-saving AI monitoring tool capabilities, allowing quick audits and efficient LLM tracking across AI platforms.
• Focused feature set, ideal for teams needing essential LLM tracking without added complexity.

Its cons include:

• Limited depth in analysis and reporting, with minimal guidance to help users improve content visibility in AI-generated results.
• Costs increase at higher tiers, which can reduce affordability as monitoring needs grow.
• No integrated content creation or optimization tools, requiring users to rely on external platforms for execution.
• Weekly data updates limit real-time visibility, preventing rapid response to sudden changes in brand references.
• Basic sentiment analysis, which lacks the deeper citation-level available in more advanced tools.

OtterlyAI Pricing Structure

OtterlyAI offers lower-cost plans designed for small teams and individuals. There’s no permanent free tier, but users can start with a 14-day free trial that includes premium features. Paid plans also offer optional add-on packages for expanded functionality.

The paid plans include:

• Lite – $25 per month (billed annually) or $29 per month (billed monthly) for up to 15 prompts tracked.
• Standard – $160 per month (billed annually) or $189 per month (billed monthly) for up to 100 prompts tracked.
• Premium – $422 per month (billed annually) or $489 per month (billed monthly) for up to 400 prompts tracked.
• Enterprise – Contact the OtterlyAI team to get pricing.

4. Arobis AI: Best for AI Visibility Optimization and Actionable GEO Insights

Arobis AI is an AI visibility tracking tool and agency designed to help brands understand, measure, and improve how they appear across leading AI search engines and answer platforms. This includes ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews.

Unlike many AI visibility tools that focus primarily on reporting mentions and citations, Arobis AI combines visibility tracking with actionable recommendations to help brands improve their presence in AI-generated answers. The platform tracks brand mentions, recommendations, citations, Share of Model (SOM), and competitor visibility while identifying opportunities to strengthen AI search performance over time.

It is best suited for SaaS companies, agencies, marketing teams, and brands that want to move beyond monitoring and actively improve their visibility across AI-powered search experiences.

Key Features

• AI Visibility Tracking across ChatGPT, Gemini, Claude, Perplexity, and Google AI. Overviews using prompt-level monitoring and multi-model analysis.
• AI Visibility Checker that quickly evaluates how often a brand appears, is cited, and is recommended across major AI platforms.
• Competitor Benchmarking and Share of Model (SOM) analysis to compare AI visibility against competing brands and identify market gaps.
• Citation and Source Analysis that reveals which websites, pages, and sources AI models rely on when generating recommendations.
• Actionable GEO Recommendations that help brands improve discoverability, strengthen citations, and increase visibility within AI-generated answers.

Pros and Cons

Arobis AI Pros include:

• Comprehensive tracking across leading AI search engines and answer platforms from a single dashboard.
• Combines monitoring with actionable optimization recommendations rather than reporting visibility metrics alone.
• Fast AI Visibility Checker that provides immediate insights into brand presence and discoverability.
• Strong competitor benchmarking and Share of Model analysis for measuring market position in AI search.
• Designed specifically for brands, agencies, and SaaS companies focused on AI search growth and Generative Engine Optimization (GEO).

Its cons include:

• Primarily focused on AI visibility and AI search performance rather than traditional SEO metrics such as backlinks and keyword rankings.
• The AI visibility category is still emerging, making benchmarks less established than conventional SEO reporting.
• Organizations new to AI search may require time to understand AI visibility metrics and their business impact.
• Best results are achieved when combined with broader SEO, content marketing, and digital PR efforts.
• Some advanced monitoring and reporting capabilities may require higher-tier plans.

Arobis AI Pricing Structure

Arobis AI offers a free AI Visibility Checker that allows businesses to assess their visibility across major AI platforms. Paid plans are designed for growing startups, SaaS brands, and enterprises that require ongoing monitoring, competitor benchmarking, citation tracking, and AI visibility optimization at scale Starting at a monthly retainer.

5. Peec AI: Best for Simple, Practical LLM Visibility Tracking

Peec AI is one of the early dedicated LLM tracking tools, specifically built to track brand visibility across major AI search engines and LLMs. This refers to ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. 

It captures daily visibility data to show which prompts trigger brand references and where citations originate. It also shows how a brand’s visibility compares to competitors. 

It’s best suited for brands with existing AI presence that want to monitor, benchmark, and refine it over time.

Key Features

• Daily brand presence tracking across ChatGPT, Perplexity, and Google AI Overviews using multi-model prompt testing.
• Clear dashboards and prompt management with AI-assisted suggestions, exportable reports, and API integrations.
• Competitive benchmarking to identify which competitors appear in automated responses instead of your brand.
• Source and citation analysis revealing URLs AI models rely on for PR, content, and link strategies.
• Sentiment insights and recommendations to prioritize high-impact improvements and strengthen AI mentions.

Pros and Cons

Peec AI Pros include:

• Clean, intuitive interface with quick setup, enabling teams to start tracking visibility with minimal onboarding.
• Comprehensive brand presence tracking across major AI platforms, including position, sentiment, and geographic context.
• URL-level citation and source analysis to show why specific results appear and how content influences automated responses.
• Strong competitive benchmarking and gap analysis, highlighting where competitors are featured instead.
• Flexible, scalable pricing, with major LLMs included by default, a free tier available, and paid plans that support unlimited users.

Its cons include:

• The platform reports brand citations and visibility metrics. However, it offers limited guidance on how to interpret the data or act on it to improve visibility.
• Tracking begins at sign-up, with no access to historical data from before the subscription, which restricts long-term analysis.
• Frequent daily updates can create data overload without clear prioritization or recommendations.
• Pricing may be high for small teams or solo founders relative to the available feature set.
• The tool focuses primarily on brand and entity tracking. It offers limited support for broader positioning or deeper analysis.

Peec AI Pricing Structure

Peec AI’s pricing is based on the number of prompts you track and the number of AI-generated responses analyzed each month. There’s no permanent free tier, but users can try the Peec AI platform during a free trial without providing a credit card.

The paid plans include:

• Starter – €75 (around $87) per month (billed annually) or €89 (around $104) per month (billed monthly) for up to 25 prompts tracked and 2250 AI answers analyzed.
• Pro – €169 (around $197) per month (billed annually) or €199 (around $232) per month (billed monthly) for up to 100 prompts tracked and 9000 AI answers analyzed.
• Enterprise – €424+ (around $493+) per month (billed annually) or €499+ (around $580+) per month (billed monthly) for up to 300 prompts tracked and 27000 AI answers analyzed.

6. Addlly AI: Best for Enterprise GEO & AI Content Operations

Addlly AI is an enterprise-focused GEO and AI visibility platform designed to help brands improve how they appear in AI-generated answers across ChatGPT, Google AI Overviews, Perplexity, Gemini, and Claude.

Unlike traditional SEO platforms that mainly focus on rankings and traffic, Addlly AI is built around AI discoverability and citation visibility. The platform combines AI visibility tracking, citation analysis, GEO recommendations, and AI-powered content generation into one centralized workflow for SEO and marketing teams.

Addlly AI is particularly well suited for enterprise organizations, agencies, and multi-market brands that want to scale GEO strategies across regions, languages, and content workflows while maintaining brand consistency.

Key Features

• AI visibility tracking across ChatGPT, Google AI Overviews, Gemini, Perplexity, Claude, and other LLM-powered search environments.
• Citation analysis and competitor benchmarking showing where brands are mentioned, which competitors dominate AI answers, and which sources AI systems rely on most.
• GEO recommendations focused on improving citation likelihood, AI interpretation, and brand discoverability in generative search.
• Brand-trained AI agents capable of generating GEO-optimized blogs, social posts, newsletters, and multilingual marketing content aligned with brand tone.
• Workflow automation that helps teams run audits, generate recommendations, and create optimized content without complex prompt engineering.
• Enterprise-grade infrastructure with SOC 2 and ISO 27001 compliance, governance controls, scalable deployment, and multi-LLM support.

Pros and Cons

Addlly AI Pros include:

• Combines AI visibility monitoring, GEO optimization, and content execution in one platform.
• Strong enterprise support with multilingual workflows, governance controls, and scalable AI operations.
• Built specifically for AI search visibility across multiple LLMs including ChatGPT, Gemini, Claude, and Perplexity.
• Brand-trained AI agents help maintain consistent messaging across campaigns and regions.
• Allows teams to create and optimize content directly inside the platform instead of relying on multiple external tools.

Its cons include:

• Enterprise-focused workflows may feel overly complex for smaller businesses or solo marketers.
• Advanced GEO analytics and citation tracking can require onboarding time for teams new to AI visibility strategies.
• Some advanced features and managed services require custom pricing discussions.
• Businesses looking for lightweight SEO-only tooling may find the platform broader than necessary.

Addlly AI Pricing Structure

Addlly AI offers custom pricing for businesses, agencies, and enterprise teams based on usage volume, GEO workflows, AI visibility tracking requirements, and managed services.

The paid plans include

• Standard Plan – Includes GEO query actions, recommendation reports, and AI content generation features.
• Advanced Plan – Custom pricing based on visibility audits, citation analysis, and content generation requirements.
• Enterprise Plan – Custom pricing for enterprise-scale GEO operations, multilingual workflows, governance controls, and managed AI visibility services.
• Agency & Managed GEO Services – Custom pricing based on client volume, tracking requirements, and managed optimization support.

7. Profound: Best for Enterprise-Scale AI Visibility and Strategic Insights

Profound is an enterprise-focused LLM tracking tool built for organizations that require depth, scale, and customization. It helps brands understand how they are surfaced, described, and compared in AI-generated responses across major LLMs and AI-powered search interfaces.

Unlike lighter tools, Profound is built for complex brand ecosystems. It supports large prompt libraries, multiple regions and languages, and detailed competitive analysis. 

It is best suited for brands that already invest heavily in SEO tools and content strategies, as it scales into generative search.

Key Features

• Comprehensive brand visibility tracking across ChatGPT, Google AI Overviews, Perplexity, and Claude.
• Competitive and category insights showing share of voice, competitor presence, and AI search visibility gaps.
• Source and citation analysis to identify websites and content AI systems that they rely on for answers.
• Conversation Explorer, which uses real user prompts to estimate AI demand and uncover emerging topics.
• Agent Analytics, which provides server-level insights into AI crawler behavior and indexing signals.

Pros and Cons

Profound Pros include:

• Built for enterprise and global brands, with security and infrastructure for complex data environments.
• Links data insights to content strategy, connecting Conversation Explorer findings with audience and market research.
• Action-oriented sentiment and prompt insights, including topic-level analysis and proactive visibility recommendations.
• Comprehensive visibility and citation metrics showing how AI systems reference and attribute content.
• Purpose-built for AI search and GEO, delivering deeper insight into AI behavior and indexing patterns.

Its cons include:

• High cost compared to many competitors, with enterprise pricing in the thousands, which can be less accessible for SMEs.
• Complex, data-heavy interface that requires time and expertise to use effectively.
• Limited built-in content creation and execution capabilities, as the platform is primarily focused on intelligence.
• Advanced features are only available on higher-tier plans, including full AI model coverage and API access.
• Some users report occasional technical issues, such as slow data exports and integration reliability problems.

Profound Pricing Structure

Profound offers pricing plans for brands and agencies, based on the number of prompts tracked. There’s no permanent free plan, but the Growth plan includes a free trial, allowing users to test the platform.

The paid plans include

For Brands:

• Starter – $82.50 per month with 2 months free (if billed annually) or $99 per month (if billed monthly) for up to 50 prompts tracked.
• Growth – $332.50 per month with 2 months free (if billed annually) or $399 per month (if billed monthly) for up to 100 prompts tracked.
• Enterprise – custom pricing available upon request.

For Agencies:

• Agency Growth – starting with $99 per month + add-ons
• Agency Enterprise – custom pricing available upon request.

8. AIclicks: #1 LLM Visibility Tool for Brands and Agencies

AIclicks is a Generative Engine Optimization (GEO) platform built for brands and agencies tracking, analyzing, and growing their visibility across AI-powered search. It monitors brand mentions across 10+ platforms including ChatGPT, Perplexity, Gemini, Claude, Grok, DeepSeek, Meta AI, and Google AI Overviews, with daily data refresh.

Where most tools stop at tracking, AIclicks goes further. It surfaces the real prompts buyers ask AI tools, maps where competitors beat you, and delivers a done-for-you action plan to close visibility gaps. Trusted by 900+ brands and 400+ agencies, the platform pairs GEO intelligence with built-in AI content agents that write, optimize, and track, all in one place.

Key Features

• Multi-platform brand visibility tracking across 10+ AI engines with daily refresh and country-level monitoring. 
• Competitor benchmarking and share of voice tracking, showing which prompts competitors dominate and why. 
• Citation and source analysis revealing which URLs AI engines trust and reference in your category, including Reddit threads, review sites, and third-party guides. 
• AI content agents that generate blogs, FAQs, and guides structured for AI extraction. 
• Done-for-you weekly action plans tied directly to visibility gaps, not raw data.

Pros and Cons

AIclicks pros include:

Broadest AI engine coverage on the market, tracking 10+ platforms including Grok, DeepSeek, and Meta AI alongside the four major engines. Daily data refresh instead of weekly, giving teams faster signal on what’s working. Built-in AI content generation removes the need for external tools to act on insights. Actionable recommendations tied to prompt-level gaps, not just aggregate scores. Scales from solo founders to agencies managing hundreds of client brands. Tracks Reddit citations, which is the second most-cited source in ChatGPT after Wikipedia, alongside traditional web sources.

Its cons include:

Higher starting price than lightweight trackers, which may not suit very small teams with minimal tracking needs. Feature depth requires some onboarding time to use effectively. AI content generation limits vary by plan, which can restrict output volume on lower tiers.

AIclicks Pricing Structure

AIclicks offers three main plans based on prompts tracked and AI answers analyzed per month. All plans include a 3-day free trial with no credit card required.

The paid plans include:

• Starter: $59 per month for 30 prompts tracked and up to 4,650 AI answers analyzed monthly, covering ChatGPT, Perplexity, and Gemini.
• Pro: $189 per month for 150 prompts tracked and up to 18,600 AI answers analyzed monthly, adding Google AI Overviews and unlimited team seats.
• Business: $499 per month for 300+ prompts tracked and up to 55,800 AI answers analyzed monthly, adding Claude and Grok coverage plus a dedicated account representative.
• Agency and Enterprise plans are available with custom pricing for teams managing multiple client brands at scale.

9. Rankshift AI: Best for Marketing teams & agencies focused on AI visibility tracking

Rankshift AI is an AI visibility tracking platform built to monitor how brands appear inside AI-generated answers across tools like ChatGPT, Gemini, AI Overviews, AI Mode, Copilot, Perplexity, Claude, Meta AI and Mistral. Instead of traditional keyword rankings, Rankshift works with prompt-based tracking, analyzing AI responses to detect brand mentions, citations, and competitor presence. 

The platform also provides share of voice metrics, historical tracking, and unique crawler analytics that show how AI bots interact with your website and which pages are used in answers.

Key Features

• Tracks brand mentions, citations, and competitor visibility across multiple AI platforms using prompt-based monitoring.
• Includes share of voice tracking to measure visibility against competitors over time.
• Sentiment analysis showing how AI perceives your brand, strengths, themes, and areas for improvement.
• Provides crawler analytics that show when AI bots visit your site and which pages are used in AI-generated answers.
• Supports integrations with tools like Looker Studio, BigQuery, and Power BI for advanced reporting.
• UI-based tracking (chat interface, not API) 
• Unlimited users and projects on every plan

Pros and Cons

Rankshift AI Pros include:

• Strong multi-LLM coverage across major generative platforms rather than focusing on one ecosystem.
• Direct insight into AI answer positioning, not just traditional SEO metrics.
• Competitor visibility benchmarking within the same AI-generated responses.
• Granular citation tracking and gap analysis, useful for GEO strategy development.
• AI crawler analytics, providing log-level insight into how generative engines discover and process site content.
• Flexible credit-based pricing, allowing prompt scheduling control and cost optimization.
• Unlimited users and projects, making it viable for agencies and larger teams.
• Focused product scope, avoiding feature bloat common in broader SEO platforms.

Its cons include:

• Requires thoughtful prompt setup: As with most LLM tracking tools, results depend heavily on the quality and coverage of chosen prompts. Poor prompt strategy limits signal quality.
• Credit-based system requires management: While flexible, improper scheduling or excessive prompt runs could deplete credits quickly if not planned strategically.

Rankshift AI Pricing Structure

Rankshift AI uses a credit-based pricing model. Unlike tools that force daily checks for every tracked prompt, Rankshift allows control over frequency, which can significantly improve cost efficiency.

The paid plans include:

• 30-day free trial available
• Paid plans start at €77 per month
• Unlimited users and unlimited projects included
• Flexible scheduling (daily, weekly, monthly prompt runs)

10. Scrunch AI: Best for Persona-Based LLM Visibility Insights

Scrunch AI focuses on how brands are presented in AI-driven responses across different personas, intents, and audience segments. Instead of treating AI visibility as one-size-fits-all, Scrunch AI analyzes how answers change depending on who is asking the question and why. 

This makes Scrunch AI especially useful for brands with multiple customer segments or complex buyer journeys. 

This LLM tracking tool helps teams understand not just whether they appear in AI-driven responses, but which audience sees them, in what context, and alongside which competitors.

Key Features

• Persona-based LLM tracking across AI-generated responses, showing how brand presence varies by audience segment and intent.
• Analysis of brand citations by intent, audience type, and use case, revealing how positioning changes across different buyer contexts.
• Competitive visibility insights within persona-specific AI-driven responses, highlighting which competitors appear for different audiences and scenarios.
• Monitoring across major LLMs, ensuring consistent visibility and insights across platforms where users ask AI queries.
• Agent Experience Platform (AXP), which enables brands to create and evaluate a separate website optimized specifically for AI engines.

Pros and Cons

Scrunch AI Pros include:

• Strong focus on visibility across different audience types, providing deeper insight than traditional brand citation tracking.
• Clean, intuitive dashboard with an easy setup and workflow, making prompt management and metric interpretation accessible for non-technical teams.
• Insightful competitor analysis that clarifies how brands feature and compete in AI-generated responses.
• Actionable insights and advanced monitoring help teams improve and strengthen brand visibility across AI search engines.
• Responsive, collaborative customer support that improves onboarding and overall user experience.

Its cons include:

• Limited technical and crawl-level analysis, reducing usefulness for infrastructure or AI crawler insights.
• Less effective for single-audience brands, where persona-based insights add limited value.
• Limited execution and optimization workflows, requiring external tools to act on insights.
• Prompt data lacks volume metrics, limiting understanding of demand and user intent.
• Restricted exporting and visualization options, often requiring manual reporting workarounds.

Scrunch AI Pricing Structure

Scrunch AI’s pricing depends on the number of users and tracking scope. There is no permanent, no-cost plan, but the entry-level plan includes a 7-day free trial to test the platform. This AI visibility tracking tool offers benefits for agencies, with pricing available upon request.

The paid plans include:

• Explorer – $83 per month (billed annually) or $100 per month (billed monthly) for up to 100 prompts tracked.
• Growth – $417 per month (billed annually) or $500 per month (billed monthly) for up to 700 prompts tracked.
• Enterprise – custom pricing available upon request.

11. Hall: Best for Citation-Level LLM Brand Tracking

Hall focuses on where and how brands are cited in AI-powered answers. Instead of limiting analysis to prompt-level mentions, this LLM tracking tool identifies which domains, pages, and brands LLMs reference and how consistently they appear across models.

This LLM tracking tool connects LLM visibility directly to content authority, citations, and source-level performance.

This makes Hall especially valuable for SEO teams, publishers, and content-led brands seeking to understand why they appear in AI replies, not just whether they are mentioned.

Key Features

• Citation-level LLM tracking, showing which domains and pages are referenced by AI engines in generated answers.
• Source and authority analysis, revealing which content formats, domains, and publishers LLMs consistently rely on when generating answers.
• Brand and competitor citation monitoring, showing which brands are referenced together and in what informational context.
• Multi-model coverage, tracking visibility across leading LLMs and AI-powered search experiences.
• Historical LLM tracking and trend analysis, allowing teams to monitor gains or losses in AI citations over time.

Pros and Cons

Hall Pros include:

• Deep focus on citations and sources, providing clearer insight into how LLMs evaluate authority and trust.
• Strong alignment with SEO and content strategy, making it easier to tie AI visibility back to specific pages and publishers.
• Clear competitive insights that show which competitors are earning AI citations and why.
• Clean reporting around references and mentions, useful for audits, benchmarks, and stakeholder reporting.
• Particularly valuable for publishers, media sites, and content-heavy brands.

Its cons include:

• Limited persona or intent-based analysis compared to tools focused on audience segmentation.
• Less guidance on execution, requiring teams to translate insights into content or technical changes themselves.
• Users report data limitations in the free version, especially when tracking multiple sites or APIs at scale.
• May feel overly granular for teams looking for high-level brand visibility summaries only.
• Smaller teams may find it takes longer to master it, especially if they are unfamiliar with citation-based analysis.

Hall Pricing Structure

Hall offers a permanent free plan with basic functionality. Pricing is transparent and predictable. It’s based on tracking scope, number of projects, supported platforms, and data depth requirements. 

The paid plans include:

• Starter – $199 per month (billed annually) or $239 per month (billed monthly) for up to 500 tracked questions.
• Business – $499 per month (billed annually) or $599 per month (billed monthly) for up to 1000 tracked questions.
• Enterprise – starting from $1499 per month.

12. Clearscope: Best for AI-Informed Content Optimization

Clearscope is one of the tools for AI visibility that focuses on improving content relevance and topical authority using AI-driven keyword and semantic analysis. 

While not a pure LLM tracking tool, it helps brands optimize content in ways that align closely with how AI engines evaluate information.

This makes Clearscope especially useful for content, SEO, and editorial teams that want to strengthen content quality and authority. These factors increasingly influence how brands feature in AI-generated responses and AI-powered search experiences.

Key Features

• AI-powered content optimization, providing keyword, topic, and semantic recommendations based on top-performing content.
• Content grading and scoring, showing how well a page aligns with expected topical coverage and relevance.
• Competitor content analysis, revealing which terms and topics competing pages include.
• Integration with writing workflows, including Google Docs and popular CMS platforms.
• SERP-based insights, helping teams align content with real search and informational intent.

Pros and Cons

Clearscope Pros include:

• Strong emphasis on content quality and topical depth, enabling teams to create authoritative, AI-friendly content with higher relevance and credibility.
• Clean, intuitive, user-friendly interface that fits seamlessly into existing editorial and content workflows.
• Actionable, data-driven recommendations with smooth integrations for tools like Google Docs and WordPress.
• Automated competitor analysis that reveals outlines, word counts, and topic coverage. This significantly reduces manual research time.
• Responsive customer support, quick to help with guidance that goes beyond basic how-to questions.

Its cons include:

• No direct tracking of brand references or citations, limiting insight into how content is surfaced or referenced by AI engines.
• Limited visibility into content performance across different LLMs or AI-powered search platforms.
• Narrow focus on content optimization, without broader SEO capabilities offered by all-in-one SEO tools and platforms.
• Higher pricing compared to lightweight SEO tools, which may present challenges for smaller teams or individual creators.
• Less suitable for brands prioritizing prompt-level insights or real-time AI visibility monitoring.

Clearscope Pricing Structure

Clearscope does not offer a permanent no-cost plan. Pricing is based on user seats and feature access. This LLM tracking tool’s paid plans include optional add-on packages that unlock additional features and expanded functionality.

The paid plans include:

• Essentials – $129 per month for up to 20 tracked topics and topic explorations.
• Business – $399 per month for up to 20 tracked topics and topic explorations.
• Enterprise – pricing available upon request.

13. BrandLight: Best for Brand Mention and Sentiment Tracking in AI Answers

BrandLight focuses on monitoring how brands are mentioned, positioned, and described in AI-powered answers. 

It analyzes brand presence, sentiment, and contextual framing across LLMs, helping teams understand how AI narratives shape brand perception.

This makes BrandLight especially useful for marketing, PR, and brand teams that want visibility into how their brand is represented in AI-driven responses, alongside competitors, and across different query types and platforms.

Key Features

• Brand citation and positioning monitoring across AI-generated responses to track visibility trends.
• Content gap identification for topics where brands are absent or weakly represented.
• Source and publisher analysis showing which references influence AI brand inclusion.
• Technical visibility insights into how AI systems access and interpret brand content.
• Alerting and reporting capabilities for brand references and sentiment changes in AI-driven replies.

Pros and Cons

BrandLight Pros include:

• Strong focus on brand mentions and sentiment, helping manage AI-driven brand narratives.
• Intuitive dashboards for marketing and PR teams, with fast onboarding and minimal setup.
• Valuable competitive insights into brand positioning and co-mentions across AI platforms.
• Clear visibility into brand exposure trends, supporting messaging and reputation decisions.
• Enterprise-ready design, offering centralized oversight across brands, regions, and teams.

Its cons include:

• Limited insight into citations and sources behind AI responses. This makes it less helpful for content and SEO analysis.
• Minimal support for content optimization or technical SEO tasks. This means teams need other tools to act on the insights.
• Sentiment analysis stays at a high level and may miss deeper context or nuance in how brands are described.
• Less useful for teams focused on building content authority or analyzing publishers and sources.
• Non-transparent pricing makes early cost comparison more difficult.

BrandLight Pricing Structure

BrandLight does not publicly list detailed pricing. Plans are typically customized based on tracking volume, number of monitored brands, and feature access. Pricing details, demos, or trials require contacting the sales team.

14. Nightwatch: Best for Hybrid SEO and AI Visibility Tracking

Nightwatch combines traditional SEO rank tracking with emerging AI and LLM tracking. This gives teams a comprehensive view of how their content performs in both search interfaces and AI-generated responses. 

Its dual focus helps brands understand visibility across classic SERPs and modern conversational search formats.

This makes it easier to align SEO strategy with evolving AI-driven discovery.

Key Features

• AI & LLM tracking, measuring how often your brand and keywords appear in responses from models like ChatGPT and Claude.
• LLM tracking metrics like average position, share of voice, and sentiment to compare performance against competitors.
• Advanced rank tracking, monitoring keyword positions across search engines, locations, and devices.
• Competitor benchmarking, showing visibility trends versus rivals in both SEO and AI search contexts.
• Custom dashboards and reporting, designed for internal analytics and client communication.

Pros and Cons

Nightwatch Pros include:

• Clean, intuitive dashboards for SEO teams, with fast onboarding and flexible tracking setup.
• Combines SEO rank tracking with AI visibility insights across search engines and AI platforms.
• AI visibility metrics, including share of voice, average position, and sentiment across models.
• Clear performance trend visibility across organic search and AI results for better decisions.
• Scalable for professional and enterprise teams, with robust reporting, APIs, and centralized project management.

Its cons include:

• Features like AI and LLM tracking may feel less detailed than more advanced tools for AI visibility.
• The platform focuses on tracking and reporting, offering limited guidance for on-page optimization, backlink analysis, or hands-on SEO recommendations.
• With many features and configuration options, new users may need some time to fully use the platform.
• Less suitable for teams that need deep citation, source-level, or authority analysis within AI-powered answers.
• Pricing scales with the number of keywords tracked, which can become costly for teams tracking large keyword sets or multiple markets.

Nightwatch Pricing Structure

Nightwatch offers tiered pricing based on tracked keywords, locations, and feature access. There is no free tier, but a 14-day free trial is available, so users can test this LLM tracking tool. Paid plans scale with usage and analytics depth.

The paid plan starts with $32 per month (billed annually) or $39 per month (billed monthly) for up to 250 tracked keywords.

15. Similarweb: Best for Market and Traffic Intelligence at Scale

Similarweb is an LLM tracking tool that provides deep data on website and app traffic, audience behavior, and competitive performance. 

Recently, it expanded into AI Brand Visibility and Gen AI Intelligence, enabling brands to track how they influence AI outputs. 

This makes Similarweb especially useful for marketing and strategy teams seeking a unified view of performance across traditional search and AI platforms. Backed by rich behavioral and competitive data, teams can better understand how AI visibility connects to real traffic.

Key Features

• AI Brand Visibility tracking shows when, how often, and where brands appear in LLM-generated responses and the prompts driving that visibility.
• AI Traffic insights reveal how much referral traffic AI tools like ChatGPT generate and which pages benefit most.
• Prompt and citation analysis help teams understand what queries lead to visibility and which sources influence AI citations.
• Competitive benchmarking highlights how a brand’s AI visibility compares to rivals for the same topics or prompts.
• Traditional digital intelligence includes web traffic metrics, audience behavior, and market performance across channels.

Pros and Cons

Similarweb Pros include:

• Combines traditional web and AI search visibility data for a complete view of brand performance.
• Comprehensive prompt and topic insights, revealing what users ask and why it matters.
• Competitive benchmarking across AI visibility and referral traffic for strategic context.
• Intuitive dashboards and visualizations that simplify trend analysis and insight sharing.
• Rich digital intelligence beyond AI, including audience behavior and market trend insights.

Its cons include:

• AI visibility and prompt analysis are newer, offering less depth than LLM-only tools.
• Focused on insights and benchmarking, with limited support for hands-on SEO execution.
• Traffic and engagement data are estimated, which may reduce accuracy for smaller websites.
• Less specialized for deep citation or source authority analysis in AI-powered answers.
• Advanced features are expensive, limiting accessibility for small teams and early-stage businesses.

Similarweb Pricing Structure

Similarweb does not offer a permanent no-cost plan, but a free trial is available to test the platform. Pricing is customized based on features, data depth, and usage needs, with enterprise tiers and custom options available through the sales team.

The paid plans include:

• Competitive Intelligence – $125 per month (billed annually) or $199 per month (billed monthly).
• Competitive Intelligence & SEO – $335 per month (billed annually) or $399 per month (billed monthly).
• Competitive Intelligence, SEO & Ads – $540 per month (billed annually) or $649 per month (billed monthly). This plan is currently not available and will launch soon.

Comparison Table of All Top 15 LLM Visibility Tools

Choosing the right AI visibility tracker depends on what you want to track, how many brands or clients you manage, and how quickly you need clear, usable insights. Some tools focus on deep analysis, others on enterprise reporting, and some prioritize simplicity and speed.

To make the decision easier, here’s a quick comparison of the best AI visibility tools:

How to Choose the Best Visibility Tool for You?

All of the covered LLM tracking tools can help you understand how your brand shows in AI-generated responses. However, Allmond stands out for teams that want results without complexity.

Allmond focuses on what most teams actually need: seeing whether their brand appears in real AI responses. It helps teams understand visibility gaps and share clear reports with clients or stakeholders. There’s no technical setup, no feature overload, and no lengthy onboarding.

Because all features are included in every plan, costs are predictable and easy to manage. This makes Allmond a strong fit for agencies, SaaS companies, e-commerce brands, and startups that want professional AI search visibility tracking without enterprise friction or cost.

If you’re looking for a practical, easy-to-use LLM tracking tool that delivers clear insights fast and scales as your needs grow, Allmond is a strong choice.

LLM Visibility Tools FAQ

1. What is LLM visibility and why does it matter?

LLM visibility shows whether your brand is present in AI-generated responses. It matters because users increasingly rely on AI tools, not search engines, to discover brands and products.

2. Who should care about LLM visibility?

Anyone with a website, product, or brand should care about LLM visibility to avoid competitors gaining visibility in AI outputs while they remain focused only on traditional SEO strategies.

3. Is technical knowledge required for LLM tracking?

No. Most LLM tracking tools are designed for non-technical users and provide simple dashboards, prompts, and reports without requiring coding or complex setup.

The post Top 15 LLM Visibility Tools in 2026 appeared first on Quoleady.

• First, the good news. LLM SEO is not a completely new universe that you need to relearn from zero. It’s more like an extension pack on top of your current SEO thinking. 
• The bad news, though, is that it does require some significant adjustments. Especially if you’re still optimizing only for traditional search behavior. But, this is not that bad, right? 

So, your job these days is not to rank in search engines alone. Now, you have to earn LLM visibility and become citable. In other words, make your content the kind of source AI models naturally want to pick. 

How do you do that? We’re about to explain it in detail in this guide. 

What LLM SEO really is

LLM SEO is the practice of making your content findable and citable by AI search engines. But the real difference lies in the tactics and priorities you need to change compared to traditional SEO. 

When it comes to the usual search engines, you mainly optimize for keywords and SERP intent. In LLM search, you optimize for:

• Information structure,
• Clarity and uniqueness,
• Factual density, 
• Your broader digital reputation.

None of these will hurt your “normal” optimization, though. After all, a clear structure and better brand visibility will only benefit SEO, right? So, it isn’t an LLM vs. SEO battle. These are just two different approaches that are still interconnected.

A quick experiment: Is LLM different from traditional optimization?

Let’s run a simple experiment to understand how LLM “algorithms” differ from traditional optimization. We asked ChatGPT to give us some “web design agency” suggestions. 

This is what we got:

Now, let’s check whether these brands quoted by ChatGPT are actually ranking well for the keyword “web design agency”. 

Note: We’ve used Semrush and the US region for this. So, the results might differ a bit with a different setup. But they will still be similar. 

The first two brands rank in positions #1 and #10. Awesome, this makes total sense.

But what about the rest? This is where it gets interesting.

One brand ranks in position #78 (!). This means that it’s basically invisible in SERPs: pretty much no one goes beyond the second page of search when googling.

But wait for it. It gets better than that. The two other brands aren’t even in the top 100 of SERPs. Moreover, one of those websites literally has no organic keywords except for their brand name. 

And the other one only ranks for branding-related keywords.

So, how, in the world, did ChatGPT find them? And why did it decide to recommend them?

We’ll get into the exact tactics in the next section. But here is a quick answer to satisfy your curiosity. Both of these brands have 7K+ backlinks. And many of them come from articles like “X Best Web Design Agencies”. 

That’s why, when assessing your link-building strategy next time, you might consider getting some link exchanges for listicles like that. And even if you can’t get an exchange deal, sponsored blog posts where your brand pays a fee could also work, as long as the content is high-quality.

To sum all this up, you can already take note that LLMs often pay more attention to:

• The overall brand exposure (i.e., how often you get mentioned on other credible sites).
• Listicles like “X Best…”.

6 tactics to rank in AI search

Now, let’s explore the strategy we’ve tested across multiple websites, which consistently earned citations in AI results. All you need is 6 steps.

1. Make your website AI-readable

Here, you have to combine technical SEO best practices and LLM optimization. A few non-negotiables for that are: site and content structure and schema.

Use a predictable site structure 

Why is this a must? Because LLMs hate chaotic layouts. All they demand from you is clarity. So, make sure that your:

• Website and URLs have a clear hierarchy (e.g., /section/subsection/topic).
• Headings (H1, H2, H3, etc.) are consistent.
• Sections have descriptive headings and target one point at a time.
• Paragraphs stay short.
• Content is clearly formatted with bullet points, lists, and visuals.

Use a schema where it makes sense

LLMs love structured data because it makes everything…clear (yes, again). So, it’s a good idea to add:

• FAQ schema,
• HowTo schema,
• Product schema (if relevant),
• Author schema (for credibility),
• Article schema (for more chances to get cited).

2. Rethink keyword research for AI

The biggest mistake people still make is assuming that the phrases users type into Google are the same as what they ask ChatGPT or Perplexity. Well, surprise-surprise, they aren’t:

• Traditional search queries are short and often clipped.
• Prompts, on the other hand, sound more like a conversation.

Let’s take a look at the actual example. 

• Someone might google “espresso beans best”. Or something else a bit unnatural to a human ear, but completely okay for search engines. 
• But in an LLM, the same person would type: “I’m new to making espresso at home. What beans should I choose?”

That’s the real shift you must acknowledge. There is no reason to chase awkward keyword stems anymore. Instead, you want to understand how people think about your topic.

What could you do practically?

Keep targeting your usual search queries based on the volume and keyword difficulty that works for your domain authority, but also do this:

• Target whole keyword clusters to make sure that you’re becoming an authority for your main topic. Start with something smaller if you can (personal finance, apartment gardening, etc.).
• When writing content, don’t limit yourself to just search queries from your SEO tool. Create a question map that humans ask, and address your keyword from different angles. Go deeper to avoid creating a superficial “SEO-friendly post”.

3. Adjust your content strategy

Most people write with Google in mind, which means they care about ranking positions. But LLMs don’t care much about that (we’ve already seen this in our “web design agency” example). They look for content that is complete, trustworthy, and usable.

We’ve already touched on making your long-form pieces more practical and usable by answering more questions related to your topic. But there is more to that.

When writing blog posts, try to always add at least some of these elements: 

• Deep explainers with real-world examples: Models pull these because they answer the query properly. Besides, real-world examples are unique and actionable, which is something any AI search engine can’t replicate.
• Comparisons and pros/cons breakdowns: These simply mimic the structure of AI answers. And when you create them based on your experience, you’re more likely to end up being cited.
• Content focused on niche expertise: This gives you authority signals that LLMs notice. But this expertise only comes with time, after publishing dozens of quality content pieces and being mentioned in several external sources.
• Original research or data-backed posts: LLMs want “safe” sources, and numbers feel that way. Besides, stats make your content more credible for humans as well.

If you add at least one of these to every post you publish, you have a much better chance of getting into AI Overviews and other AI search results.

4. Write with AI in mind

Who are we even writing for these days? Search engines, LLMs, or our readers? That’s a good question. Ideally, we should be writing for all of them. While it sounds like “mission impossible,” it isn’t that hard. And you’ll see why.

Writing “AI-friendly” content is mostly about little things that will benefit your overall SEO and make your copy clearer for visitors. Here is what you could do:

• Lead with clear answers. If your H2 is “What is the Mediterranean diet?” Your first sentence of that section should ideally be “The Mediterranean diet is…” Be direct first and add examples, stories, etc., later.
• Add summaries. You can add summaries, key points, TL; DRs, etc., in every major section or just one per article.
• Use expert and human language. Something like, “Here’s what we tested…” or “From our experience…” sends a sign that you know your thing instead of just rephrasing what’s already out there.
• Formatting. We’ve already mentioned it. But this is really important for LLM rankings, so once again: make your content look clean and skimmable.

5. Work on your brand reputation and external mentions 

LLMs look beyond just your website to determine how credible you are. So, off-page SEO matters even more now for LLM visibility. If you remember the example we’ve seen in the beginning, most of the brand recommendations we got from ChatGPT didn’t have high DA/DR or top rankings in SERPs. Instead, they had lots of links. What could these be?

• Mentions on industry blogs and other websites that are related to your niche.
• Review platforms like Capterra, G2, etc. 
• Product/service reviews and interviews with your team.
• Reddit threads and Quora answers.
• Product/service roundups and those “X Best…” listicles.

If your brand is mentioned across trusted sources, LLMs treat you as someone they can trust. But how do you get all those brand mentions? While there are many link-building tips, you should always start with a strategy:

1. Find tactics that work for your business.
2. List websites and platforms that fit your requirements.
3. And turn it into a consistent process you actually care about.

From there, you’ll only need to find proper tools and backlink services that can make this process easier. And over time, you’ll see how your overall brand exposure helps improve your LLM rankings.

6. Become a true source

This is the single most important part of LLM optimization, and the one most of us underestimate.

LLMs don’t need a 1,500-word paraphrase of what’s already on the first page of Google. Why? They were trained on that. They already know it. What they want (and what they cite) is information that came from a human who has actually done the work.

Your content should reflect your true experience: 

• Things that worked for no logical reason,
• Mistakes you made during campaigns, 
• Strategies you refined, 
• Real numbers. 

Give super-specific examples, provide screenshots, and the weird little nuances that no generic guide mentions. If you want your articles cited, give the model something it can’t find anywhere else. Be the person who brings new angles, new questions, and new thoughts.

LLM visibility in action: Real examples that actually work

Let’s look at some pieces that show up in AI-generated answers, and understand why.

1. Clear examples and content depth

First, we have an article titled Indirect distribution channels explained with examples that made it to AIOs.

Why was it cited? There are at least 3 good reasons for that: 

1. It includes very clear, real-life examples (you have a whole section on them).
2. It has depth and addresses exactly what users want to know (not just the definition, but also pros and cons, use cases, things to consider, indirect vs. direct channels, etc.).
3. It’s detailed and non-fluffy with a structure that LLMs understand.

2. A listicle with detailed descriptions

The next example is an article on Google alternatives. 

Here are the reasons why this one has worked: 

The structure is clean and understandable for both humans and AI.

It’s a listicle, and it’s something that LLMs like, as we’ve already seen.

You get a detailed description of each tool with unique features and best use cases.

3. Original research within your area of expertise

Here comes the research titled How much should you pay for a guest post [We analyzed 37,542 websites].

There was no doubt that this guide would end up in AIOs. Why? Because:  

• It’s original research within the expertise and topic authority.
• The part that “we analyzed 37K+ websites” gives actual credibility.
• The content is very detailed because you can see prices based on traffic, domain authority, niche, geography, etc.

Conclusion

You can safely say that traditional optimization was about rankings, while LLM SEO is about being chosen as a reliable source. Now, you’re competing for trust.

The path to earning AI visibility is not as hard as it may seem. But it requires you to be intentional. Be generous with your knowledge and transparent with your experience, and you’ll get there.

The post LLM SEO: Guide to ranking in AI search results appeared first on Quoleady.

But just because the way people search is changing doesn’t mean your blog posts are useless. Far from it. You don’t need to scrap everything and start over – you just need to reformat the content you already have so it works better in this new answer-first online world.

Why does this shift matter for you, as a SaaS marketer? Because your buyers are searching differently. They’re asking AI assistants for product recommendations and solutions, often skipping traditional search results altogether. If your brand isn’t showing up in those AI-generated answers, you’re missing out on high-intent traffic and letting competitors own the conversation.

In this post, I’ll show you how to repurpose your existing content into formats AI tools actually use – so you can get your brand appear in ChatGPT and other LLM answers.

Why repurpose blog content for AI assistants?

AI assistants are changing how people search for and consume information. Instead of scrolling through ten blog posts to find an answer, users now expect quick, conversational responses. Tools like ChatGPT and Perplexity pull information from across the open web, and they often pull from content you’ve already written.

But it’s not just third-party assistants. Google’s search results are evolving too. With AI Overviews now showing up for a wide range of queries, your content needs to be ready for a new kind of visibility where direct answers, not just links, get the spotlight.

.

Most blog posts are written for humans, not AI assistants. They’re not reading every paragraph and appreciating your clever analogies or smooth transitions. They’re scanning for facts, definitions, steps, stats – the useful bits.

So if your blog post opens with a vague intro, buries the point halfway down, and ends with a fluffy conclusion, chances are it’s getting skipped. Not because it’s bad, but because it’s not formatted in a way AI can easily pull from.

Most SaaS blogs are full of useful insights. You’ve probably already answered dozens of common questions in your space. It’s just trapped inside walls of text. Repurposing your blog content for AI assistants gives it a better chance of being surfaced in these new formats, both in Google and beyond.

It makes your existing content work smarter in a search world that’s shifting from links to answers.

So, if you want your brand to show up where people are actually looking for answers, it’s time to rethink how your blog content is packaged and delivered.

Dig deeper: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

What makes content “AI-ready”?

So what kind of content actually gets picked up by AI assistants? The answer is simple – the kind that’s clear, direct, and helpful. Content that goes straight to the point.

If you want your content to show up in AI-generated answers, it needs to do one thing really well: answer real questions quickly. That means no unnecessary explanation and no long-winded intros. But helpful info, structured in a way that’s easy to find and easy to use.

Here’s what that looks like in practice:

• Clear, concise answers: Think one to three sentences that actually say something. If someone asks “What is product-led growth?” give them a clean, one-sentence definition upfront.
• Structured formats: FAQs, how-tos, comparisons, and step-by-step guides are gold. They’re scannable, predictable, and easy for AI to lift directly into a response.
• Direct language: Talk like a human. Use “you” and “your” to make it personal. Drop in real examples where it makes sense. AI tools like clarity, and so do your readers.
• Useful over clever: The goal isn’t to impress, it’s to solve real problems your audience is dealing with. What do your customers keep Googling? What do they ask on sales calls? Start there.

In short: write like you’re trying to help someone, not like you’re trying to rank on Google. Ironically, that’s what makes your content more likely to show up in AI-powered results anyway.

Related: Structuring Web Pages for AI-First Indexing

How to repurpose your existing content for AI assistants

You don’t need to start from scratch. If you’ve been blogging for a while, you probably already have a ton of useful content – it just needs a bit of reshaping to work better for AI assistants.

Here’s how to do it without turning it into a huge project:

1. Audit what you’ve already got

Go through your blog and look for posts that answer real questions – the kind of questions people type into Google or ask on calls. Focus on evergreen topics – those that stay relevant over time. These are your best articles for repurposing.

Think: “How does [X] work?”, “What’s the difference between [A] and [B]?”, “Best practices for [Y],” etc.

2. Break posts into answer-sized chunks

A single 2,000-word blog post can often be turned into 10 or more high-quality snippets. Pull out each section that answers a specific question or explains a clear concept. Think of it like turning one deep-dive guide into a stack of helpful, bite-sized solutions.

3. Use Q&A formats, lists, and definitions

These are the kinds of structures AI assistants love. They’re easy to scan and understand. Reformat sections of your blog into question-and-answer pairs, bullet lists, and straightforward definitions. This structure makes it easier for AI tools to pick out the exact info they need

If a blog includes a section like “5 steps to do X,” format it as a list. If you’re explaining a term, make the definition easy to grab. Don’t bury it halfway down the page.

4. Strip the filler

Ditch the vague intros, fluffy conclusions, and anything that doesn’t directly answer the question. Keep your snippets focused and direct. If it doesn’t help the reader (or AI) get to the answer faster, cut it.

5. Add TL;DRs and takeaways

For each post, include a quick summary or a “key takeaways” section right at the top. This not only helps your human readers but also gives AI assistants a clear, concise answer to serve up.

6. Add structured data

Add schema markup – especially FAQ and HowTo schemas. You don’t need someone technical to do this. It can be done in wp plugging. This helps search engines and AI assistants understand the structure and intent of your content, making it more likely to be featured in answer boxes or AI responses.

Dig deeper: Schema & Structured Data for LLM Visibility: What Actually Helps?

7. Keep it fresh

Don’t let your content go stale. Regularly update your repurposed snippets with the latest data, stats, and examples. AI assistants prioritize up-to-date, accurate info – so keep your content fresh to stay visible

The goal isn’t to rewrite everything – just to shape what you already have into something AI tools can actually use. Think of it like taking a long-form article and turning it into a helpful cheat sheet. That’s how you get your brand in front of the right people, right when they need answers.

You might be interested in LLMs vs SEO: What’s Changing in Search Discovery

Our client success stories

Let’s talk about real results. At Quoleady, we’ve helped SaaS brands not only rank on Google but also get featured in AI-powered answers – right where your future customers are looking. 

With just a few smart tweaks, our clients are showing up in Google AI Overviews and even getting cited by large language models (LLMs) like ChatGPT in real-time responses.

Here’s what that looks like in action:

4.1 Pass Kit and Loopy Loyalty highlighted in LLM answers

We optimized blog content for clarity, direct answers, and keyword intent – and both PassKit and Loopy Loyalty ended up featured as the top two answers in an LLM response for “loyalty card software.” Just clear explanations, structured headings, and questions people actually search for.

4.2 ResponseScribe featured in Google AI Overviews

For ResponseScribe, we focused on simplifying their content and answering key questions head-on. That same blog post was later cited directly in a Google AI-generated summary – the kind that shows up before any organic results. It didn’t require a full rewrite, but better formatting and a more useful structure.

Read also: Top 6 Generative Engine Optimization (GEO) Agencies of 2025

4.3 Fuel Finance featured in Google AI Overviews and LLM answer

For Fuel Finance, we went all-in on structure: adding FAQ schema, clear data tables, and question-style subheadings. We also broke down complex ideas into bite-sized, skimmable sections. Their post now ranks for “AI forecasting tools” in Google AI Overviews and gets cited by LLMs like ChatGPT for the same keyword. All because it answers questions directly and includes structured support.

These results are what happens when your blog content is formatted for answers, not just rankings. You don’t need to overhaul your strategy; you just need to make your expertise easy for AI to find and share. That’s what we do at Quoleady – helping your brand show up everywhere answers are happening.

Tips for SaaS marketers

If you’re thinking “Do I need to start writing a totally different kind of content?” – not really. Your blog is still one of the most valuable assets you have. You just need to treat it as more than an SEO play.

Here’s how to make it work for AI-driven discovery too:

1. Don’t ditch your blog – make it your AI foundation

Your blog is where your expertise lives. It is still your best tool for showing up in AI-powered search results. AI assistants and Google’s new AI Mode are scanning for clear, structured, and up-to-date answers. Treat your blog as the foundation, not just a side project.

2. Talk to your product and support teams

Some of the best content ideas come straight from your product and support teams. They know the real questions users are asking–grab those and turn them into Q&A-style content or detailed how-tos.

Dig deeper: How Retrieval-Augmented Generation (RAG) Impacts AI Recommendations And What SaaS Marketers Should Do to Stay Ahead

3. Keep your tone human

You’re not writing a whitepaper. You’re talking to someone who wants a fast, clear answer. Keep it friendly, direct, and skip the jargon. The more your content sounds like something you’d say out loud, the better.

4. Update your old posts

You don’t need to write more – just revisit what you already have. Add clear subheadings. Summarize key points. Strip out filler. Even small changes can make a post more AI-friendly and more useful to readers.

Most of this comes down to one mindset shift: stop thinking only in keywords and rankings. Start thinking in questions and answers. That’s where AI is heading – and your blog can lead the way.

Conclusion: Your blog still works – you just need to rethink how

You don’t need to scrap your entire content or reinvent your strategy. The foundation is already there – your expertise, your insights, and your published posts. What’s needed now is a shift in how that content is structured and surfaced.

AI tools and Google’s AI Overviews are prioritizing clear, concise, and well-organized answers. To appear in those results, your content should reflect that. Focus on clarity. Eliminate filler. Answer specific questions in a way that’s easy for both humans to understand and LLM tools to parse.

Repurposing your old content makes sure the value in your content can be found in the formats LLM tools actually use today.

Ready to work on the blog changes?? Book a call with Quoleady and we’ll show you the 20% of effort that drives 80% of your results.

FAQ

What is LLM-friendly content?

LLM-friendly content is clear, structured, and easy for AI tools to understand and use in answers. It avoids fluff and gets straight to the point.

What is the best content to repurpose for AI Assistants?

Blog posts that answer real questions like how-tos, comparisons, definitions, and FAQs. These are perfect for repurposing.

How to repurpose content for LLM answers?

Break long posts into short Q&A-style sections, use clear headings, cut the filler, and format answers in a scannable way.

What are the best practices for creating LLM-friendly content?

Use direct language, structured formats (like lists and FAQs), answer real questions clearly, and keep your content up to date.

The post From Blog Posts to Answers: Repurposing for AI Assistants appeared first on Quoleady.

That means your content doesn’t always have to rank #1 in Google anymore to get seen. If an LLM picks it up and uses it in an answer you get visibility anyway.  Sometimes even more than you would from a traditional blog click.

But LLMs aren’t quoting just anything. They pull content that’s clear, genuinely useful, easy to summarize, and structured for AI-first indexing. If your content checks those boxes, you’re way more likely to get cited, mentioned, and discovered by new customers.

In this post, I’ll walk you through 10 content formats that LLMs actually like and how to use them to get your brand in ChatGPT answers. Let’s get into it.

Top content formats to get featured in LLM answers

Not all content is LLM-friendly but some formats consistently show up in AI answers. Here are a few you should definitely be using on your SaaS site if you want more visibility without fighting for the top spot in Google.

1. Original research & data-driven content

LLMs prefer fresh data. If you’ve got unique stats, benchmarks, or survey results, you’re sitting on a goldmine. If you’ve got access to your industry usage data, customer trends, or can run a quick survey, share it! Publish your findings as a blog post, a report, or even a simple chart. This kind of content gets picked up because it’s both useful and hard to find anywhere else.

Why LLMs pick it up: AI looks for facts that stand out. If your site is the source of a stat or trend, that makes you quote-worthy.

How you can do it:

• Share anonymized usage data from your own SaaS tool (example: https://www.hubspot.com/state-of-marketing)
• Run a quick survey with your user base or LinkedIn audience
• Compile industry benchmarks no one else is publishing (DataBox is great with this: https://databox.com/marketing-benchmarks-by-industry)

You don’t need a full-blown research report. Even a few solid stats in a blog post can get picked up.

2. Expert quotes & professional insights

LLMs are surprisingly good at spotting fluff – and they’ll skip right over your generic “thought leadership” if it sounds like everyone else. LLMs are looking for expert takes that are either backed by real experience or offer a contrarian (but logical) viewpoint. Plus, memorable opinions make your brand stick in people’s minds.

Why it works: LLMs prefer expert voices and citing unique perspectives, especially when they’re backed by real experience or data.

How to do it:

• Interview industry experts or customers with niche knowledge

Example of an article by Tekpon with expert insights

• Add comments from your product or content team
• Quote respected experts in your field and add your own spin

Read also: From Blog Posts to Answers: Repurposing for AI Assistants

3. Step-by-step how-to guides

LLMs prefer structure. Numbered steps, short sentences, bullet points, and clear headings make your content easy to read and easy for AI to summarize. Whether it’s onboarding, troubleshooting, or a process breakdown, actionable guides are exactly what users (and LLMs) want when they’re trying to solve a problem. A well structured format ensures the information can be easily parsed by top models and adapted for various tasks.

For example: This is how our client, Fuel Finance, restructured their information by adding tables, FAQs, and other forms of content design in their blog. As a result, they got featured in ChatGPT AI answers for the keyword “AI forecasting tools.” The second column of their redesigned table provided actual content that was fine tuned for clarity, while the first offered plain text explanations that helped AI understand content in the right context.

Why it works

LLMs are trying to help people solve problems and answer questions. Step-by-step guides, when well structured and fine tuned, give them something useful to pull from. Because AI is trained on next word prediction, content with consistent format and clear context has a higher chance of being cited.

How to do it

• Write practical how-to guides for your product
• Create onboarding walkthroughs with screenshots or short videos
• Break down complex processes into simple, numbered steps with clear section headings

Keep it skimmable, use subheadings, and avoid info dumps. The cleaner the format, the more likely it’ll show up in an AI-generated answer. For SaaS and tech companies, investing in content design that is easily parsed and paired with other forms of media will help AI systems understand content.

Related: Schema & Structured Data for LLM Visibility: What Actually Helps?

4. Comparison posts (X vs Y)

People love asking LLMs to compare tools, features, and approaches before making a decision – “X vs Y,” “Is A better than B,” “What’s the difference between X and Z.” If you’ve got clear, side-by-side breakdowns, you’re already ahead. But if your post is too salesy or biased, LLMs will skip over you. Focus more on clarity and less on hype.

Example of a comparison article from upbase.io

Why it works: LLMs prefer structured comparisons for users deciding between tools, features, or strategies. It’s a go-to format for answering buyer questions.

How to do it:

• Create honest, detailed comparison posts between your SaaS and competitors
• Break down differences in features, pricing, integrations and use cases
• Keep it neutral and useful – avoid salesy language or biased scoring tables

Being real builds trust (with readers and machines).

You might be interested in: Structuring Web Pages for AI-First Indexing

5. Q&A and FAQ pages

You don’t need a separate FAQ page. Just work questions into your blog posts, product pages, or help docs, and answer them clearly. LLMs are trained to spot and answer questions, so the more your content mirrors how people actually ask, the better your chances of being featured in AI answers.

Example: Our client, Bettermode, added user questions into the blog content naturally.

Why it works: Direct, question-based content mirrors how people talk to LLMs. That makes it super easy for AI to pull your answers.

How to do it:

• Add H2/H3 questions throughout your blog posts (and answer them right after)
• Create a FAQ section on key product or feature pages
• Use customer support tickets or sales calls to find common questions to address
• Keep answers short, clear, and to the point

It’s a simple format, but it works really well for LLM visibility.

Learn more: How to get your SaaS brand quoted by ChatGPT (and other AI Tools)

6. Case studies & success stories

LLMs prefer to cite real scenarios because it adds credibility to their answers. Even simple before-and-after examples can work wonders, as long as they’re clear, focused, and actually prove a point. Case studies give your content that real-world weight most blogs don’t have.

Example: Quoleady’s latest LLMO case study about getting their client, Legal Nodes, a 47.5% increase in meetings with top 5 AEO ranking within 3 months.

Why it works: Real-life examples build credibility and help users picture your product in action. LLMs use them to support broader points.

How to do it:

• Share customer success stories with specific outcomes
• Include data (time saved, revenue gained, churn reduced, etc.)
• Highlight before/after situations or unique product use cases
• Use clear headings and keep the story short and actionable

Just keep it concise. There’s no need to write a 2,000-word case study to understand how your product helped.

Don’t just rank on search—get found by AI assistants too. Work with Quoleady

7. Lists, tables, and summaries

If there’s one thing both readers and LLMs appreciate, it’s content that’s easy to skim. Lists, tables, and TL;DRs make your posts more digestible and easier to quote. Summaries or recap boxes at the top or bottom of your posts give LLMs easy info to grab and cite. Lists and tables break down info so it’s quick to scan, compare, and share.

Example: A comparison table with all important information about AI marketing agencies.

Why it works: Easy to parse, easy to cite. Structured info like this gives LLMs clean chunks to pull from without needing to “guess” what’s important.

How to do it:

• Use bullet points for key takeaways
• Add comparison tables when talking about features or pricing
• Drop a summary box at the top or bottom of longer posts (TL;DR-style)

This kind of structure helps people and machines get what they need.

Tools like Lovable help you create structured blog pages that resemble landing pages – they are easier to read, skim and comprehend. Here’s one of the examples of the blog structure you can build with the help of Lovable that’s structured, skimmable and easy to comprehend and pull information from: 

See also: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

8. Checklists and cheat sheets

Checklists and cheat sheets are easy to skim for LLMs. You can easily break down detailed topics. They’re practical, actionable, and get easily featured into an AI-generated answer.

Why it works: LLMs prefer short, actionable formats. Checklists are easy to understand and easy to use, which makes them perfect to be in AI responses.

How to do it:

• Create checklists for common workflows (SEO audits, onboarding, setup, etc.)
• Keep items short and clear – one action per line
• Turn blog content into printable or downloadable cheat sheets

Bonus: checklists are also great lead magnets if you want to double up on value.

9. Schema markup & structured data (JSON-LD, XML)

Producing great content is not enough, you need to format it properly. Schema markup and structured data help both LLMs and search engines “read” your content more easily. 

Why it works: Making your content machine-readable with the help of schema and structured data for LLM visibility means a higher chance of getting picked up, cited, or featured in snippets.

How to do it:

• Use schema for blog posts, FAQs, product reviews, and how-to guides
• Implement JSON-LD (easiest option) on your pages
• If you’re publishing at scale, automate it via your CMS or SEO plugin

You don’t need a dev team to get the basics in place.

Read also: Top 7 AI Marketing Agencies in 2025

10. Multimodal content (infographics, videos, audio)

Text isn’t the only thing LLMs are learning to understand. Models are getting better at picking up cues from images, audio, and video, especially when it’s paired with good metadata and transcripts.

Why it matters: Infographics, charts, and explainer videos can all show up in AI tools (and in Google’s AI Overviews). The richer your content, the more angles you can be cited from.

How to do it:

• Add descriptive alt text to images and infographics
• Include transcripts with your videos and podcast episodes
• Use visuals to support key points

Even if AI doesn’t fully get your graphic today, you’re future-proofing your content for what’s coming next.

Our client results in LLMs and Google AI Overviews

At Quoleady, we don’t just talk about LLM-friendly content — we build it. And our clients are already seeing the results show up in real AI-powered searches.

Here are a few quick wins from recent client projects:

Case study 1: Loopy Loyalty and PassKit

We recently put ChatGPT to the test with the query “loyalty cards providers.” 

The result: our clients Loopy Loyalty and PassKit showed up as the #1 and #2 answers. 

This isn’t just luck, their content was clear, structured, and packed with unique insights, so LLMs trusted it enough to feature right at the top. That’s visibility you can’t buy with paid ads or even a #1 Google ranking.

Case study 2: Bettermode

Bettermode already had solid content, but we helped them appear in LLM answers. 

We refined their structure with better headings, improved the flow, and rewrote section intros for more context. 

The result: Bettermode appeared in Google AI Overview for over 2,000 keywords. For example: 

Their traffic went from a few hundreds to over 1k impressions in 6 months.

This proves that even small tweaks like clearer headings and stronger context can make your content more attractive to Google’s generative AI.

Case study 3: Fuelfinance

FuelFinance wrote a genuinely useful article, then layered in smart formatting and schema markup to boost visibility.

They added FAQ schema, clear data tables, question-style subheadings, and broke complex ideas into bite-sized, skimmable sections. 

The result: their post now ranks for “AI forecasting tools” in Google AI Overview and gets picked up by ChatGPT for the same keyword. 

Conclusion: LLMs pick smart, structured content

If your web content is clear, supported with structured data, and optimized with consistent file formats like XML, large language models are far more likely to feature it. These language models don’t just copy text — they analyze context, process natural language, and look for formats that can be parsed quickly even at inference time.

You don’t even have to rank #1 to show up in AI-generated answers. Structured data and clean LLM-content-format give language models a foundation for better understanding and quoting your material.

Action step: Pick 2–3 of the formats above and apply them to your next blog, landing page, or even short social media posts. It doesn’t have to be a complete content overhaul — small improvements to structured data, file formats like XML, and how you present context can make a huge difference.

And if you need help creating LLM-friendly content design for your SaaS site, that’s exactly what we do at Quoleady. Let’s make your expertise impossible for large language models to ignore.

Book a strategy call today.

TL;DR: key takeaways

• LLMs (like ChatGPT) are changing how people discover content – and they don’t always pick the top-ranking result.
• To get cited in AI answers, your content needs to be clear, useful, and easy to summarize.
• Formats that work best: original research, expert quotes, how-to guides, comparisons, FAQs, case studies, lists, checklists, schema markup, and multimodal content.
• Start small: pick 2–3 formats and update your next blog or landing page.
• Need support? Quoleady helps SaaS brands create LLM-friendly content that gets seen.

FAQ section

How do LLMs decide what content to show?

Large language models (LLMs) look for content that’s clear, highly structured, and easy to quote. They favor helpful formats like step-by-step guides, FAQs, tables, or html markup. Both human readers and systems benefit when you structure information properly, using headings, lists, and consistent file formats. This improves LLM performance because the data can be easily parsed and turned into a relevant response.

Do I need to rank #1 to get picked up by ChatGPT or AI Overviews?

No. Language models don’t just pull from the top result in search engines. Instead, they evaluate what’s most helpful, well-formatted, and contextually correct. You can get cited in an answer even if you’re not ranking first. A simple format with clear headings and concise points often performs better than verbose sections.

What are the best practices for optimizing content to appear in LLM-generated answers?

• Use formats like how-tos, comparisons, FAQs, and original data
• Write clear, direct answers to common queries
• Break up your content with headings, lists, and tables
• Add structured data (like FAQ or Article schema) to help artificial intelligence systems parse your content
• Focus on clarity over keyword stuffing, because large language models (LLMs) prefer helpful over hype
• Apply prompt engineering techniques to create content that aligns with common prompts and user intent

How to write content for LLMs?

Write like you’re trying to be genuinely helpful. Use clear headings, short paragraphs, and formats that mirror file formats AI can parse, such as html tables or schemas. Think about both human readers and artificial intelligence: what’s easy for them to understand and reuse in an answer? Incorporating retrieval augmented generation practices and leveraging the right tools will improve LLM performance and make your response more reliable.

What is LLM-friendly content?

LLM-friendly content is content that’s easy for large language models (LLMs) to process and surface in AI search engines. It’s usually well structured, specific, and written in a way that mirrors how users ask queries. Using consistent file formats, schema markup, and thoughtful content design ensures that both language models and human readers get value. The point isn’t just rankings in search engines, but creating answers that are helpful in real context.

Work with Quoleady to make your SaaS brand visible in both search and AI results

The post 10 Content Formats That Get Picked Up by LLMs appeared first on Quoleady.

I’ve seen it happen to dozens of SaaS brands. You’ve got great content, a solid SEO strategy, decent traffic, but when potential customers ask AI systems for recommendations, you’re nowhere to be found.

There could be many reasons for this. One of them is that your site might not be giving AI models the structure they need to understand what you actually offer. Although schema markup won’t fix everything, it helps AI tools achieve better semantic understanding of your pages. That means your site is more likely to appear in AI summaries and deliver higher click through rates when users compare options.

Nowadays, when visibility in LLMs is becoming just as important as rankings in Google, that extra clarity can make a real difference. Leveraging schema, reviews, and even specialized tools to analyze your site’s data points will not only help AI systems recognize your authority but also improve your click through rates in both search and AI-driven results.

Do LLMs really use schema markup? (The real answer)

Let’s cut through the confusion. I’ve dug into the latest research and industry confirmations, and here’s what we know so far:

Yes, LLMs do process schema markup – and we have proof.

The biggest confirmation came in March 2025 when Fabrice Canel, Principal Product Manager at Microsoft Bing, confirmed that schema markup helps Microsoft’s LLMs understand your content during his presentation at SMX Munich. This is an official statement from Microsoft.

Microsoft uses structured data to support how its large language models (LLMs) interpret web content, specifically for Bing’s Copilot AI. That’s one of the major AI platforms officially confirming they use schema markup.

Schema markup, or structured data, indirectly influences LLM rankings. Schema markup helps search engines understand what your content is about, and it can help signal the relevance of your page to a user’s queries.

The technical reason is simple. Schema.org is structured data – a predefined, machine-readable format that search engines, Knowledge Graphs, and AI systems can use for reasoning.

What about other AI platforms?

While Microsoft has been most transparent, the research suggests other LLMs likely use schema too. A 2024 academic study on LLM4Schema.org showed that GPT-3.5 and GPT-4 can generate schema markup, which indicates these models understand schema structure well enough to create it.

The brands that are getting cited more often make it crystal clear what they do, who they serve, and why they matter – through structured data that AI systems can actually parse and understand.

What type of schema types matter most for SaaS brands?

The following schema types help give structure to your content in a way LLM bots can recognize and cite.

1. Organization schema: Your identity foundation

This is your foundation. It creates what schema experts call your “content knowledge graph”, essentially telling AI systems who you are, what you do, and how you’re connected across the web. 

It is often used on homepages or “About” pages. It helps link your brand to its name, logo, social accounts, and website. It’s basic but important.

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your SaaS Company",
  "description": "Brief description of what your SaaS does",
  "url": "https://yourcompany.com",
  "logo": "https://yourcompany.com/logo.png",
  "sameAs": [
    "https://twitter.com/yourcompany",
    "https://linkedin.com/company/yourcompany"
  ],
  "founder": {
    "@type": "Person",
    "name": "Founder Name"
  }
}

Why it works: Knowledge graphs with LLMs can significantly improve decision-making accuracy, and the Content Knowledge Graph is an excellent foundation to use schema data in LLM tools. When AI systems need to understand your company context, organization schema provides the structured data they need.

2. SoftwareApplication (or WebApplication) schema: Your product’s identity

This one’s more niche, but it fits SaaS tools well. If you want to spell out that your product is a software app – with features, platforms, ratings, etc. – this schema can help.

It overlaps a bit with Product, but can give more detail about compatibility (e.g., works on Windows, Mac, cloud, mobile), which might come in handy in certain contexts.

The WebApplication schema should be incorporated into multiple website URLs, ideally linked to the WebPage schema for marketing purposes.

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Your Product Name",
  "description": "What your product does and who it helps",
  "applicationCategory": "BusinessApplication",
  "operatingSystem": "Web-based",
  "offers": {
    "@type": "Offer",
    "price": "99",
    "priceCurrency": "USD",
    "priceValidUntil": "2025-12-31"
  }
}

Why it works: Using a schema for SaaS products must be structured in a way so it is specific to a subscription-based product. When someone asks AI about software solutions in your category, this schema helps AI understand exactly what your product does and who it serves.

Here’s how to implement it:

Where: Add it to your main product landing page (e.g. /product, /features, or even /pricing) — anywhere you describe what your software does.

How: Use the SoftwareApplication or WebApplication schema as a JSON-LD block in the page’s <head> or directly in the body. Include fields like:

name

applicationCategory (e.g. “CRM”, “Project Management Software”)

operatingSystem (e.g. “Windows”, “iOS”, “Cloud-based”)

offers (for pricing plans)

aggregateRating (if you’ve got one)

Extra tip: You can link this to your WebPage schema using the mainEntity property, so machines know this is the primary topic of the page.

Learn more: Structuring Web Pages for AI-First Indexing

3. FAQ schema: Your direct pipeline to AI answers

If you’ve got a support page, pricing breakdown, or blog post with common questions, adding FAQ schema is a no-brainer. It literally tells the LLM tools: “Here’s a question, and here’s the answer.”

LLMs prefer Q&A formats. This schema helps AI models quickly grab clear, structured answers, especially for tools like Perplexity or Google’s AI Overviews, which often surface bite-sized summaries.

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is the best CRM for small businesses?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The best CRM depends on your specific needs, but most small businesses benefit from..."
      }
    }
  ]
}

Why it works: FAQ schema creates perfect question-answer pairs that match exactly how people query AI systems. When someone asks “What’s the best CRM for small businesses?”, your FAQ content becomes the structured answer AI can pull from.

4. HowTo schema: Step-by-step authority

Got a product tutorial, setup guide, or walkthrough? Wrap it in the HowTo schema.

This one’s especially useful for help docs or blog posts that explain something step by step. If your page has a process (like “How to integrate with Zapier” or “How to invite team members”), this schema gives structure to each step, tool, and requirement.

It’s one of the few schema types where Google still shows rich results when it’s done right. Because AI models pull from well-structured how-to content as it provides clear, actionable information users are seeking.

{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to Set Up Your First Email Campaign",
  "description": "Step-by-step guide to creating effective email campaigns",
  "step": [
    {
      "@type": "HowToStep",
      "name": "Define your audience",
      "text": "Start by identifying who you want to reach..."
    }
  ]
}

Why it works: HowTo schema breaks down complex processes into digestible steps, which is exactly what AI systems need to provide helpful, actionable responses. It’s structured data that matches how people actually want to learn.

5. Article Schema: Content that gets cited

If you’re publishing thought leadership, SEO content, or product updates – this schema helps AI systems understand the authority, freshness, and context of your content and mark it as a blog post or article.

It gives metadata about the headline, publish date, author, and main topic. Tools like ChatGPT or Perplexity often quote content that looks well-structured and editorial. This schema tells them, “Hey, this is an article, not just a random page.”

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Your Article Title",
  "author": {
    "@type": "Person",
    "name": "Author Name"
  },
  "datePublished": "2025-01-15",
  "publisher": {
    "@type": "Organization",
    "name": "Your Company"
  }
}

Why it works: Article schema provides publication dates, author information, and publisher details – all signals that help AI systems assess content credibility and relevance when deciding what to cite.

You might be interested: LLMs vs SEO: What’s Changing in Search Discovery

A few lesser-known types that can help too

If you’ve already incorporated the basics, here are a few more schema types that might give your pages extra clarity:

• Review: If you’ve got customer quotes, ratings, or testimonials, this schema can help signal trust and social proof, especially on comparison pages or product pages.
• Author: Add this to blog content to clearly show who wrote the piece. LLMs often try to cite authors, so this helps give proper credit and adds credibility.
• Breadcrumb: It shows the page’s place in your site structure, like Home > Blog > SEO Tips. It helps LLM tools understand content hierarchy.
• Offer: This works well alongside Product schema. If you’re showing a free trial, discount, or pricing tier, this tells LLMs, “Hey, this is something the user can actually buy or sign up for.”
• ContactPoint: Great for your “Contact Us” or “Support” page. It tells AI tools (and search engines) how people can get in touch with your team – email, phone, chat, etc.

You don’t need all of these on every page, but if they fit the content, they’re worth using.

Optimize once, get discovered everywhere: Google, ChatGPT, and beyond. Work with Quoleady

How to add schema to high-value SaaS pages

Most SaaS brands try to implement schema on every page at once and burn out halfway through.

Don’t do that.

Instead, focus on applying Schema Markup to the most relevant and valuable content, prioritizing quality and relevance over quantity. 

Here’s exactly how to start your implementation:

Phase 1: Foundation pages

These are the pages that matter most for your brand identity and product understanding:

• Homepage (Organization schema): This is your brand’s introduction to AI
• About page (Organization + Person schema for founders): Builds authority and trust signals
• Main product/service pages (SoftwareApplication schema): For SaaS companies, integrating schema markup into their product pages is crucial for helping search engines understand their SaaS offerings

Why start here? These pages get the most traffic and represent your core business. If AI systems only understand three things about your company, these should be them.

Phase 2: Content pages

Once your foundation is set, focus on content that answers questions:

• FAQ pages (FAQ schema): A direct pipeline to AI responses
• How-to guides (HowTo schema): The step-by-step content that AI prefers to reference
• Your best blog posts (Article schema): Focus on your highest-traffic, most-cited content first

Pro tip: Don’t try to schema markup every blog post. Start with your top 10 performers and work from there.

Phase 3: Conversion pages

These pages drive business results:

• Pricing page (Offer schema): It helps AI to understand your pricing model.
• Case studies (Review schema): It’s a social proof that AI can reference.
• Contact page (ContactPoint schema): It makes you easy to find and contact.

Related: From Blog Posts to Answers: Repurposing for AI Assistants

How to actually implement this stuff

Here’s where most guides get technical and lose you. I’m going to keep it simple.

Your best option: JSON-LD

JSON-LD is Google’s recommended format. JSON-LD takes semantic data and turns it into a small piece of code that can be implemented via the <script> tag in the page head or body of an HTML webpage.

Why JSON-LD over the other options?

• It’s clean and doesn’t mess with your page design
• For JSON-LD, you’ll simply paste the code into the head section of your HTML
• It’s what Google recommends (and if Google likes it, other AI systems probably do too)
• Your developer won’t hate you for choosing it

The other options (and why you probably don’t want them)

• Microdata: Microdata requires you to manually insert specific lines of text throughout your HTML. It’s messier and harder to maintain.
• RDFa: Similar to microdata but with different syntax. Unless you have a specific reason to use it, stick with JSON-LD.

Some additional tips to simplify things

If you’re using WordPress:

• Install a plugin like Rank Math or Yoast SEO
• These tools let you add schema per page without any coding
• For FAQs and HowTos, they usually have built-in blocks or toggles

If you’re on Webflow, HubSpot, or another SaaS-friendly CMS:

• Use their custom code blocks or schema plugins
• You can paste in JSON-LD (the preferred format for structured data)
• Some platforms (like HubSpot) have native support for blog schema

If your dev team handles the site:

• Ask them to add schema in JSON-LD format to the <head> or body of the page
• Google recommends JSON-LD over microdata or RDFa
• Keep it lightweight; don’t go overboard with 10 types on one page

Pro tip: Don’t try to mark up everything. Focus on what’s relevant to the content and clearly visible on the page. Schema should reflect what’s actually there – not what you wish was there.

Read also: 10 Content Formats That Get Picked Up by LLMs

This is how your implementation plan should look like

Week 1: Audit what you have

For sites with schema already in place, first audit the existing markup, looking to identify improvements, errors to fix, and missed opportunities. Even if you think you don’t have schema, check anyway because your CMS might have added some automatically.

Week 2: Foundation First

Implement Organization schema on your homepage. This is your number one priority. Test it, make sure it works, then move to your product pages.

Week 3: Content Schema

Add FAQ schema to your FAQ pages. Start with just one page, test it, then expand.

Week 4: Validation and Monitoring

Test everything thoroughly. Set up monitoring so you know if something breaks.

The Reality Check

You don’t need to be perfect. You need to be better than your competitors who probably aren’t doing this at all. Start with the basics, test everything, and expand gradually.

Your goal is to make your content more understandable to AI systems that your potential customers are already using.

Tools to test and validate your schema

Adding schema is one thing. But making sure it actually works is where most brands slip up. 

You can implement it perfectly and still screw it up with one missing comma. Brands spend weeks implementing schema only to discover it’s been broken the whole time because they skipped the testing phase.

Don’t be that brand.

Here are a few solid tools to check if your schema is correctly set up:

1. Google’s Rich Results Test

This is Google’s official tool for testing your structured data to see which Google rich results can be generated by the structured data on your page. This shows you exactly how Google sees your schema and whether it’ll trigger rich results in search.

How to use it:

• Go to search.google.com/test/rich-results
• Enter your URL or paste your schema code
• Look for green checkmarks (good) and red errors (fix these)

Pro tip: Test both your URL and your raw code. Sometimes the URL test shows different results than code testing.

2. Schema.org Markup Validator

For generic schema validation, use the Schema Markup Validator to test all types of schema.org markup, without Google-specific validation. You can identify issues quickly and it will show you the exact part of the markup that has an issue. This one feature alone saves you tons of time.

How to use it:

• Go to validator.schema.org
• Paste your schema code or enter your URL
• Fix any errors it identifies (it’ll show you exactly where the problem is)

The difference: Google’s tool focuses on what Google can use. Schema.org’s validator checks if your markup is technically correct according to schema.org standards.

3. Google Search Console

Rich result reports in Google Search Console show structured data (and its validity) found on your site. This shows you how your schema performs in the real world, not just in testing tools.

How to use it:

• Go to Enhancements → Structured Data in your Search Console
• Look for errors and warnings
• After you fix all instances of a specific issue on your site, you can ask Google to confirm your fixes

Reality check: Sometimes you’ll see errors in Google Search Console that don’t show up in Google’s Structured Data Testing Tool or in the Schema.Org Markup Validator. This is normal, you should fix what Search Console tells you to fix.

Common mistakes (and how to avoid them)

Schema is easy to mess up, and most of the time, you won’t even notice until something stops showing up in search or tools like Perplexity ignore your page.

Here are the biggest schema mistakes I see SaaS brands make:

1. Copy-pasting generic schema without updating it

You’d be surprised how many sites have a schema that still says “Example Company” or uses placeholder FAQs. If the content in your schema doesn’t match what’s actually on the page, it’s useless and could even get flagged.

Fix it: Always double-check that your schema reflects the live page content.

2. Adding FAQ or HowTo schema to pages that don’t need it

Yes, FAQPage and HowTo schema can help with visibility, but only if your content actually includes FAQs or step-by-step instructions. Otherwise, it just looks spammy.

Fix it: If it’s not on the page, don’t mark it up.

3. Forgetting to update schema when the page changes

You tweak your pricing page or update a blog post, but forget the schema? Now you’ve got outdated or broken markup.

Fix it: Make schema updates part of your content update workflow and not an afterthought.

4. Using the wrong format (or mixing formats)

There are a few ways to add schema – JSON-LD, microdata, RDFa – but Google recommends JSON-LD. Mixing formats or using outdated methods just makes things messier.

Fix it: Stick with JSON-LD. It’s clean, flexible, and easy to debug.

5. Trying to add every type of schema on one page

More schema does not mean better results. You don’t need to jam in every type just because it exists. That usually creates clutter or worse, conflicting signals.

Fix it: Focus on 1–2 relevant schema types per page. Quality over quantity.

See also: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

Final thoughts: Schema won’t make you rank, but it can make you visible

Let’s be clear – schema markup alone won’t shoot you to the top of Google or guarantee a shoutout from ChatGPT. But it does help large language models understand what your content is about. And when AI search engines like Perplexity or Claude are scanning the web for clear, structured AI answers, that matters.

Adding schema to your high-priority pages is low effort and high impact. Start with FAQ, HowTo, Product, and Organization. Keep it clean, keyword rich, and make sure it reflects what’s actually on the page.

LLMs don’t guess, but they scan for structure. Structured data helps AI search systems and large language models understand the meaning of your website’s information. This boosts content visibility not only in traditional search results but also across AI driven platforms. Make it easy for AI search to interpret what you do, and you’ll start showing up in more AI answers that your customers are reading.

Your future customers are already asking AI search tools for recommendations in your space. If you’re not showing up, you’re losing out on organic traffic to competitors. Need help implementing schema markup for your SaaS? At Quoleady, we help B2B SaaS brands optimize their SEO strategy for both search engines and AI models.

The post Schema & Structured Data for LLM Visibility: What Actually Helps? appeared first on Quoleady.

That’s where Generative Engine Optimization (GEO) comes in.

GEO helps your brand show up directly inside AI-generated answers. Instead of optimizing only for Google SERP, you’re also thinking about how AI models read your content, understand it, and decide whether to mention your product. With the right content and digital PR in place, GEO agencies help your brand get visibility inside AI search results.

And now there’s a new layer to this: tracking it.

Most teams publish content and hope AI tools will pick it up. The problem is you never really know where your brand appears, which queries you show up for, or which competitor is dominating AI answers. That’s why many SaaS teams are starting to use LLM visibility tools – tools that monitor ChatGPT, Claude, Perplexity, and other AI systems to show:

• when your brand is mentioned
• which competitors appear more often
• which prompts trigger YOU vs THEM
• how your visibility changes after new content, PR, or GEO work

It’s basically the new version of keyword rank tracking, but for AI assistants.

For SaaS companies, this matters more than ever. Your future users might never even land on your homepage unless an AI tool mentions you first. Combining GEO services with an LLM visibility tool gives you both sides: optimization and measurement.

In this post, I’ll walk you through the top 10 generative engine optimization agencies delivering real results for B2B SaaS brands. With the right content strategy, AI optimization, and smart paid media planning, these teams are shaping how companies get discovered in the AI era. Let’s get into it.

On another note, if you want updates about LLMO, join our LLMO SaaS community on Reddit — a place where we share how GEO is evolving SEO and how teams are tracking their visibility across AI tools.

Related: From Blog Posts to Answers: Repurposing for AI Assistants

Why work with a GEO agency?

You might be thinking, “Can’t I just handle GEO myself?” Sure, you could. But here’s what you’re really signing up for:

It’s a completely different game than traditional SEO. 

While SEO focuses on keywords and backlinks, GEO makes AI engines understand your brand story, your unique value proposition, and when to recommend you. It’s less about gaming algorithms and more about creating genuinely helpful content that AI can process and cite.

The technical side gets complex fast. 

You need to understand how different AI models consume and interpret information. What works for ChatGPT might not work for Claude or Perplexity. Each platform has its own preferences for content structure, citation formats, and information hierarchy.

You’re competing against established players. 

While your competitors are still figuring out GEO, the smart ones are already working with agencies. They’re building AI visibility while you’re still trying to decode how these systems work.

Learn more: 10 Content Formats That Get Picked Up by LLMs

Time is your biggest enemy. 

As a SaaS founder, you’re already juggling product development, sales, marketing, and probably a dozen other priorities. But learning GEO while running your business can be a recipe for disaster. 

The stakes are higher than you think. 

If AI engines don’t know about your brand or misunderstand what you do, you’re invisible to a growing segment of your target market. And unlike traditional SEO where you can recover from mistakes, GEO missteps can embed incorrect information about your brand in AI training data.

A good GEO agency brings specialized knowledge, proven strategies, and the bandwidth to execute consistently. They’ve already made the mistakes, learned from them, and developed systems that work.

You might be interested in Structuring Web Pages for AI-First Indexing

Benefits of working with a GEO agency

Here’s what a good GEO agency can actually do for you:

1. Help you show up in AI answers

GEO is less about rankings, and more about mentions. A good GEO agency knows how to get your brand, product, or insights quoted in ChatGPT, Perplexity, and other LLM tools. That means more visibility, even if users never click on a search result.

2. Create content that LLMs actually use

Most blogs aren’t written with AI in mind. GEO agencies focus on content that’s structured for LLMs – answering real user questions clearly, citing your brand naturally, and aligning with how AI models process language.

3. Build topical authority (the right way)

GEO agencies help you own your niche. They focus on consistent, high-quality content around your core topics, which increases the chances that AI tools see your brand as a reliable source.

4. Save you time

GEO isn’t a quick fix. It takes strategy, testing, and a long-term content plan. A good agency handles all of that for you – research, writing, optimization, tracking – so you can focus on your product and customers.

5. Future-proof your visibility

AI search is only going to grow. Working with a GEO agency means you’re adapting now instead of playing catch-up later. It’s a way to make sure your brand doesn’t get left behind as search keeps evolving.

Read about LLMs vs SEO: What’s Changing in Search Discovery

Top GEO Agencies right now

1. Quoleady

Office location: Remote (core team based in Europe)

Year founded: 2020

Team Size: 11-50

Industries served: SaaS, B2B tech, and digital startups

Overview: 

Quoleady is a B2B SaaS SEO and AI visibility agency that helps software companies grow through organic search, digital PR, and expert-driven content marketing. The agency works with both startups and enterprise SaaS brands, helping them improve visibility across Google and AI-driven discovery platforms such as ChatGPT, Gemini, Perplexity, and Google AI Overviews.

Unlike generalist agencies, Quoleady focuses exclusively on B2B SaaS. Their team combines SEO, content marketing, digital PR, and GEO strategies to help software companies build category authority, create research-backed content, increase qualified pipeline, and drive long-term revenue growth.

Quoleady specializes in developing expert-driven content and authority-building strategies that support visibility across both traditional search engines and emerging AI search experiences, helping SaaS brands reach buyers throughout the modern discovery journey.

Key services (what they do best)

• GEO + LLM visibility: Quoleady helps your brand get featured in AI-generated answers by analyzing your website, SERP presence, LLM visibility, and competitors – then crafting a custom strategy to build true omnipresence. That means getting your tool into top-ranking listicles, pages LLMs pull information from, and making sure the technical setup is covered too: llms.txt, crawlability, and on-page optimization. On the content side, we focus on building topical authority and covering high-intent use cases people actually ask in tools like ChatGPT and Perplexity.

• Saas content strategy with GEO in mind: They build 6-month content strategies specifically for B2B SaaS brands, targeting high-intent topics and content gaps your competitors are missing. Every piece of content is designed to rank traditionally and get picked up by AI tools.
• SaaS link building that boosts AI visibility: No PBNs or shady placements here. Quoleady focuses on editorial links received via manual outreach to real SaaS blogs with real traffic. They offer performance-based pricing and guarantee placements for a full year.
• SaaS content writing optimized for both humans and AI: Every article is written by a dedicated SaaS writer using detailed briefs and SEO tools. They’ll handle editing, internal links, and can even publish straight into your CMS.
• SaaS LLM optimization: They can help your brand show up in ChatGPT, Gemini, Claude and Google’s AI Overviews, through content updates, listicles, and digital PR. They audit your LLM visibility, find missed opportunities, and optimize your website, content and digital presence.

You can find the full service list over at Quoleady.com

Client results (Real GEO wins)

Quoleady helps B2B SaaS brands to show up in AI-generated answers and Google’s AI Overviews. Check out how their clients appeared in LLMs with their strategy:

Their LLM wins:

• Loopy Loyalty: Ranked #1 on ChatGPT for “digital loyalty card solutions”
• PassKit: Ranked #2 on ChatGPT for “digital loyalty card solutions”
• Expandi: Ranked #4 on ChatGPT for “LinkedIn automation tools”
• GoRout: Ranked #3 on ChatGPT for “softball pitching devices”

Read about their LLMO case study about how they helped Legal Nodes get +47.5% increase in booked meetings along with top 5 AEO rankings within 3 months.

Google AI Overviews (as of July, 2025):

• ShoutAboutUs: Featured for 691 keywords in AI Overviews
• BetterMode: Featured for over 2,000 keywords in AI Overviews
• FuelFinance: Featured for 942 keywords in AI Overviews
• OneFlow: Featured for over 7,000 keywords in AI Overviews
• GoRout: Featured for over 2,000 keywords in AI Overviews
• PassKit: Featured for 1,000 keywords in AI Overviews
• Loopy Loyalty: Featured for 648 keywords in AI Overviews

They set up Google Analytics to track traffic from LLMs and add an llms.txt file to guide AI engines on what to crawl — all as part of the LLMO package.

If you want a GEO-savvy team that actually understands SaaS, Quoleady is the one to call.

2. Avenue Z

Office location: Miami, FL, United States (with offices in New York, NY and Orlando, FL)

Year founded: 2023

Team Size: 51-100

Industries served: Fintech, financial services, banking, regulated industries, technology brands

Overview:

Avenue Z is a marketing and communications agency specializing in SEO, AI search visibility, and authority-building for financial services and regulated industries. The agency helps fintech, banking, and financial brands improve discoverability across both traditional search engines and AI-powered platforms such as ChatGPT, Gemini, and Perplexity.

Their approach combines SEO, AI optimization, PR, and entity authority-building to strengthen brand credibility and visibility in high-trust, regulation-heavy markets. Avenue Z focuses on aligning content, brand signals, and off-site authority so AI systems consistently surface their clients as reliable sources.

Key services

• SEO & Technical SEO for Financial Services: Delivers compliant, scalable SEO strategies tailored to financial and regulated industries, with a focus on technical foundations and long-term authority.
• AI Search Optimization (GEO / AEO): Optimizes content and brand signals to improve inclusion in AI-generated answers, summaries, and citations across LLM-powered platforms.
• Content Strategy & Editorial SEO: Develops structured content strategies designed for both search intent and AI readability, including educational content and thought leadership.
• Entity & Authority Optimization: Strengthens brand entities across the web through structured data, authoritative mentions, and consistent topic association.PR & Digital Authority Building: Uses PR, earned media, and off-site signals to increase trust, credibility, and AI-recognized authority.
• Analytics, Measurement & AI Share of Voice Tracking: Tracks performance across traditional SEO metrics and emerging AI visibility indicators.

Client results:

• Fintech brand: Improved visibility across AI-powered search platforms and increased inclusion in LLM-generated financial topic summaries
• Financial services platform: Strengthened entity authority, resulting in improved rankings and citations in AI-generated answers
• Regulated technology company: Built a comprehensive GEO strategy that increased trust signals and consistent brand mentions across AI search results

3. NoGood

Office location: Headquartered in New York City, with hubs in Miami, LA, and Dubai

Year founded: Not specified

Team Size: 51-100

Industries served: SaaS, healthtech, fintech, and more

Overview:

NoGood is a performance marketing agency that has expanded into GEO through what they call Answer Engine Optimization (AEO). Their focus is on helping brands show up in AI-generated answers across tools like ChatGPT, Perplexity, and Gemini – especially now that more users are relying on AI chat instead of traditional search.

Their AEO service focuses on structuring your content so it’s easy for LLMs to understand and reference, increasing the number of times your brand gets cited in AI responses, and tracking how and where your content shows up across AI tools. This also involves cleaning up your existing content to align with what AI engines look for – clarity, direct answers, and structured data.

Key services

• Answer Engine Optimization (AEO): NoGood structures web content for LLM visibility, optimizes brand mentions, and formats answers so they’re more likely to appear in tools like Perplexity and Claude.
• Full-funnel growth marketing: NoGood has its performance roots in offering services in paid media, CRO, and lifecycle marketing alongside content strategy.
• Content optimization with revamp: They’ll audit your existing content and repurpose it for better performance in AI-generated answers, focusing on readability, information density, and question-answer formatting.

Client results:

• Inflection AI: Helped them drive over 1 million daily active users within 3 months
• Invisibly: Increased app installs by 103% MoM for a Founders Fund startup
• JVN Hair: Drove 298% revenue increase in Q1 2022 compared to Q4 2021
• SteelSeries: Increased overall net sales by 35% in Q3 2023 through paid channel initiatives

Related: Schema & Structured Data for LLM Visibility: What Actually Helps?

4. AIclicks

Office location: Lithuania, Vilnius (Europe)

Year founded: 2025

Team Size: 11-50

Industries served: B2B, Ecom, SaaS, Fintech

Overview:

AIclicks is a GEO agency that helps brands get cited inside AI answers across ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews, and other major models. The team built their own AI search visibility platform, AIclicks.io, and uses it daily on client accounts, so clients work with operators who run GEO at scale.

Their approach is execution-heavy. They start by auditing where a brand shows up across 6+ AI models and which prompts competitors win. From there, they fix the source coverage gap by earning brand mentions on high-authority sites, placing the brand in top-ranking Reddit threads, and creating content engineered for LLM citation. Monthly reporting tracks visibility, mentions, share of voice, and attributed traffic.

Key services:

• LLM Visibility Action Plan. Maps how every major AI model sees a brand today, where competitors win, and which prompts buyers search.
• Reddit Engagement Action Plan. A step-by-step plan covering Reddit, review sites, forums, directories, and social platforms that LLMs cite most.
• Growth Channel Engagement. Finds the highest-traffic Reddit threads in a niche and places brands in top-ranking comments.
• Brand Mention Building. Earns placements on high-authority publications and data sources that AI models trust and pull from.
• Content Creation. Articles engineered for citation: structured, authoritative, formatted for how LLMs extract information.
• LLM Technical Audit. Identifies what blocks ChatGPT and other engines from crawling, parsing, and citing a site.visibility gaps, not raw data.

Client results:

• Tinggly (ecommerce). ChatGPT share of voice grew from 15.1% to 24.5% in 6 months, a 63% jump. Moved from 3rd to 1st in their category.
• Global hospitality brand (NDA). 75% organic growth, 20% revenue lift, and 12K added traffic in 6 months.
• AIclicks (own brand, 90 days). Referring domains grew from 18 to 150. Top 5 visibility across 300 tracked prompts. Cited in 251 of 328 prompts. 11.65% share of voice. 50K organic visits per month and 3,000+ LLM mentions traced back to Reddit activity.

5. Omniscient

Office location: Austin, TX, United States

Year founded: 2019

Team Size: 11-50

Industries served: SaaS, B2B software, technology, and digital-first brands

Overview:

Omniscient is an organic growth agency that helps marketing leaders at B2B software companies turn SEO and content into growth channels. They’ve added GEO services to their traditional SEO offerings, focusing specifically on B2B SaaS brands.

They take a research-heavy approach, identifying where your brand already appears (or doesn’t) in tools like ChatGPT and Perplexity, then build a plan to improve your presence through better content targeting and entity optimization.

Key services:

• GEO strategy & execution: They build content clusters around topics LLMs already generate answers for, then optimize articles to boost your brand’s visibility in those responses.
• Content creation for SaaS: Their team produces blog posts, thought leadership, and landing pages with a strong focus on search intent, structure, and AI-readability.
• Entity SEO + authority building: Omniscient works on building your brand’s presence across the web – so LLMs start to associate your domain with specific topics and expertise areas.

Client results:

• EdTech company: Increased blog traffic by 87% and helped them rank in Perplexity and ChatGPT for multiple category terms
• B2B SaaS platform: Grew monthly organic leads by 220% and improved citations in LLM outputs
• Productivity tool: Built a full GEO content strategy that led to consistent mentions in AI summaries

6. Skale

Location: London, UK

Founded: 2020

Team size: 51-200

Industries: SaaS, B2B, Technology

Overview:

Skale is an organic growth agency helping SaaS and B2B companies scale through SEO, content, and Generative Engine Optimization. Their focus is on improving visibility across Google and AI-generated search results while supporting real business growth.

They approach GEO as a natural extension of SEO, combining topic clustering, entity optimization, and technical execution to help brands get cited by LLMs. Skale prioritizes revenue impact, aligning organic growth with signups, pipeline, and demand.

Key services:

• GEO optimization: Improving brand visibility and citations in AI-driven search and LLM-generated responses.
• SEO strategy: Revenue-focused SEO programs tailored to SaaS and B2B growth models.
• Content marketing: Search- and LLM-friendly content built to strengthen topical authority.

Client results:

• SaaS client (Rezi): Increased overall organic visibility nearly 9×, driving a significant uplift in key acquisition metrics.
• B2B SaaS brand (Maze): Grew organic sign-up momentum by 3× within months through targeted content authority building.
• Tech platform (Holded): Expanded organic acquisition scale with 1,370% more signup signals and strong growth across revenue-linked acquisition.

7. RevenueZen

Location: Portland, OR

Founded: 2017

Team Size: ~30

Industries: B2B SaaS, fintech, healthcare, professional services

Overview:

RevenueZen is a B2B growth agency that started with a focus on SEO, LinkedIn thought leadership, and content marketing. Now, they’re diving into Generative Engine Optimization to help clients stay relevant online.

Their GEO service focuses on helping brands show up in AI-generated responses through a mix of strategic content creation, entity optimization, and on-site SEO. They approach GEO with a blend of brand storytelling and technical precision – aiming to make sure your content is both LLM-friendly and human-readable.

Key services:

• GEO strategy + execution: RevenueZen builds out topic clusters, schema markup, and branded content that’s formatted for LLMs to understand and surface. They also audit existing content to improve entity association.
• Thought leadership + personal branding: They help founders and execs grow visibility on LinkedIn – which can indirectly improve brand mentions and credibility in AI-generated content.
• B2B content + SEO: Blog writing, SEO audits, and full content strategy development designed to build long-term authority for niche B2B markets.

Client results:

• Fintech startup: 9x increase in organic traffic within six months
• B2B SaaS platform: 220% lift in qualified leads from organic content
• IT services company: Gained multiple AI citations in Perplexity for branded and unbranded search queries

Learn more: Structuring Web Pages for AI-First Indexing

8. FirstPageSage

Location: San Francisco, CA
Founded: 2009
Team Size: 50–100
Industries: SaaS, finance, cybersecurity, healthcare

Overview:

FirstPageSage is an established agency in the SEO space, and they’ve now added Generative Engine Optimization as a key part of their offering. Their approach leans heavily on long-term strategy like building topical authority and brand credibility so that AI tools begin to reference your content naturally.

They focus on publishing “thought leadership SEO” – content that ranks well and gets cited by LLMs due to its depth and subject matter expertise. Their GEO strategy includes technical optimization, structured data, and entity-level targeting to help brands show up in ChatGPT, Gemini, and beyond.

Key services:

• Long-term GEO strategy: FirstPageSage creates full editorial calendars designed around topic clusters, branded keywords, and information LLMs pull into their answers.
• Content production at scale: Their team delivers regular content, usually 1–4 articles per week, to build momentum across organic and AI search.
• Technical SEO + entity optimization: Schema, internal linking, and entity association are a core part of their GEO playbook.

Client results:

• SaaS platform: 38% increase in AI-related visibility across Perplexity and Google’s AI Overviews
• Cybersecurity firm: Grew lead gen by 152% through consistent, expert-driven content
• Healthcare tech company: Increased referring domains by 200+ and built domain authority that led to multiple AI answer placements

You might be interested: How to get your SaaS brand quoted by ChatGPT (and other AI Tools)

9. The Ad Firm

Location: Carlsbad, CA
Founded: 2009
Team Size: ~20
Industries: Local services, home improvement, legal, B2B

Overview:

The Ad Firm is a digital marketing agency that focuses heavily on technical SEO, paid media, and CRO. Their Generative Engine Optimization approach is grounded in technical updates and structured content. They focus on making websites easier for AI tools to crawl, understand, and reference, while also cleaning up outdated content that might confuse LLMs.

Compared to other agencies on this list, The Ad Firm leans more into the technical and on-site SEO side of GEO, making sure your site is optimized from a code and structure level.

Key services:

• Website structuring for LLMs: The Ad Firm restructures site architecture, metadata, and schema to make content more digestible for AI tools.
• Local GEO strategy: They help regional brands appear in AI answers by combining traditional local SEO tactics with new generative-friendly formatting.
• Content cleanup + optimization: They revise old or thin content to match how LLMs prioritize clarity, expertise, and citations.

Client results:

• B2B services firm: Increased organic leads by 75% after restructuring service pages for LLM visibility
• Legal client: Saw consistent mentions in AI tools like ChatGPT and Bing Chat following GEO-related on-site updates
• Home services company: 3x increase in quote requests after content and schema changes

10. Growth Marketing Pro

Location: San Francisco, CA
Founded: 2017
Team Size: ~20
Industries: B2B SaaS, ecommerce, real estate, healthcare, nonprofit, and more

Overview:

Growth Marketing Pro (GMP) is a full-service GEO agency on the cutting edge of AI search, offering breakthrough strategies like Reddit organic marketing, Wikipedia, third-party brand mentions, traditional SEO, link building, technical on-site LLM optimization and more. Clients are seeing results in weeks, in the form of improved LLM visibility, visibility on the Google SERP, and increases in traffic, leads and revenue from AI referral sources.

Unlike traditional GEO agencies, GMP’s senior-led model ensures every engagement is run by marketers who have scaled million-dollar SaaS brands, tripled inbound leads, and managed six-figure daily ad spend. Their work connects across the full funnel, from high-intent organic content to conversion-optimized paid campaigns, driving qualified leads, customers, and pipeline growth you can measure.

Key services:

• GEO / AEO + SEO: Increase visibility everywhere people search – on ChatGPT, Google AI overviews, Perplexity and the Google SERP with high authority on-site and off-site content & link building to start showing up in AI answers.
• Reddit organic, Wikipedia + Third Party Mentions: These are the most commonly LLM cited sources. Utilize off-site optimization strategies to make sure that your brand is showing up where it matters across the internet and forums – especially with organic Reddit posts that rank on Google and influence LLMs. 
• Paid search & social: Improve Google page one visibility with PPC advertising, and amplify across paid social channels with creative campaigns and remarketing.

Client results:

• Cube Software: Grew organic traffic from 0 to 56,000+ visitors/month, achieved #1 rankings for core FP&A terms in Google and ChatGPT, and generated 0 to 150+ leads/month from AI platforms through Reddit visibility.
• SmartRecruiters: Increased MQLs by 37× and decreased CPA by 89% through Google and LinkedIn ads optimization.
• Noteworthy VC Firm: Ranked #1 for brand coverage, mentions, and share of voice in related LLM searches, with their new Wikipedia page becoming the #2 most cited VC firm on ChatGPT. 

Comparison table

Know more about How Retrieval-Augmented Generation (RAG) Impacts AI Recommendations And What SaaS Marketers Should Do to Stay Ahead

How to choose the best GEO agency for your brand

Not every GEO agency will be the right fit for your business. The key is knowing what you actually need from the agency. Traditional SEO focuses on keywords and rankings in traditional search engines, but a modern approach must include AI-powered search platforms and generative search engines.

Here’s what to look for:

1. They understand how LLMs actually work

If an agency can’t explain how ChatGPT or Google’s AI Overviews choose sources, that’s a red flag. You want a team that knows how language models crawl, filter, and cite content – not just someone repackaging old SEO tactics with a new label.

Ask:
Can you show examples of clients you’ve helped appear in AI-generated search results or LLM answers? Can they track AI search visibility over time with log file analysis or other tools?

Related: LLMs vs SEO: What’s Changing in Search Discovery

2. They go beyond SEO and understand the difference between the two

SEO strategy is mostly about keywords, ranking in the SERPs, link building, and appearing on the first page sage of traditional search engines. On the other hand, GEO strategy revolves around creating content that is structured for AI answers. This includes structured data implementation, schema, FAQs, entity tagging, and building authoritative links.

Look for an agency that can integrate flow SEO techniques from a flow agency perspective and optimize content for AI-powered search platforms while still leveraging proven traditional SEO focuses.

3. They can show real results

Don’t just take their word for it. Look for proof that their clients are actually being cited in ChatGPT, Gemini, Perplexity, or AI Overviews. Bonus points if they can help you track that visibility over time using tools, custom reporting, or paid search campaigns. Agencies that combine first page sage, link building, and AI strategies can demonstrate measurable outcomes.

Learn more: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

Final thoughts

GEO is already reshaping how people discover and trust information. If your brand isn’t showing up in tools like ChatGPT, Gemini, or AI Overviews, you’re already falling behind.

Choosing the right GEO agency can save you months of trial and error. Whether you need a full content strategy, help with structured data implementation, or want to see your brand cited by LLMs, agencies that integrate flow SEO with traditional search engines techniques bring measurable results.

For B2B SaaS companies seeking real proof of helping clients rank in AI-generated search results, Quoleady is a smart choice. They focus on getting your brand mentioned, cited, and found where it matters most now, combining link building, paid search, first page sage, and generative search engines strategies for maximum impact.

Book a call with Quoleady today.

The post Top 10 Generative Engine Optimization (GEO) Agencies of 2026 appeared first on Quoleady.

For years, finding SaaS tools meant googling “Best Industry tool” or “Best SaaS alternatives,” scanning listicles, and checking review platforms like G2 or Capterra.

Now discovery works differently. Instead of sorting through multiple Google results, many users turn directly to Large Language Models (LLMs) like ChatGPT, Gemini, Claude, and Perplexity. You type a user’s question, and instantly get an answer.

But the search results themselves have also changed. Before 2024, Google’s top spots went mostly to blog posts and review sites. Today, with Google confirming that the algorithm favors UGC platforms, Reddit dominates — especially for high-intent SaaS queries. This has made Reddit SEO an unexpected factor in brand visibility and even brand sentiment.

Are LLMs Learning From Reddit’s Content?

This raises important questions:

• Are LLMs sourcing Reddit threads, top-ranking blog posts, and other trusted sources from their training data?
• If Reddit mentions are everywhere in search engines, do they shape LLM responses?
• Can SaaS companies improve their Reddit presence to appear in AI answers?

We tested it.

You might be interested: Structuring Web Pages for AI-First Indexing

The Experiment: Reddit vs. LLMs

We chose dozens of high-intent SaaS “alternatives” keywords where Reddit ranks in Google’s top five.

Examples:

• “Trello alternatives”
• “Zapier alternatives”
• “Slack alternatives”

From each, we analyzed reddit discussions in relevant subreddits and pulled the tools mentioned in reddit posts. This gave us a dataset of which tools niche communities recommend.

We then asked four leading AI models — ChatGPT, Claude, Perplexity, and Gemini — for their recommendations. Each model produced unique lists.

Finally, we compared them to see:

• Which tools from Reddit threads appeared in LLM responses
• Which ones were absent
• How much Reddit SEO overlaps with LLM optimization

The Results: Overlap, Misses, and Surprises

Reddit–LLM Overlap (%):

Perplexity showed the strongest influence from Reddit’s content, followed closely by Gemini.

Related: 10 Content Formats That Get Picked Up by LLMs

Tools everyone mentions

Highly visible tools like Notion, Trello, Slack, Airtable, and ClickUp appeared in both Reddit threads and AI-generated answers. Their constant brand mentions and positive brand sentiment keep them in relevant discussions across platforms.

Tools Reddit loves but LLMs ignore

Some tools dominate Reddit discussions but never appear in AI-generated responses: Nimbus Note, Missive, RemNote, MirrorFly, Campsite, Jandi, Breakroom. These cases show how brand visibility on Reddit doesn’t always influence LLMs directly — especially if posts are buried or lack links Google can access.

Lesser-known wins

Tools like Chanty, Missive, Zulip, and Twist — though niche — appeared in both Reddit mentions and LLM responses, suggesting influence LLMs pick up on relevant discussions when brand accounts and community activity are strong.

Read also: From Blog Posts to Answers: Repurposing for AI Assistants

Why Reddit matters for LLM optimization

Reddit ranks in Google’s top 5 for 76% of high-intent SaaS searches. That visibility means AI engines and search engines likely pull context from Reddit’s content during training data collection or AI generated search results processing.

This makes Reddit an indirect but powerful factor in LLM optimization. Being active in relevant subreddits, answering users’ questions with helpful answers, and building brand accounts that share quality insights can boost brand sentiment and influence how ai models respond.

What SaaS brands should do

To appear in both google results and AI-generated answers:

1. Engage in Reddit SEO — Participate in high-ranking reddit threads with authentic, non-promotional content.
2. Track brand sentiment — Monitor reddit mentions and brand mentions to understand community perception.
3. Use multiple platforms — Don’t rely solely on Reddit; other platforms still shape LLM responses.
4. Create valuable content — Ai generated answers often favor sources that offer helpful answers with strong context.

Learn more: Do G2 and Capterra Reviews Influence ChatGPT Rankings? [2025 Research]

The Bottom Line

Reddit plays a growing role in shaping AI answers, but success means balancing search visibility, brand mentions, and LLM optimization.

If your company wants to influence LLMs, you need to build brand visibility across forums, platforms, and conversations. By combining Reddit SEO, consistent brand accounts engagement, and strategic LLM optimization, you can increase your brand presence in both human and AI-generated search results.

Book a strategy call today.

The post Does Reddit influence LLMs responses? (The 2025 Research) appeared first on Quoleady.

This shift is often called AI-first indexing because AI models are starting to decide which content shows up first in answers. If your site isn’t ready for this, you’re missing out on a lot of potential traffic and visibility.

Traditional SEO had a lot to do with keywords, backlinks, and meta tags. This still matters, but it’s not enough. Today, you also need to make sure your content makes sense to machines trained to understand human language. This means clear structure, plain language, and actually answering the questions your future customers are asking.

But don’t panic! In this post, I’ll break down exactly what’s changed, why it matters for your SaaS site, how to get your SaaS brand in ChatGPT answers and–most importantly–how you can structure your web pages to make AI work for you, not against you.

Here are some simple steps you can take right now to get your pages ready for the future of search.

What is AI-first indexing, really?

AI-first indexing is Google’s, Bing’s, and other platforms’ way of deciding what gets seen, ranked, or totally ignored, using artificial intelligence. Instead of crawling and indexing every page on the web, search engines now let their AI do a pre-check.

Before your SaaS page even gets a shot, the AI asks: “Is this content actually valuable? Does it answer real user questions? Does it connect to known topics or entities?” If the answer is no, your page might never make it into the search results, no matter how slick your product is. That’s where LLM SEO and LLM optimization come in.

Think about Google’s Search Generative Experience (SGE) or Bing’s integration with GPT. These aren’t giving links to the users but they’re summarizing content, pulling answers from multiple pages, and generating overviews right in the search results. If your content isn’t clear, well-structured, and easy to quote or summarize, it’s going to get skipped over.

Here’s where it gets interesting: AI-first indexing isn’t just “mobile-first” 2.0. With mobile-first, the focus was on how your site looked and loaded on smaller screens. With AI-first, it’s more about how well your content communicates. AI tools need structured, plain-language content that actually says something useful – and they’re trained to detect fluff, keyword stuffing, and vague copy.

Read also: How to get your SaaS brand quoted by ChatGPT (and other AI Tools)

What this means for your SaaS site

If your homepage, product pages, and blog content are written like sales decks or stuffed with jargon, AI tools are going to struggle. That means fewer mentions in AI answers, less visibility in generative search, and fewer chances to get in front of buyers early.

You need to structure your pages so AI can instantly see what you’re about, how your content connects to real-world concepts (like your product category or pain points you solve), and why your answer is better than the rest.

In short: AI-first indexing forces you to build pages that are both human-friendly and machine-readable, or you’ll get left behind.

Next, let’s break down how they actually read your pages – and what they look for.

Why your page structure matters more than ever

So far, we know that AI doesn’t “read” your website like a human does.

It doesn’t get distracted by pretty graphics or clever taglines. It’s not scrolling through your landing page, admiring your clever copy, or filling out your demo form.

Instead, it’s scanning for structure, context, and relationships between ideas. If that’s missing, your SaaS landing page is just a wall of text or a jumble of features. Your page gets ignored, plain and simple.

Related: From Blog Posts to Answers: Repurposing for AI Assistants

It’s not just about keywords anymore

In old-school SEO, you had to hit all the right keywords in the right spots. And yeah, that still helps – but it’s not enough. Today’s AI models look at the context around those keywords. They’re asking things like:

• What is this page really about?
• Does it clearly explain a product, feature, or solution?
• Is it connected to other useful, related content?

That means you need to give them more than just a list of features and a slick tagline.

Semantic search is here – and it’s not going away

AI doesn’t just match phrases anymore – it understands entities (like your company, your product, your niche) and how they relate to each other. This is called semantic search, and it’s how tools like ChatGPT or Google’s SGE pull answers.

If your content is vague, jargony, or buried in weird design choices, it gets skipped. If your structure is clear and easy to parse, it becomes usable – and quotable.

Good vs. bad content structure for AI indexing of SaaS pages

Let’s say you run a B2B SaaS product that automates invoice processing.

I’ll take two quick examples to explain my point:

Bad structure

One long, unbroken page with no subheadings, random feature lists, and no clear explanation of what your SaaS actually does. As a result, AI gets confused, misses key info, and your page gets skipped.

Bad structure example

• Hero headline: “Empowering the future of finance”
• Body copy: A wall of buzzwords and generic benefits
• No clear subheadings or explainer sections
• CTA buried in a dropdown or carousel

AI looks at that and goes: “No clue what this does. Moving on.”

Good structure

Clear H1 for your main topic (“All-in-One CRM for SaaS Startups”), H2s for each feature or pain point, bullet lists for benefits, and a FAQ section at the end. As a result, AI can easily scan, extract, and rank the most relevant sections, even if a user’s query is super specific.

Good structure example

• Headline: “Automated invoice processing for finance teams”
• Subhead: “Cut manual work by 80% and get paid faster”
• Clear sections: Features, use cases, integrations, FAQs
• Internal links to related blog posts or guides

Now the AI (and your visitors) know exactly what you offer, who it’s for, and how it helps. That’s the goal.

Learn more: LLMs vs SEO: What’s Changing in Search Discovery

What are the core elements of an AI-friendly web page?

You don’t need to know technical SEO or a fancy plugin to make your web pages work for AI-first indexing. But you do need to get a few basics right – consistently. Here’s what actually matters for AI indexing and semantic search.

a. Clear, logical headings

Headings aren’t just for breaking up walls of text. They’re the roadmap AI uses to figure out what your page is about, how each section connects, and which web pages to pair with matching search queries. When you use them right, they help AI models and search engines understand what your page is about and how the content flows.

Use H1 for the main idea – usually your page title. Then, use H2s for sections, and H3s for supporting points under each section. This also helps search engine users quickly scan mobile and desktop versions of your site.

Example (SaaS landing page):

H1: “All-in-one payroll software for remote teams”

H2: “Automate global payments”

H2: “Stay compliant in 100+ countries”

H2: “Why finance teams love our product”

H3: “Save time on admin”

H3: “Reduce payment errors”

This kind of layout makes your content easier to scan on both mobile and desktop versions, and easier for AI to pull into answers.

Don’t stop at search rankings — get discovered by AI assistants too. Book a call with Quoleady.

b. Concise, context-rich content

This part’s simple but hard: write clearly. Your content should be easy for real people to read, but also packed with enough context so AI models and search engines understand the topic. Ditch the fluff and get straight to the point.

Machines don’t love fluff. They want direct, useful information. If you can answer a question in two sentences, do that. Think less sales pitch, more helpful explainer. This helps with things like featured snippets and Google’s SGE, where short, specific answers from matching web pages often get pulled in directly.

Bad: “Our platform revolutionizes team synergy to drive operational efficiency.”

Good: “Our tool helps remote teams share files, track tasks, and manage deadlines in one place.”

One sounds like marketing speak. The other sounds like something AI (and real search engine users) can actually use.

c. Schema markup and structured data

Schema markup is a way to give search engines extra info about your content. It’s not visible on the page, but it helps AI understand what’s what. Structured data also signals to semantic search engines how your pages should be indexed.

You don’t need to mark up everything. Just hit the essentials:

• FAQ schema → For your support or product Q&A sections
• How To schema → For guides, tutorials, onboarding help
• Product schema → To highlight pricing, features, availability
• Review schema → If you’re showcasing customer ratings or testimonials

Most CMS tools or SEO plugins make adding structured data pretty easy. It’s low effort, high impact, and helps search engines understand the purpose of your web pages.

d. Internal linking that makes sense

AI follows your links to understand how your site fits together. Proper internal linking ensures that your mobile version and desktop version of the site is consistent, helping search engines map your content and match search queries to the right web pages.

For instance, your blog talks about “automated expense reporting,” and your product has a feature page for that – link to it. If you’ve written a guide on compliance workflows, link it from your landing page’s “security” section.

Pro tip: Don’t overdo it. Just link where it makes sense, and always use anchor text that’s clear (not “click here”). Correct canonical URL structures and URLs also prevent duplicate content issues and improve page speed, which is critical for mobile device users and mobile first indexing. Avoid keyword stuffing; keep links relevant and context-rich.

Adding these fundamentals helps search engines understand your content, boosts ai indexing, and ensures your mobile version, structured data, and internal linking work together so AI models can better surface your content in semantic search results.

Related: Does Reddit influence LLMs responses? (The 2025 Research)

Practical steps to restructure your SaaS pages

Okay – so now you know why structure matters and what AI is looking for. But what if your current pages… aren’t exactly AI-friendly?

You don’t need to start from scratch. A few small changes to your page structure, meta tags, and internal linking can make a big difference for both human visitors and AI models. Let’s walk through how to check your site and clean things up – without a full redesign.

Simple audit: Is your page AI-friendly? (Checklist)

• Does your hero section clearly explain what your product does in one sentence?
• Are your headings (H1, H2, H3) logical and descriptive, not just “Features” or “Learn More”?
• Can a visitor (or AI) understand your core value proposition within 10 seconds?
• Do you have clear sections for features, use cases, pricing, and proof (like testimonials)?
• Are your CTAs obvious and relevant to the page content?
• Is your internal linking helping users and search engines discover relevant pages like features, pricing, and blog posts?
• Are you using schema markup for FAQs, products, reviews, or how-to guides?
• Is your copy free of jargon and fluff, focusing on what your user actually cares about?

If you answered “no” to any of these, don’t panic. That’s normal. Let’s fix it.

Easy fixes you can do today

You don’t need a developer or a full content overhaul. Here are a few small moves that add up:

• Rewrite your H1 so it clearly says what your product does.
  Not: “Smarter finance workflows.” → Try: “Automated invoicing software for B2B startups”
• Add subheadings to break up your sections. Large blocks of text can confuse AI and human visitors alike.
• Add 1–2 internal links to related pages. This helps AI understand your link structure and helps humans find what they need.
• Turn feature lists into scannable bullets. Avoid long paragraphs – short, clear points improve mobile usability and cumulative layout shift.
• Answer 2–3 FAQs right on the page. Use FAQ schema markup to improve featured snippets and AI-generated results.

Tools to help (free and paid)

• ChatGPT (Free or Plus): Paste your page copy and ask, “What’s this about?” If AI can’t explain it clearly, your page structure may need adjustment.
• Ahrefs or SEMrush (Paid): Track which search queries you rank for and which relevant pages are performing.
• Screaming Frog SEO Spider (Free up to 500 URLs): Spot missing H1s, messy meta tags, or bloated pages fast.
• Google Search Console (Free): Check organic search impressions vs. clicks and identify gaps.
• Merkle Schema Markup Generator (Free): Add structured data like FAQ, HowTo, and product markup easily.

Start with these basics: clear structure, logical flow, and helpful links. The easier it is for both AI and human visitors to understand your SaaS, the better your technical SEO results will be.

Fixing your homepage and product pages for AI

Your homepage and product pages aren’t just digital brochures anymore. In an AI-first world, they’re often the first thing search engines and AI models see. That means they need to do more than look nic they need to communicate clearly.

Homepage: Keep it simple, specific, and skimmable

Checklist for an AI-friendly homepage:

• Clear H1 stating what your product is and who it’s for
• Short intro explaining the problem you solve
• Key benefits in bullets (avoid fluff)
• Internal links to your main feature pages and blog content
• Clear CTA (yes, “Book a demo” still works)

Example:
Instead of: “The Future of Team Collaboration Starts Here”
Try: “Project management software for remote design teams”

Product pages: Structure like a conversation

Each product page should answer three questions:

1. What is this? (Feature overview in plain language)
2. Why does it matter? (Benefits, not just specs)
3. How does it work? (Screenshots, short videos, or step-by-step guidance)

Pro tip: Use headings that sound like real questions:

• “How does automated invoicing work?”
• “What tools do finance teams get?”
• “Is it easy to integrate with Xero?”

Add internal links to related features or use cases so both AI and humans can explore your product naturally. This helps your AI-first indexing, improves organic search, and builds topical authority.

Dig deeper: GEO vs SEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

Real-world examples: SaaS pages that nail AI-first structure

It’s one thing to talk about AI-first indexing best practices, but what does it look like in practice? Here are two SaaS brands we’ve worked with at Quoleadythat got real results from structuring their content the right way – for both human visitors and AI models.

Bettermode: Clean headings, clean rankings

Page: Discussion Forums for Online Communities

Bettermode already had good content, but what we helped them do was refine the page structure for better search engines understand signals. That meant fixing up their headings, making sure each section had a clear purpose, and using H2s and H3s to guide readers (and artificial intelligence systems) through the page.

What we did:

• Restructured headings for clarity and flow
• Rewrote section intros for stronger context and semantic meanings
• Optimized internal linking to related product and blog pages for stronger link structure and better SEO strategy

Result: The brand appeared in Google’s search results and Google AI Overview for the keyword “discussion forum examples.” The clean, logical layout made it easier for AI models and people to understand the site’s content, and search engines understand rewarded it in organic search.

Takeaway: Your heading structure isn’t just about looks. When it reflects what the page actually covers, AI gets it, and so do your users. This is exactly what AI-first optimization is about – making your website’s mobile usability, desktop versions, and mobile version equally strong so search queries lead to relevant pages.

Fuelfinance: Smart use of structure + schema

Page: 8 Best AI Forecasting Tools in 2025 (Ranked & Compared)

FuelFinance took things a step further. They wrote a strong, helpful article and then layered in schema markup and strategically structured data to make the content even easier to parse for crawlable AI during the crawling process.

What they did:

• Added structured data: FAQ schema markup + clear data tables with canonical URL structures URLs
• Used question-style subheadings to mimic user search queries and improve chances for featured snippets
• Broke complex ideas into bite-sized, skimmable sections for human visitors and AI answers

Result: The post ranks for AI forecasting tools in Google AI Overview and gets featured in LLM (AI generated results) for the same keyword. This happened because it answers questions directly and includes strategically structured data.

Takeaway: If you’re already writing helpful content, adding schema markup, improving URL structure, and tightening internal linking is like giving your content a megaphone — for AI summaries and for results page visibility.

Read also: Top 6 Generative Engine Optimization (GEO) Agencies of 2025

Final thoughts: Your content has to make sense to LLMs and people

LLM vs SEO isn’t about picking sides. The best brands are combining both. If you want your SaaS content to show up in AI generated results, it has to make sense to large language models (AI models) and real people.

You’re not writing for robots. You’re writing for real people – AI is just the middle layer now. If your content is vague, bloated, or full of keyword stuffing, it gets ignored. But if it’s well-structured, skimmable, and genuinely helpful, it performs across mobile device and desktop versions, gets picked up in organic search, and may even appear in featured snippets.

Think like a helpful teacher, not a keyword machine. Break things into chunks. Answer real questions. Link related pages so search engines understand your topic depth. Keep both mobile first indexing and technical SEO in mind – first indexing is already here.

Read also: Schema & Structured Data for LLM Visibility: What Actually Helps?

TL;DR – Key takeaways

• AI-first indexing is real – tools like Google SGE and Bing are rewriting how search engines understand content.
• Clear structure > clever copy – use proper H1, H2, H3 tags for page structure.
• Write like a human, for a machine — answer real search queries in natural language, avoid keyword stuffing.
• Schema markup helps AI get it – add FAQ, HowTo, Product, or Review schema where it makes sense.
• Internal linking matters – helps AI understand your site’s content and link structure.
• Start with a simple checking audit crawl reports – see if canonical tags are correct and mobile friendly site performance is good.
• Fix your homepage and product pages first – ensure they have clear meta tags, strong URL structure, and fast page speed.
• Being helpful is the whole game – if your content is useful, AI will notice and show it in AI answers.

FAQs about AI-first indexing

What is AI-first SEO?

It’s about structuring content so AI models can parse it, understand the semantic meanings, and surface it in AI summaries. It’s different from mobile first indexing, which focused on the mobile version of your site.

How is it different from mobile-first indexing?

Mobile first indexing prioritized mobile device and mobile friendly site performance; AI first focuses on content clarity and technical SEO.

Is there a checklist to follow for optimizing my site for AI bots?

Yes – start with internal linking, schema markup, meta tags, same URL for mobile version and desktop versions, and avoid keyword stuffing.

Do I need to change my whole website?

No – but clean page structure, correct canonical tags, and URL structure help search engines understand your site’s content.

What’s the most important thing to fix first?

Your homepage and product pages – make them fast (page speed, content delivery network), usable on mobile device, and clear for both human visitors and AI models.

Turn your B2B SaaS content into a magnet for both algorithms and customers. Work with Quoleady

The post Structuring Web Pages for AI-First Indexing appeared first on Quoleady.

And if you’re in B2B SaaS which is likely to reach USD 315.68 billion in 2025, you can’t afford to ignore it. Because if someone asks ChatGPT for the “best CRM for startups” or “alternatives to HubSpot,” and your brand isn’t mentioned? You’ve missed your shot.

At Quoleady, we’ve helped B2B SaaS brands get found not just on Google, but also in the answers AI assistants give to users every day. We’ve researched every credible source, read every LinkedIn thread on LLM optimization, and tested dozens of strategies across client sites to see what actually works.

This article brings all of that together – a practical guide based on real data, experiments, and results. And the good news is, you don’t need to throw out your whole content strategy. 

In this post, I’ll walk you through how to get your SaaS content featured in tools like ChatGPT – not just by tweaking your content, but by rethinking where you publish, how you build authority, and what makes your content worth quoting in the first place. Let’s get into it.

How do LLMs (like ChatGPT) find and use content?

Many people think ChatGPT works like Google but it doesn’t. It doesn’t crawl your website or fetch new blog posts in real time. Instead, it was trained on a snapshot of public content that was available at the time – like blogs, articles, Wikipedia, and forums.

That said, some tools and versions (like ChatGPT with browsing, Perplexity, or Microsoft Copilot) can access fresh information. They use browsing tools or APIs (like Bing Search) to pull in recent web content and answer questions more accurately. But this depends on the specific tool and whether browsing is enabled.

So how does your content actually get used in an AI-generated answer?

It all comes down to how “memorable” your content is. That doesn’t mean witty or quirky – it means clear, factual, and cited. LLMs are trained to look for patterns in trustworthy information. They tend to favor these things in your content:

• Clarity: Short, focused paragraphs, clear headlines, and logical structure make it easy for LLMs to understand and quote your content
• Factual and well-cited: LLMs love content that’s backed by data, references, and expert opinions
• Widely referenced: When your content or brand is mentioned across trusted blogs, media, and community threads, LLMs are more likely to pick it up. The more you’re talked about online, the more authority you gain.

That’s why some websites get quoted constantly, and others don’t even make the cut. If your content is well-structured, has a number of links from great domains and sounds like a human wrote it (not a jargon robot), it’s got a better shot at showing up.

Bottom line: your goal is to show up in the places LLMs already trust. 

That includes high-authority websites commonly used in training data – like Wikipedia, Stack Overflow, major news outlets, and well-established blogs. It also means industry publications, niche SaaS blogs that are publicly accessible and frequently cited, and content hubs like Reddit, Quora, Medium, and GitHub. 

Many of these are also indexed by Bing, which powers live results for tools like Bing AI and some versions of ChatGPT.

Dig deeper: How Retrieval-Augmented Generation (RAG) Impacts AI Recommendations And What SaaS Marketers Should Do to Stay Ahead

Want to be Quoted by AI? Start with better SEO

The 2025 B2B SaaS SEO Performance Report analyzed 300 B2B SaaS websites and over 15,000 data points to figure out what’s actually working right now.

Top-performing brands aren’t just publishing more content or chasing trends. They focus on high-impact content – like alternatives, comparison pages (e.g. Slack vs Microsoft Teams), product-led use case articles, template libraries, and helpful tools like calculators. These are the pieces that earn traffic, links, and conversions.

They also make data-driven SEO decisions: prioritizing topics based on search intent, traffic potential, and conversion value – not just copying what competitors are doing.

In short, it’s not about how much you publish – it’s about publishing the right content in the right format, backed by strategy and quality.

And here’s what’s interesting: the same SaaS companies succeeding in search are also the ones showing up in AI-generated responses. They’re not doing anything radically different – they’re just getting the fundamentals right.

That’s exactly the approach we took with GoRout. We targeted lead-gen keywords their ideal buyers were actually searching for, structured the content with clear headings and direct answers and got them cited on great relevant platforms. As a result, their organic clicks doubled in just six months:

But it wasn’t just SEO success – their content also started showing up in LLM-generated answers, including a #3 ChatGPT ranking for “softball pitching devices”:

The future of SEO is not about just ranking on page one, but being the source that LLMs choose to quote. If you want your SaaS brand to stay visible, it’s time to think beyond Google and start optimizing for the new answer engines.

Related: LLMs vs SEO: What’s Changing in Search Discovery

5 steps to get your content into LLM answers

If you want your B2B SaaS brand’s name (and content) to actually show up in ChatGPT or Perplexity answers, you need to do more than just write a blog post and hope for the best. Here’s what you can do:

1. Stop recycling content that’s already available online

LLMs prefer original and trustworthy information. If your content is just repurposing the same stuff everyone else is saying, it’s forgettable. Also, why would ChatGPT choose your answer if it’s exactly the same as hundreds of others? When we consult clients on LLMs visibility, we always recommend including fresh data, expert insights, or anything first-hand and experience-based in their content: 

Think: What kind of content are LLMs more likely to pick up?

Insights from your product

Share unique data based on how people use your tool. This kind of content is hard to replicate and adds real value.
Example: Semrush published how often their brand appears in AI Overviews.

Survey results from your users

Run surveys and publish what you learn. LLMs are more likely to surface fresh insights that reflect real user behavior.
Example: A “State of SaaS SEO” report based on 300 customer responses.

Quotes or input from experts and founders

Including expert commentary adds credibility – something LLMs tend to favor when selecting content to reference.
Example: A founder roundup on how teams are adapting to AI search.

Templates, calculators, and generators

Tools that solve real problems and offer hands-on value are more likely to be quoted or recommended.
Example: Dashboards examples and reporting tables by Coupler.io

Real-world product use cases

Show how your product works with numbers and outcomes. LLMs often surface this kind of content when answering tool-specific questions.
Example: “How Our CRM Helped Reduce Churn by 23%.”

Mini-tools or benchmarks

Small, helpful tools or up-to-date industry benchmarks are frequently referenced – especially when no one else is publishing them.
Example: “2025 SaaS Pricing Benchmarks” or “AI Content Score Checker.”

Helpful visuals or decision trees

Well-structured visuals make complex ideas easier to understand – for both users and AI.
Example: A flowchart for picking the right customer onboarding strategy.

Open datasets or live dashboards

If your data is public, original, and regularly updated, it’s more likely to be referenced by others – including LLMs.
Example: A live dashboard tracking AI tool adoption across industries.

Foundational explainers on emerging topics

LLMs often reference clear, structured content that defines or explains new concepts.
Example: “What Is GEO?” explained in plain language.

Takeaway: LLMs are more likely to use original content because it improves the quality of their answers. Say something new – not just what everyone else is saying.

Read also: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

2. Publish in the right places

Even the best content won’t get picked up if it’s sitting on an island. LLMs learn from public, high-authority websites – content that’s accessible, well-linked, and easy to find. They typically ignore gated PDFs, hidden pages, or private Slack threads.

So while your company blog is important, also think about:

• Guest posts on trusted industry blogs to have strong authority
• Mentions in popular SaaS newsletters like SaaS Hill, MKT1, etc. to reach thousands of marketers and founders 
• Listing your SaaS on review websites like G2, Capterra and GetApp (builds credibility)
• Contributing to Wikipedia pages, Reddit threads to get scraped for real-world opinions and product mentions
• Getting mentioned in roundup posts, listicles and product comparisons (these formats are easy for AIs to summarize and generate answers)
• Use social media like LinkedIn and Twitter (X) to show thought leadership and share industry reports

The more visible, trusted, and widely distributed your content is, the more likely it is to be picked up by LLMs – and the more discoverable your brand will be, everywhere your buyers are searching.

We recently analyzed dozens of high-intent keywords like “Slack alternatives” and “Intercom alternatives” and found that Reddit ranks in the top 5 on Google for 76% of them.

Zapier with their tools listicles is also showing up on Google top 5 quite often (53% of all high intent “alternatives” searches). If your brand shows up on Zapier, Reddit, or other top-ranking pages, there’s a good chance it’s also showing up in LLM answers.

My advice: Get into as many SaaS tool listicles in Google top as possible be it Reddit, Zapier or even your competitor. It will help your SEO and, most certainly help your brand appear on LLMs. 

Here’s an example of how LLMs pick up information from Reddit. Once this post was published:

ChatGpt started showing Vexa.ai a SaaS brand in the results:

Another high intent keyword study we’ve done around Reddit influence shows that Reddit-listed tools appear in all four LLMs (ChatGPT, Perplexity, Gemini, Claude), with overlap percentages ranging from 16.67% to 39.71%. This suggests that:

– LLMs are likely using Reddit either as part of their training data or indirectly through other indexed content.

– Perplexity (39.71%) and Gemini (36.27%) show the strongest Reddit-tool alignment, suggesting they might rely more on up-to-date or user-generated data sources.

If you’re not active on Reddit yet, it’s time to start. Jump into threads that are relevant to your business and genuinely engage – it’s one of the most underrated growth channels right now (and ways to get into LLMs answers as well).

Check out our latest research on whether Reddit influences LLM results.

3. Make your content easy to find and use

LLMs aren’t skimming your blog like a human. They’re trained to scan patterns, structure, and clarity – so give them clear ones to show up in AI answers.

Use:

3.1 Descriptive headlines

• Good:
  How to Measure SaaS Churn Rate (With Formula & Examples)
  (Clear, keyword-rich, tells the reader exactly what to expect)
• Bad:
  Churn Rate
  (Too vague – doesn’t set expectations or match search intent)

3.2 Add questions in headings

LLMs and users often search in question form. If your headings don’t match those questions, you’re missing out.

• Good:
  “How Much Does SaaS Customer Support Software Cost in 2024?”
  (Matches common LLM/user queries like “cost of SaaS support tools” or “SaaS pricing 2024.”)
• Bad:
  “Pricing Overview”
  (Too generic – doesn’t align with how people actually ask about pricing.)

Dig deeper: Structuring Web Pages for AI-First Indexing

3.3 Plain language (ditch the jargon)

• Good:
  “SaaS onboarding is the process of helping new users get value from your software as quickly as possible.”
  (Simple, direct, no buzzwords)
• Bad:
  “Our onboarding paradigm leverages synergistic enablement touchpoints to accelerate user time-to-value.”
  (Full of business jargon – confusing for both readers and LLMs)

3.4 Bullet points, numbered lists, and bolded takeaways

• Good:
  “Key benefits of SaaS onboarding:
  • Faster time-to-value for users
  • Higher product adoption rates
  • Reduced churn”
    (Easy to scan, each point is clear and concise)
• Bad:
  “SaaS onboarding offers many benefits including the fact that it can help users realize value faster, it can increase product adoption, and it may also help with churn reduction.”
  (Long, dense sentences – harder for both humans and AIs to extract key points)

3.5 Direct, answer-style writing

• Good:
  Q: What is SaaS onboarding?
  A: SaaS onboarding is the process of guiding new users to successfully adopt and use your software.
  (Directly answers the question – ideal for LLMs and featured snippets)
• Bad:
  “When it comes to onboarding in the SaaS world, there are many things to consider, and the process can look different depending on your product and audience…”
  (Indirect, buries the answer in fluff and context)

3.6 Add long-tail keywords

Long-tail keywords are specific, niche phrases that users (and LLMs) ask about.

• Good:
  Target “best SaaS tools for remote team collaboration” instead of just “SaaS tools.”
• Bad:
  Stuffing generic keywords like “SaaS software solutions” repeatedly.
  (LLMs ignore this – it’s vague and doesn’t answer specific questions.)

3.7 Use Perplexity and ChatGPT suggestions for keyword research

LLMs can tell you what people are asking.

• Good:
  Type “SaaS pricing models” into ChatGPT and see its dropdown suggestions:
  • “SaaS pricing models explained”
  • “SaaS pricing model that are trending in 2024”
  • “SaaS pricing models that work best for startups”
    Use these exact phrases as subheadings or FAQs.
• Bad:
  Guessing keywords based on intuition alone.
  (You’ll miss the actual phrases LLMs are trained to prioritize.)

3.8 Structure landing pages for LLM prompts

Anticipate how users will ask about your product and structure your page to answer those prompts directly.

• Good:
  A landing page titled “Best CRM for Small SaaS Teams” includes:
  • “What features do small SaaS teams need in a CRM?”
  • “How much does CRM software cost for small teams?”
  • “Top 5 CRM tools for SaaS startups (2024)”
    (LLMs can easily extract these answers for queries like “CRM for small SaaS teams.”)
• Bad:
  A generic landing page titled “CRM Software” with vague sections like “Why Choose Us” and “Features.”
  (No clear answers for specific LLM/user prompts.)

Pro Tip:
Run your headlines and subheadings through ChatGPT or Perplexity. Ask, “What questions would someone have about [topic]?” If your headings don’t match the answers, rewrite them.

Don’t forget about tech on-site optimization: use schema markup (like FAQ page or article) and keep your site crawlable by search bots. A clean sitemap, fast-loading pages, and mobile-friendly design all help your content get indexed and understood by both search engines and AI models.

4. Backlinks (and mentions) are important

Backlinks aren’t just for Google anymore. LLMs pay attention too. If your content is getting mentioned, linked to, or shared by trusted sites, it’s more likely to show up in AI answers. Even if there’s no link (just a mention of your brand) it still helps.

But not all links are created equal. As Tamara Novitovic points out in her recent LinkedIn post, the time of spammy guest posts and shady link swaps is over. LLMs (and Google) are getting smarter at spotting genuine authority versus manufactured hype.

So what actually works now?

4.1 Earning mentions in niche publications

Try to get mentioned in reputable industry blogs, SaaS news sites, or useful resource lists. These are often referenced by both readers and LLMs when looking for reliable information.

4.2 Collaborating with partners on co-branded content

Partner with other SaaS companies or experts to create webinars, reports, or guides. When both brands promote it, you get more visibility and high-quality links. 

4.3 Share expert insights and original ideas

Publish content that stands out (unique data, a fresh take, or real expertise). These are the kinds of assets that get picked up organically by journalists, bloggers, and influencers.

4.4 Keep showing up with helpful content

You don’t build authority in a day. The brands that win are the ones consistently publishing content that’s genuinely useful and worth mentioning. It builds slowly, but it compounds. And LLMs notice.

5. Keep it fresh and relevant

LLMs prioritize up-to-date information, just like Google does – especially in the SaaS industry.

So revisit your top pages regularly:

• Update stats and screenshots
• Refresh product comparisons
• Add new FAQs based on customer feedback or search trends

Content updates are important both for SEO and getting your content featured on LLMs. The changes you might get from a simple content update are impressive. When we update clients’ content on the blog, here’s the change we usually see:

Monitor what LLMs are already quoting in your niche. If you spot outdated info or gaps, that’s your opportunity to publish something better and more current. Brands that stay on top of trends and keep their content updated are the ones that get referenced most often and get much better positions in Google search.

Need help getting featured in LLMs? Check out these top 6 AI marketing agencies in 2025

Technical SEO for LLMs: don’t let your content be invisible

It’s not just about what you write – it’s also about how your content is delivered. LLMs (and Google) might miss your content if it’s hidden behind JavaScript, blocked by robots.txt, or lost in a complicated site structure.

Make some small tech tweaks to get a huge difference in whether your content gets picked up by LLMs.

1. Add LLMs.txt files to your website

The llms.txt file is a simple text file you place in the root directory of your website. It helps large language models (LLMs) like ChatGPT, Gemini, or Claude better understand and process your content. It points them to your most important pages, highlights key info, and tells models what you want them to use.

While robots.txt tells search engines what not to crawl, llms.txt does the opposite: it shows AI what matters most and makes your content easier to pick up and use.

What to do:

• Create a plain text file named llms.txt and place it in your website’s root directory (e.g., yourdomain.com/llms.txt)
• List your most important pages (product docs, blog posts, case studies, API references, etc.) with short, clear descriptions. Example:

• Use markdown formatting so that LLMs can better extract context from it.
• Refresh the file as you publish new content or update existing resources.
• Don’t include private or sensitive URLs. Only list public, crawlable pages you want AI models to use.
• Use tools or plugins (like Website LLMs, Markdowner, or FireCrawl) to generate and validate your llms.txt file

2. LLMs and JavaScript-rendered content

LLMs (and even many search engines) struggle with content that’s only visible after JavaScript loads. If your key information, blog posts, or product details are hidden behind scripts or dynamic loading, there’s a good chance LLMs won’t ever “see” them.

What to do:

• Make sure your core content is server-side rendered or available in the raw HTML.
• Test your pages with “View Source” or use tools like Google’s Rich Results Test to see what’s actually visible to crawlers and LLMs.

3. Blocked by robots.txt or meta tags

If your robots.txt or meta tags (like noindex) are blocking important pages, LLMs won’t be able to access or train on them.

What to do:

• Double-check your robots.txt and meta tags to ensure your best content is open for crawling.
• Avoid blocking pages you want to appear in AI answers.

4. Crawlability and sitemaps

LLMs use the same public web data as search engines. If your sitemap is up to date and your site is easy to crawl, you increase your chances of being included in LLM training datasets.

What to do:

• Keep your XML sitemap current and submit it to Google Search Console.
• Ensure all important pages are linked internally and not orphaned.

5. Structured data (schema markup)

Adding schema markup (like FAQPage, Article, or Product) helps both search engines and LLMs understand your content’s structure and intent.

What to do:

• Use Schema.org markup for FAQs, articles, products, and reviews.
• Test your markup with Google’s Rich Results Test.

Common LLMs optimization mistakes SaaS brands make (and how to avoid them)

Most B2B SaaS brands are still playing catch-up when it comes to AI-driven search and optimizing for LLMs. Here are the classic mistakes we see again and again (and how you can avoid them):

1. Over-optimizing for bots whether it’s Google or AI

It’s easy to get caught up chasing a perfect SurferSEO score or stuffing your keyword into every other sentence. But Google’s said it before, and with AI tools now in the mix, it matters even more: write for people, not bots.

Over-optimized content doesn’t just turn off real readers, it also makes it harder for LLMs to understand and use your content.

How to fix it: It’s been said a million times, but with all the AI-generated content flooding the web, it’s worth repeating: write like a human, for humans. LLMs prefer clear, helpful, easy-to-scan content so don’t feed them keyword soup you’ve put together in 20 minutes.

2. Hiding content behind logins or paywalls

If your best insights are locked away in gated PDFs, login-only dashboards, or hidden behind a newsletter form, LLMs can’t use them. Public, crawlable content is what gets used in AI answers.

But the idea of giving away your best insights for free can feel risky. You might wonder, “How do I make money or build my funnel if I keep my most valuable content publicly available?”

How to fix it: You don’t have to give away everything, but make sure your most authoritative, high-value content is at least partially accessible and crawlable. 

Offer deep insights, frameworks, or actionable tips up front, and then invite readers to go deeper (with templates, tools, or bonus material) behind a soft gate if you need to. This way, you build trust, grow your brand’s visibility in AI answers, and still have opportunities to capture leads and nurture your funnel. Try to strike the right balance between discoverability and conversion.

3. Using jargon and buzzwords 

SaaS content is already full of tech jargons – don’t make your content harder to understand.  Nobody likes reading a wall of tech-speak, and LLMs are no exception. Overly complex language, acronyms, and fluffy buzzwords make your content harder to parse and less likely to be quoted.

How to fix it: Use plain language, short sentences, and direct answers. Instead of “streamlining workflows with scalable automation,” just say “helping teams save time with automated tasks.”

4. Not tracking where your content is being cited

You’d be surprised how often ChatGPT or Perplexity are already quoting your content – and you’d never know unless you check. Most brands don’t even realize they’re showing up in AI answers (or worse, not showing up when they should).

One of our clients was panicking after their organic traffic went down. Luckily, we discovered where it went – the same % of traffic they lost is now coming from LLMs! 

How to fix it: Start monitoring where your content is being mentioned, linked, or pulled into AI tools. You can search for brand mentions in tools like Perplexity, check referral traffic in GA4, or peek at server logs to spot AI-related hits. Check out our “How to create LLM referral report in GA4” guide. 

5. Writing content with AI (the right and wrong way)

Using AI to help you write isn’t a problem because whether you spend 30 min writing an article or 8 hours, what matters is the end result. AI writing tools can be a huge time-saver and a great way to share your ideas. 

The trouble starts when AI becomes your only source of ideas and you’re just publishing a recycled version of what’s already out there. If your content is just a remix of existing articles with no fresh perspective, unique data, or real expertise, it won’t stand out to people or LLMs.

How to fix it: Use AI as a tool, not a crutch. Start with your own insights, experiences, or research, and let AI help with structure, editing, or brainstorming. The best-performing content is usually the one where you’ve invested real thought, added something new, and made it genuinely helpful for your audience.

How to check if your content is already in LLMs (and what to do next)

Before you invest more time rewriting your content, do a quick check – your brand might already be showing up in AI answers. you don’t need fancy tools or insider access to the model’s training data to get a sense of your visibility. Here’s how you can check:

• Ask the AI directly: Just type your main topic or question into ChatGPT, Perplexity, or Gemini and see what sources they mention or quote. If your brand or unique data shows up, congrats – you’re in the game. 
• Try different prompts: Use variations of your target questions, product names, or research titles. Sometimes LLMs surface different sources depending on how the question is phrased.
• Look for direct citations: Some AIs will mention your brand by name, while others paraphrase your content. Both are wins, but direct mentions are gold.

For example, we recently asked ChatGPT about “loyalty card software” and our clients showed up as the #1 and #2 answers proving that their content was not only indexed but trusted enough to be featured.

If you’re not showing up yet, don’t panic. Here’s what to do next:

• Audit your content: Is it public, easy to read, and linked to from high-authority sites? If not, start there.
• Get feedback: Ask the AI what sources it’s using and see what those pages have in common. Is it structure, clarity, authority, or freshness?
• Iterate and improve: Update your content, target new distribution channels, and keep testing. LLMs update their knowledge over time, so persistence pays off.

Tracking your progress is key. Don’t forget to set up analytics for your website to monitor referral traffic from LLMs, so you’ll know exactly when your content starts getting picked up and where those AI-powered visitors are coming from.

Conclusion: You don’t need to “optimize for AI” – you need to be useful (and omnipresent)

TL;DR: how to show up in LLM answers:

• LLMs are changing how people find information. If your SaaS brand isn’t showing up in ChatGPT, Perplexity, or Gemini – you’re missing a major discovery channel.
  
• Getting featured isn’t about tricking AI. It’s about creating content that’s original, well-structured, and widely referenced across public, high-authority sources.
  
• You don’t need to start over. Just adapt your SEO strategy to focus on clarity, accessibility, and distribution beyond your blog – think Reddit, tool listicles, expert quotes, and content that’s genuinely useful.
  
• We’ve tested it. The brands getting quoted in LLMs are doing the basics right – and doing them consistently.

To sum everything up, I’ll say that you’re not going to outsmart an LLM with keyword tricks. And you don’t need to. This isn’t about gaming the system. It’s about making your brand visible and your content valuable that AI tools want to quote you.

LLM visibility is the new layer on top of SEO. If your SaaS content is clear, original, and your brand appears in the right places, you’ll show up in answers where it matters most.

Ready to get your content featured on LLMs? Quoleady helps B2B SaaS brands get discovered by both humans and AI. Let’s make your expertise impossible to ignore.

Book a strategy call today.

The post How to get your SaaS brand quoted by ChatGPT (and other AI Tools) appeared first on Quoleady.

You’ve probably noticed it. AI systems are getting better at suggesting what content to write, what products to show, or which leads to chase. Everything feels a bit more personal.

But most marketers have no idea why that’s happening. It’s not just better algorithms or more training data quality—it’s something called RAG systems, short for retrieval augmented generation.

The name sounds like something out of a research paper, but the concept is both simple and game-changing if you want to understand how artificial intelligence works behind the scenes and how your content fits into it.

What is retrieval-augmented generation (RAG), in plain English?

Most large language models work like this: they’ve read a massive amount of content during training, and when you ask them something, they predict the best answer based on what they remember. The problem? They don’t actually know if what they’re saying is still accurate—or even real.

That’s where RAG models come in.

When someone asks a question, RAG systems first retrieve relevant information from leveraging external data sources—such as your internal docs, medical literature, or clinical guidelines—to ensure the answer contains up to date information. Then, the system blends this retrieved data with its existing knowledge to create contextually relevant responses.

Read also: From Blog Posts to Answers: Repurposing for AI Assistants

How RAG works step-by-step

1. You ask a question.
2. The system searches databases, knowledge bases, or APIs for the most relevant information.
3. It pulls in that retrieved data and uses it to generate a fact-checked answer.

How this differs from regular search

Traditional search just gives you a list of links. RAG models extract and process the content behind those links, then weave it into a coherent, useful reply.

Why this matters for marketers, researchers, and content teams

Your content can directly influence AI systems’ recommendations—not just search rankings. In RAG workflows, evaluation metrics typically include retrieval accuracy, data freshness, and overall quality. That means your documentation, product pages, or whitepapers can become part of an AI system’s decision-making engine.

For example, in healthcare, RAG systems can pull from medical literature and clinical guidelines to ensure that doctors receive relevant information grounded in evidence. In marketing, large language models using RAG can recommend strategies based on your latest content instead of outdated insights.

How RAG improves recommendations

One of the biggest issues in artificial intelligence is “hallucination,” where models generate confident but wrong answers. RAG systems reduce this risk by pulling in up to date information before responding, improving both reliability and trust.

That means the recommendations you get are:

• More accurate
• More relevant
• More transparent

You might be interested in: 10 Content Formats That Get Picked Up by LLMs

Measuring RAG’s effectiveness

In RAG-based AI development, performance is measured using metrics such as retrieval accuracy, data completeness, and the quality of contextually relevant responses. These benchmarks ensure that RAG models not only work in theory but actually help users make better decisions.

Whether you’re working with large language models in marketing or using RAG for medical research, RAG systems give AI systems the ability to retrieve relevant information from trusted sources and provide fact-checked answers. By combining stored knowledge with fresh up to date information, they bridge the gap between static training and real-world, dynamic content—delivering results that users can trust.

RAG vs. traditional recommendation engines

Related: Schema & Structured Data for LLM Visibility: What Actually Helps?

Where B2B SaaS marketers might see RAG in action

RAG isn’t just some abstract tech. You’ll start noticing it in the AI models and platforms you already use. Here’s where it shows up in real life:

• AI writing tools: Instead of recommending generic blog topics, they suggest ideas based on your content and competitor analysis—often by incorporating real time data from external databases and domain specific knowledge. That means fewer bland listicles and more ideas that fit your niche.
• Chatbots: With real time data retrieval, bots don’t just copy-paste vague answers. They pull retrieved data from your docs, specs, or clinical guidelines, generating factual accuracy users can trust.
• SEO platforms: RAG can suggest keyword clusters and internal linking ideas by combining structured data, competitor trends, and training data from your own site.
• SaaS onboarding flows: Feature recommendations feel more personal because generative models match real time data with underlying data from usage patterns and help guides.

And here’s where it gets interesting for SEO folks: when search engines or generative models start surfacing your content, RAG decides what’s worth showing. If your content is clear, domain specific knowledge-rich, and easy to reference, you stand a better chance of being in those AI-generated answers.

Read: Does Reddit influence LLMs responses? (The 2025 Research)

The SEO angle: What RAG means for online visibility

Here’s the new reality for SEO: generative models powered by RAG systems serve answers from structured data and retrieved data, not just whatever ranks on page one. To make it into the “retrieval pool,” your content must be optimized for real time data retrieval and underlying data clarity.

That means:

• Structured content: Headings, logical flow, and labeled sections for AI development RAG parsing.
• Straightforward copy: Backed by scientific literature or credible sources to improve factual accuracy.
• Smart internal linking: Improves retrieval accuracy and helps AI connect related generated responses.

These elements feed into evaluation metrics that AI systems use to measure answer quality—ranging from human evaluation to automated scoring of generated responses.

Why RAG improves results

One key advantage of RAG is how it merges training data with leveraging external data sources. For instance, a healthcare assistant can combine clinical guidelines and scientific literature with patient-specific inputs to generate contextually relevant recommendations. In marketing, RAG can pull relevant structured data and usage patterns to create generated responses adapted to the moment.

This blend ensures:

• More factual accuracy in generated responses
• Stronger retrieval accuracy from trusted external databases
• Answers shaped by evaluation metrics and domain specific knowledge rather than just generic training data

Related: Structuring Web Pages for AI-First Indexing

Should you change how you create content?

You don’t need to throw away your entire strategy, but with artificial intelligence moving toward generative models and real time data integration, it’s time to make your pages easier for AI to scan and reference.

That means:

• Use structured data wherever possible.
• Clearly present underlying data and citations from scientific literature or industry sources.
• Write concise, quote-worthy sections that an AI could lift into generated responses without losing context.

The future of visibility isn’t just ranking in search engines—it’s being part of generative models’ answers. And that’s where evaluation metrics meet quality content.

Want expert help getting your SaaS content picked up by LLMs? Here are the best AI marketing agencies for SaaS brands

Closing thoughts

RAG is more than just a tech buzzword. It’s a real upgrade in how RAG-powered tools pull and present information by using a retrieval component and a generation process. For marketers, it’s a clear sign that traditional generative models are being replaced by RAG-based systems with stronger context awareness and better generation capabilities. This shift is part of a rapidly changing AI landscape, where contextual relevance and response accuracy matter as much as speed.

For SEO folks, this means one thing: double down on quality content that answers user queries with accurate information and accurate responses. Forget tricks or shortcuts. You’re no longer just writing for search engines—you’re creating resources for AI systems that use dense vector retrieval, a semantic search method that finds information by meaning rather than exact keywords.

At Quoleady, we help B2B SaaS brands produce content that AI can find in their knowledge bases, cite as external knowledge, and deliver in AI responses that improve user satisfaction.

FAQs about Retrieval-Augmented Generation (RAG) and AI recommendations

What’s the difference between RAG and regular AI tools?

Regular AI tools (like a basic chatbot) generate output solely from static datasets and training data, often missing context awareness. RAG, on the other hand, uses a retrieval process to pull external knowledge from a knowledge base or domain specific knowledge bases before starting its generation processes. This improves contextual relevance, improves response accuracy, and strengthens the system’s ability to deliver accurate information.

Why should marketers care about RAG?

Because it changes how recommendations get made—from blog ideas to product suggestions and chatbot replies. RAG technology enables AI applications to draw from knowledge bases that reflect your brand’s expertise, which can directly impact customer satisfaction. If your content is part of those sources, you become part of the generation processes that shape AI responses.

See also: LLMs vs SEO: What’s Changing in Search Discovery

Does RAG affect SEO?

Yes. RAG performance in ranking and retrieval depends on retrieval accuracy, system’s ability to understand user queries, and the depth of your knowledge base. When rag based systems pull external knowledge from your content, they feed it into generation processes that produce accurate responses—meaning your visibility is tied to being part of trusted domain specific knowledge bases.

Do I need to optimize my content differently for RAG?

Not radically, but you should make your content easier to retrieve. That means structuring it for parallel processing and semantic search, writing with context awareness limited only by the retrieval process and knowledge base size, and ensuring accurate responses backed by reliable sources. For healthcare and legal sectors, this may include integrating patient data or relevant case law while respecting data privacy—a critical challenge in the rapidly evolving landscape of AI.

Can small SaaS brands benefit from this?

Absolutely. Smaller brands with focused knowledge bases can see faster widespread adoption of their content in AI generated responses, especially if they create high-quality, contextually relevant material that RAG technology can easily retrieve. The key is aligning with how traditional generative models are being replaced by retrieval-driven generation processes that serve accurate responses to niche user queries.

The post How Retrieval-Augmented Generation (RAG) Impacts AI Recommendations And What SaaS Marketers Should Do to Stay Ahead appeared first on Quoleady.

For years, traditional SEO was about keywords, rankings, and landing that prime spot on traditional search engines like Google. You wrote for search engines so users could find you. That was the deal.

But now, instead of just matching keywords, search engines are developing to understand user intent. And it’s not just Google tweaking its algorithm quietly. We have large language models (LLMs) like ChatGPT, Gemini, and Microsoft Copilot stepping in, delivering direct answers that sometimes bypass the old “10 blue links” format entirely.

That’s great for users—they ask a question, get a precise answer, and avoid digging through multiple pages.

So what does this mean for brands and SEO pros who’ve mastered traditional SEO practices? If AI tools are becoming the new gatekeepers of information, where does that leave websites and search engine optimization?

Is SEO dead? Or is there a smarter way to show up in the evolving landscape of search discovery?

Traditional SEO: What worked until now

For years, traditional SEO revolved around three pillars: keywords, backlinks, and addressing user intent. You optimized on-page content, structured your site with schema, internal linking, and meta tags to help search engines crawl and index your site effectively.

Landing a featured snippet—the small box above regular results—was the holy grail of organic search traffic.

Search engines would crawl your content, index it, and rank pages based on how well they matched queries. Your focus was on keyword density and search engine rankings.

But what’s happening now?

LLMs: A new layer of search discovery

Large language models (LLMs) don’t search the web the way traditional search engines do. They generate answers based on vast training data instead of returning a list of links.

This shift means people are not just searching; they’re conversing with AI. The search discovery process is more direct and personalized, reducing the need for users to click through multiple pages.

That’s a game-changer. Users might get AI generated answers right away—no click needed. Your brand might be mentioned or may not.

Search engines are no longer the only way users find information. Instead, AI search and large language models dominate, changing how brand’s visibility is measured.

Dig deeper: LLMs.txt: The New SEO Hack for AI-Powered Search

Key differences between SEO and LLM-based discovery

What does this mean for SEO and LLMs?

SEO strategies need to develop. Traditional SEO still matters for search engines and driving search traffic, but optimizing for large language models and AI search requires new tactics:

• Focus on natural language that matches user intent.
• Structure content for both traditional search engines and LLMs.
• Aim for inclusion in AI generated answers and direct answers.
• Use AI tools to analyze and refine content for both keyword relevance and semantic depth.
• Balance keyword density with meaningful natural language that appeals to large language models.

Learn more: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

The future of search: Blending SEO and LLM optimization

In 2025, search engines and large language models coexist. Users expect fast, accurate answers, whether via a link or a direct response from an AI assistant.

To succeed, marketers must blend traditional SEO techniques with LLM SEO approaches. Understanding the difference between traditional SEO practices and LLM optimization will help you maintain and grow your organic search traffic while adapting to the rise of AI-driven search tools.

Remember, optimizing for large language models (LLMs) is not just about keywords anymore—it’s about understanding context, user intent, and creating content that is useful enough to be cited by AI search assistants.

If you want your brand to maintain a strong presence in this shifting landscape, embrace both traditional SEO and large language model optimization strategies. This dual approach will maximize your visibility across search engines and large language models, ensuring your content reaches users wherever they look for answers.

We help B2B SaaS brands rank where it matters—in search and AI. Work with us

How LLMs are changing user behavior

User behavior is shifting dramatically. More and more, people are turning to virtual assistants like ChatGPT instead of traditional search engines. You type a question, and instead of clicking through multiple web pages, you get a direct answer, often in a neat paragraph—no need to jump from site to site.

This shift is already changing how people interact with search results. As AI assistants and AI-powered search experiences become more common, users increasingly consume information directly within the interface instead of visiting multiple websites.

Google’s AI Overviews are accelerating this trend by providing direct answers for informational queries that previously required users to browse several sources. At the same time, referral traffic from LLMs is becoming a growing acquisition channel for many businesses.

For example, one client of ours experienced a sharp drop in organic traffic as visits were redirected to LLMs instead. Their LLM traffic increased as organic visits declined, highlighting how users now interact differently with search results.

See also: Schema & Structured Data for LLM Visibility: What Actually Helps?

How large language models handle complex queries

Large language models don’t just answer simple questions like “weather in Paris.” They handle complex, exploratory user queries such as “What’s the difference between LLMs and traditional SEO?” or “How do I optimize my site for AI Overviews?”

These models draw on vast training data and pull from many sources to generate comprehensive, concise responses—answers that once required visiting multiple sites and reading extensively.

Tools like Perplexity combine real-time web browsing with conversational replies, Gemini handles follow-up questions, and Bing Copilot provides citations without forcing users to click unless they want to. This is zero-click search at scale: users interact differently, asking questions and getting immediate answers, often without visiting your web pages.

What this means for your SEO strategy

SEO isn’t dead. But marketing teams need to rethink their approach. Optimizing solely for search rankings on Google’s crawler is no longer enough. Today, you must consider how LLMs prefer content that’s clear, authoritative, and structured for easy summarization by AI.

Structured data, internal links, and clear signals of semantic relevance remain crucial—not just for traditional SEO, but also for helping AI understand and cite your content.

Your content must satisfy search intent better than ever to earn brand mentions in AI-generated answers and digital PR channels. The new goal is inclusion in LLM-powered responses and Google AI Overviews—not just clicks.

Real results: Clients featured in LLM and AI overviews

Our clients have seen firsthand how creating content optimized for large language models drives visibility.

• ResponseScribe’s content was cited directly in Google AI Overviews, boosting their brand mentions and authority.

• Pass Kit and Loop Loyalty are now authoritative sources referenced by LLMs across major chatbot platforms and Google AI summaries, increasing their presence in digital marketing.

These examples show how SEO professionals can leverage well-structured, authoritative content—supported by E-E-A-T principles—to capture attention from both search engines and generative search tools.

To stay competitive, marketing teams must blend traditional SEO tactics with strategies focused on AI-powered discovery. This means investing in structured data, understanding user queries, and creating content that satisfies semantic relevance and search volume trends while earning citations and brand mentions in AI-driven results.

Learn about how to create LLM referral report in GA4

Optimizing for LLM discovery: what you can do

Want your brand to show up in LLM responses, not just on Google’s search results page? Here’s how to create content that gets noticed—and cited—by AI models and authoritative sites.

1. Create content that gets cited by LLMs

LLMs love clear, fact-based snippets. Think of your content as a cheat sheet for an AI assistant: short, direct statements, stats, and answers that are easy to grab and rephrase. The more “fact-checkable” your quality content, the better your chances of getting an authoritative answer. Use upfront summaries, bullet points, and Q&A sections to highlight your expertise and make it easy for AI to create content citations.

2. Be an authority in your niche

Reputable sources and authoritative sites get preferential treatment by AI systems. Build your brand as a trusted resource through detailed guides, case studies, or thought leadership. This boosts your brand recognition and improves your website’s visibility in both traditional search results and LLM visibility. Consistency in messaging across your site and social media also helps establish your authority.

3. Use structured data and schema markup

Adding structured data and schema like FAQ, How-To, or Article markup helps AI models better understand your page’s content. This technical step makes your pages easier for LLMs to parse, increasing the chances your content is pulled for direct answers.

4. Distribute content across platforms LLMs crawl

LLMs don’t only index your website—they crawl forums, Reddit, Quora, and LinkedIn. Repurpose your insights in these places to build referring domains and increase your referral traffic. The broader your content footprint, the more your expertise will be recognized by AI.

5. Monitor your brand’s appearance in AI answers

Tracking your LLM visibility and how your brand appears in AI assistant responses helps you understand the feedback loop between your content and AI-driven discovery. Note that AI responses may vary per user, so use multiple checks beyond personal accounts.

Read also: Do G2 and Capterra Reviews Influence ChatGPT Rankings? [2025 Research]

6. Build holistic topic clusters

Don’t rely on one-off articles. Cover your topics comprehensively with beginner guides, expert interviews, and case studies, all interlinked. This structure signals to AI that your site is a trusted, connected resource, improving your authority and increasing the chance of being cited.

7. Share unique data and original insights

Reputable sources stand out by sharing unique, verifiable data—surveys, fresh stats, or expert opinions. LLM optimized content that offers original insights is more likely to earn brand recognition and appear as an authoritative answer in search results and AI responses.

8. Keep your content fresh and up-to-date

AI systems prioritize content freshness. Regularly update your articles, add new examples, and include “last updated” dates. Fresh, relevant content signals to both humans and AI models that your site is a reliable information source.

9. Optimize for readability

Make your content easy to scan: short paragraphs, bullet points, clear headings, and conversational phrases help. The more LLM optimized content is scannable, the better its chances to appear in Google Gemini or other AI assistant summaries.

10. Understand LLM knowledge cut-offs

Many AI systems have a knowledge cut-off date. For fast-changing industries, focus on platforms with real-time browsing or sources LLMs frequently retrain on to ensure your business information stays current in AI responses.

Bonus tips:

• Use conversational language and anticipate questions users and AIs might ask.
• Internally link related articles so LLMs see your site as a trustworthy, authoritative network.

The goal is not just to rank in search results, but to be referring domains that LLMs trust and cite. Being found isn’t enough—you’ve got to be worth quoting.

Learn more: From Blog Posts to Answers: Repurposing for AI Assistants

Will traditional SEO still matter?

Short answer: yes. Just not in the same starring role.

Google isn’t ditching search results. Instead, we’re seeing a mashup: AI assistants and traditional listings appear side by side. Sometimes users get a direct authoritative answer from AI models, sometimes a list of websites, and sometimes both.

Traditional SEO is still useful. But now it fuels two things:

• The search results people still click
• The LLM responses people read instead of clicking

In other words, SEO isn’t going away—it’s sharing the stage with AI-driven platforms. The better your SEO and quality content, the more likely your site becomes the reputable source that gets cited and summarized, no matter where the question is asked.

Conclusion: Don’t сhoose between SEO and LLMs – cover both

This isn’t an either-or scenario. If you want to stay discoverable, you can’t pick sides. It’s SEO and LLMs, all the time. Ignoring either channel means missing out on valuable traffic from your target audience.

Write for both bots: Google’s crawler and AI assistants. Keep publishing LLM optimized content that’s clear, structured, and genuinely helpful. Use simple language and conversational phrases. Add schema markup to make your content worth citing—not just clicking.

Whether a human, a crawler, or an AI assistant reads your page, the goal is the same: to be the trusted voice they want to reference.

You might be interested in: 10 Content Formats That Get Picked Up by LLMs

Search discovery is evolving fast

But if you focus on content generation that prioritizes quality, clarity, and authority, you’ll be ready for whatever comes next. It’s not just about ranking in search results anymore. It’s about showing up where the answers actually are.

Want your business information and brand to show up in Google Gemini and other AI summaries? Quoleady helps SaaS companies create content that gets cited, not just ranked.

We’ve helped dozens of clients boost their brand recognition and improve their LLM visibility by optimizing content for both SEO and AI-driven platforms.

FAQs about LLMO vs SEO

How is optimizing for LLMs different from traditional SEO?

Traditional SEO focuses on ranking in search results, while optimizing for LLMs means crafting clear, authoritative content that AI models can easily understand, cite, and reuse in their responses.

Can my existing content be adapted for LLM discovery?

Absolutely! By adding structured data, clarifying key points, and making answers more direct, you can turn your current content into LLM optimized content and increase its chances of being featured.

Which platforms do LLMs crawl besides my website?

AI systems also scan public platforms like Reddit, forums, LinkedIn, and other community sites. Sharing your expertise across these channels builds referring domains and expands your footprint in the AI discovery space.

How do I measure if my content is being used by LLMs?

Use specialized tools to measure success by monitoring AI citations, changes in traffic, and your presence in AI-generated answers. This feedback loop helps you optimize your strategy over time.

This balanced approach, mixing SEO fundamentals with LLM-centric tactics like targeting long tail keywords and engaging on AI driven platforms, is your best path forward in today’s search landscape.

Work with Quoleady to make your SaaS brand visible in both search and AI results.

The post LLM Optimization vs SEO: What’s Changing in Search Discovery appeared first on Quoleady.

Some agencies focus on using AI tools to create content and analyze data. Others specialize in helping brands get discovered in AI-driven search engines like ChatGPT, Google’s AI Overviews, Gemini, and Perplexity. Many agencies combine these approaches to give clients a competitive edge.

The main goal is to reduce guesswork and enhance marketing efforts so every campaign delivers measurable impact.

A. Agencies that use AI to improve traditional marketing

Many digital marketing agencies now integrate AI marketing into traditional channels, making tasks faster and more efficient. They rely on AI marketing tools like Jasper, ChatGPT, SurferSEO, and Midjourney to handle:

• Content creation: Generating blog posts, landing pages, and product descriptions at scale
• SEO: Automating keyword research, optimizing content, running site audits, and building backlinks
• Email marketing: Personalizing campaigns, automating sends, and improving open rates
• Social media: Scheduling posts, analyzing engagement, and creating visuals or captions
• PPC (pay-per-click): Optimizing ad copy, predicting click-through rates with predictive analytics, and managing bids

Benefits:

• Faster campaign launches and higher content output
• Cost savings on repetitive tasks
• Easier scalability for content and ads

Limitations:

• Core strategy often stays the same, only executed more efficiently
• Limited adaptation to new AI-driven search behaviors

Read also: From Blog Posts to Answers: Repurposing for AI Assistants

B. Agencies that optimize for LLMs and AI search

A growing number of AI marketing agencies specialize in getting brands noticed by large language models (LLMs) and AI-powered search tools such as Google’s AI Overviews, ChatGPT, Perplexity, Gemini, and Claude. This approach—called Generative Engine Optimization (GEO)—is becoming a key part of digital marketing.

How GEO works:

• Optimizes content so AI chatbots and search models can easily find, understand, and reference it in responses
• Uses authority signals, structured data, and credible backlinks to influence AI answer selection
• Prioritizes expert insights, statistics, and credible sources to increase the likelihood of being cited

Ranking factors for AI answers:

• Well-cited, trustworthy content that builds credibility
• Clear formatting and structured data
• Quality backlinks from reputable websites
• Strong E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)

Benefits:

• Future-proof visibility as AI-generated answers grow in importance
• Stronger brand authority when cited by trusted AI platforms

Limitations:

• GEO standards are still developing, with unpredictable results
• Requires continuous testing and updates

Related 10 Content Formats That Get Picked Up by LLMs

Why many agencies combine both approaches

The most effective AI marketing agencies merge the efficiency of traditional digital marketing enhanced with AI marketing tools and the forward-looking strategies of GEO. By doing this, they cover every angle—boosting day-to-day campaign performance while preparing brands for the future of AI-driven discovery.

With artificial intelligence, machine learning, and predictive analytics at the core of their marketing services, these agencies help brands not just keep up with change—but lead it.

Dig deeper: LLMs.txt files: The New SEO Hack for AI-Powered Search

Best AI marketing agencies to boost your business growth

1. Quoleady

Office location: Remote (core team based in Europe)

Year founded: 2019

Team Size: 11-50

Industries served: SaaS, B2B tech, and digital startups

Overview: Quoleady is a B2B SaaS SEO and AI visibility agency that works mainly with software companies, using data-driven SEO, content marketing, digital PR, and GEO strategies to help them grow. They specialize in creating expert-driven, research-backed content that increases organic visibility, improves presence across Google and AI search platforms, generates qualified pipeline, and builds category authority.

Quoleady is known for its commitment to quality, ethical marketing, and maintaining a consistent brand voice for each client. Their team creates well-researched content tailored to business goals while helping companies improve visibility across both traditional search engines and AI-driven discovery platforms.

As a remote-first agency, Quoleady partners with SaaS companies worldwide, helping startups and enterprise brands grow through organic search, digital PR, authority building, and AI visibility initiatives designed to support long-term revenue growth.

You night be interested in: Schema & Structured Data for LLM Visibility: What Actually Helps?

Key services

• Brands featured in LLM answers: Optimizing content and strategy to help brands appear in AI-generated responses, such as Google’s AI Overviews, ChatGPT, Gemini, and Perplexity.
• SaaS SEO guidance: Advanced keyword research, on-page optimization, and analysing and fixing on-site issues to boost your rankings and organic traffic.
• SaaS content strategy: In-depth market and competitor analysis to identify high-ROI content opportunities, followed by the creation of authoritative blog posts, guides, and landing pages.
• SaaS digital PR & link building: Ethical, white-hat outreach to get your brand featured on top sites like Monday.com, HubSpot, Zapier, Forbes and Entrepreneur.
• Content refresh & optimization: Updating old content to give it a new life with Google rankings boost and LLMs visibility.

Noteworthy client results

Quoleady has helped all kinds of SaaS and tech brands get noticed in AI-generated answers and Google’s AI Overviews–two key places to show up if you want to drive qualified traffic and build authority in 2026.

Featured in LLM answers:

• Expandi: Ranked #4 on ChatGPT for “LinkedIn automation tools”
• GoRout: Ranked #3 on ChatGPT for “softball pitching devices”

Google AI Overviews (as of April 10, 2025):

• ShoutAboutUs: Featured for 691 keywords in AI Overviews
• BetterMode: Featured for over 2,000 keywords in AI Overviews
• FuelFinance: Featured for 942 keywords in AI Overviews
• OneFlow: Featured for over 7,000 keywords in AI Overviews
• GoRout: Featured for over 2,000 keywords in AI Overviews
• PassKit: Featured for 1,000 keywords in AI Overviews
• Loopy Loyalty: Featured for 648 keywords in AI Overviews

These results show that Quoleady consistently helps clients get featured in high-visibility AI-generated search results. Here is one more result of a client showing a noticeable increase in traffic coming from ChatGPT.

As AI Overviews and tools like ChatGPT continue to share authoritative sources, such exposure is becoming even more valuable for building online visibility and trust.

Want to track this kind of traffic for your own site? Check out our guide on how to create LLM referral report in GA4 to monitor visits from LLMs like ChatGPT.

If you want an agency that truly partners with you and delivers results, book a call today.

2. Growth Hackers

Office location: Sheridan, Wyoming, United States; New Taipei City, Taiwan

Year founded: 2016

Team Size: 11-49

Industries served: B2B SaaS, eCommerce, healthcare, manufacturing, fintech

Overview: Growth Hackers is an AI-centered marketing agency focused on helping brands scale through data-driven acquisition, conversion, and retention strategies. Their approach combines performance marketing experience with predictive analytics and real-time optimization. By automating key parts of the sales and marketing funnel, they support companies aiming for steady, measurable growth.

Key services

• Generative Engine Optimization (GEO) to help brands appear in LLM-generated results across tools like ChatGPT and Gemini
• AI-powered conversion rate optimization driven by continuous real-time data analysis
• Automated sales funnel development to maintain a consistent flow of qualified leads
• AI-based video production using models such as Veo3 and Sora
• Programmatic SEO to create scalable, keyword-rich landing pages
• Automated outreach workflows for both social platforms and email

See also: Structuring Web Pages for AI-First Indexing

Noteworthy clients results: 

• Helped a client reach $100M ARR through an AI-supported content marketing strategy focused on improving messaging across the funnel
• Assisted another company in securing $40M in funding by automating user acquisition and investor outreach processes

3. Skale

Office location: Remote-first (serving tech and SaaS brands globally)

Year founded: 2019

Team Size: 11-50

Industries served: Tech companies and B2B SaaS brands

Overview: Skale is an AI Search growth agency built specifically for tech and SaaS companies that want to turn AI-powered discovery into measurable revenue. While many agencies treat AI visibility as a nice-to-have metric, Skale approaches it as a predictable customer acquisition engine. Their work bridges the gap between showing up in LLM-generated answers and connecting that visibility directly to pipeline growth.
By combining AI Search optimization with traditional SEO and a proprietary attribution framework, they give clients complete transparency into how AI-driven discovery influences demand generation, shapes buying decisions, and converts prospects into customers.

Key services

• AI Search optimization across ChatGPT, Perplexity, Google’s AI Overviews, and other LLM platforms
• Traditional SEO strategy designed to compound long-term organic growth
• Proprietary AI attribution framework linking visibility to pipeline and revenue
• Demand generation tracking through AI-powered channels
• Brand selection optimization in AI-generated recommendations
• Full-funnel conversion tracking from AI discovery to closed deals

Noteworthy clients

Skale has worked with leading tech and SaaS brands including G2, SmallPDF, SEON, Happy Scribe, HubSpot, and Freshworks. Their clients benefit from clear, data-driven insights that connect AI Search visibility to business outcomes. The agency’s proprietary attribution model helps brands understand exactly how AI-powered discovery impacts their bottom line, from initial brand awareness through final conversion.

Starting investment: Comprehensive audits begin at $5,000, providing brands with a detailed roadmap for turning AI visibility into a predictable growth channel.

4. Growth Marketing Pro

Office location: San Francisco, California, United States

Year founded: 2017

Team Size: 11–50

Industries served: B2B SaaS, startups, marketing agencies, eCommerce, technology companies.

Overview: Growth Marketing Pro is a content and SEO-focused growth agency that helps companies scale organic acquisition through data-driven strategies and high-performing content. Known for its strong expertise in SEO and content marketing, the agency combines proven growth frameworks with AI-driven insights to improve rankings, increase traffic, and drive conversions. Their approach emphasizes sustainable, long-term growth by aligning content with search intent and business goals.

Key services

• Generative Engine Optimization (GEO) to improve brand visibility across AI-driven search platforms like ChatGPT and Gemini
• SEO-driven content marketing focused on ranking for high-intent keywords and driving qualified traffic
• Programmatic SEO to scale content production and capture large keyword opportunities
• Conversion-focused content strategy designed to turn organic traffic into leads and customers
• Link building and authority growth through strategic partnerships and outreach
• AI-powered keyword research and optimization to identify emerging trends and opportunities

Noteworthy clients results:

• Cube Software: Grew organic traffic from 0 to 56,000+ visitors/month, achieved #1 rankings for core FP&A terms in Google and ChatGPT, and generated 0 to 150+ leads/month from AI platforms through Reddit visibility.
• SmartRecruiters: Increased MQLs by 37× and decreased CPA by 89% through Google and LinkedIn ads optimization.
• Noteworthy VC Firm: Ranked #1 for brand coverage, mentions, and share of voice in related LLM searches, with their new Wikipedia page becoming the #2 most cited VC firm on ChatGPT. 

5. Scalerrs

Office location: Fully remote 

Year founded: 2024

Team Size: 11-50

Industries served: Mid-market and enterprise B2B SaaS

Overview: Scalerrs is one of the few SaaS marketing agencies that has genuinely rebuilt its playbooks for the AI search era and not just added a GEO bullet point to a service page. The agency runs a multi-channel visibility approach , designed to help mid-market and enterprise SaaS companies own their product category across every surface where buyers are now searching: Google, ChatGPT, Perplexity, Google AI Mode, Reddit, YouTube, and third-party listicles.

What makes Scalerrs distinct in the AI marketing space is that the channels most agencies treat as add-ons (Reddit, Wikipedia, AI citation building) are core services delivered by in-house specialists, not subcontracted to freelancers. The results feed back into a single client portal where CMOs and SEO leads can track progress in real time.

Key services

• AEO / AI Search optimization for ChatGPT, Google AI Mode, Perplexity and Claude
• Traditional Google SEO with full-service technical SEO
• Reddit marketing (in-house) with authentic community participation in relevant subreddits
• 3rd-party listicles & Wikipedia compliant pages 
• YouTube SEO
• SaaS link building with manual outreach 
• SaaS, ICP-specific content creation

Noteworthy clients results:

• Achieved 4K+ AI citations in 30 days; ranked #3 in average AI visibility for Morgen
• Qrvey ranked #3 in AI search brand visibility behind only Sisense and Tableau; ~2,836 mentions across LLMs
• AutoRFP unlocked self-attributions from ChatGPT, Perplexity, and Claude doubled after engaging Scalerrs; 3x increase in overall organic search
• 30% of Defaults total pipeline attributed to AEO/SEO and AI search; $400K+ in pipeline generated from organic in a single quarter with no additional ad spend

6. Embarque

Office location: London, United Kingdom

Year founded: 2020

Team Size: 11-50

Industries served: SaaS, marketplaces, job boards, and fast-growing startups across various sectors.

Overview: Embarque is an SEO agency with a very straightforward approach: deliver premium content that converts and gets brands noticed by large language models (LLMs) and AI-powered search platforms. Their services has a strong focus on structured content, programmatic SEO, and several other AI ranking signals.

Key services

• AI SEO strategy for LLM visibility (ChatGPT, Perplexity, etc.)
• Structured content production like “What is,” “How to,” and comparison pages
• Programmatic SEO to to cover more topics and get included in AI answers
• Optimize entities and schema to help AI models recognize your brand
• Internal linking and site architecture aligned with topic clusters
• Track keywords across both AI tools and traditional search engines

Noteworthy clients results:

• Helped MentorCruise drive 11,500+ visits from AI search in under 3 months
• Instatus and Growform both saw 800+ AI search visits
• Clients regularly appear in AI-generated summaries across Perplexity, Gemini, and ChatGPT

Learn more: How to get your SaaS brand quoted by ChatGPT (and other AI Tools)

7. Omnius

Office location: Europe (remote-first, serving clients across the continent)

Year founded: 2019

Team Size: 11-50

Industries served: B2B SaaS, fintech startups, and enterprise technology companies

Overview: Omnius is a B2B SEO agency with a deep know-how in areas like Generative Engine Optimization (GEO), LLM Optimization (LLMO), and Answer Engine Optimization (AEO). These are the new places where brands need to stand out as people shift from classic search to getting answers directly from AI tools. Instead of just fine-tuning content for keywords, their team designs your whole site and online presence to work well with AI, so you show up in ChatGPT, Perplexity, Gemini answers and references.

Key services

• Featured snippet & voice search optimization
• Content structure & clustering
• AI crawler optimization 
• Strategic keyword placement to maximize AI visibility
• Schema Markup & LLMs.txt
• Citation engineering
• Synthetic query generation

Noteworthy clients results:

• Helped AuthoredUp grow organic traffic by 110% in six months
• Generated 84M+ new users for clients since launch
• Delivered 64% higher conversion and top-3 rankings for key commercial terms
• Helped leading European SaaS and fintech brands become top-cited sources in ChatGPT and Perplexity for their core topics.
• Clients have reported increased inclusion in AI-generated answers and knowledge panels, leading to higher authority and trust.

Related: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

8. Avenue Z

Office location: New York, NY, United States

Year founded: 2022

Team Size: 51-200

Industries served: eCommerce, healthcare, technology, B2B, and a wide range of consumer and enterprise brands

Overview: Avenue Z is a modern marketing and communications agency focused on influence, revenue, and reputation. They help brands stand out in AI-generated answers, search results, and major media. Their team combines PR, content, and AI tools to make sure your brand gets seen and trusted. They’ve helped fintech and SaaS companies get featured in top publications, grow leads, and earn media mentions with their strategies.

Key services

• AI Optimization (AIO) for search and brand reputation
• Strategic content development to get your brand featured in LLM-generated results
• AI technical optimization to make your site more discoverable by LLMs
• AI visibility audit to track your brand’s share of voice across AI platforms 
• Conversational content optimization to match your brand’s voice

See also: Structuring Web Pages for AI-First Indexing

Noteworthy clients results: 

• One client saw a 210% increase in ranked keywords after a website revamp, which included AI-optimized content and technical improvements, leading to a tripling of clinical registrations.
• Another project resulted in a 54.6% increase in scheduled test flights that improved online presence, lead generation, and engagement-driven in part by strategic AI optimization.

9. Rank4AI

Office location: United Kingdom, remote

Year founded: 2024

Team Size: 1-10

Industries served: Cross sector – SME to enterprise

Overview: Rank4AI is a UK-based AI search agency founded by Adam Parker, focused on improving visibility and recommendation likelihood across platforms like ChatGPT, Gemini, and Google AI, without relying on traditional SEO or PPC models. The agency builds clear, consistent brand signals across technical foundations, content, and external ecosystems, delivering hands-on implementation so businesses are accurately understood and recommended by AI systems.

Key services

• AI Search Audits
• Entity and Signal Alignment
• Content Structuring for AI
• External Validation and Citations
• Recommendation Readiness

Noteworthy clients results:

• Built around interpretation and trust rather than short term ranking tactics
• Hands on execution alongside strategy, clients receive active implementation, not just recommendations
• Active research and monitoring across all six major AI platforms
• Education first approach, clear reasoning is provided alongside every change so clients understand what is being built and why

10. Stella Rising

Office location: New York, NY, United States

Year founded: 1982 (formerly known as Women’s Marketing Inc., rebranded to Stella Rising in 2018)

Team Size: 51-100

Industries served: Consumer brands, beauty, health & wellness, food & beverage, retail, and eCommerce

Overview: Stella Rising is a digital marketing agency known for helping consumer brands keep up with big changes online. As search engines shift toward AI-powered answers and tools like ChatGPT, Perplexity, and Google’s AI Overviews, their Generative Engine Optimization (GEO) service has helped brands stay visible in LLMs. Their strategy is to blend classic SEO with smart AI strategies to make sure your brands show up in search engines as well as mentioned and recommended in AI-generated answers.

Key services

• Generative Engine Optimization (GEO)
• Content structuring for AI
• Brand mention monitoring
• AI search trend research
• Technical SEO and structured data
• Performance tracking & strategy adjustment

Noteworthy clients results:

• Boosted ROAS by 126% for AHAVA Dead Sea Labs
• Brands working with Stella Rising have benefited from improved accuracy and frequency of citations in AI-generated responses

11. Soar

Office location: Remote-first (serving clients globally)

Year founded: 2017

Team Size: 11-50

Industries served: SaaS, eCommerce, Web3, agencies, and digital publications

Overview: Soar is an agency that’s found its own niche in LLM SEO. They can help your brand show up in the sources that tools like ChatGPT and Claude pull from when generating answers. Their approach goes beyond traditional SEO, focusing specifically on the channels and tactics that influence how LLMs learn, cite, and recommend brands. The big idea behind Soar is simple: as more people turn to AI for answers, brands need to be visible not just in search results, but in the data that trains and informs these models.

Key services

• LLM training data inclusion
• LLM SEO through Reddit, Quora, and Discord campaigns
• Content placement for AI training data exposure
• Community-first SEO and digital PR
• Managed content seeding across forums and micro-communities

Noteworthy clients results (AI Optimization & LLM Visibility):

• Generated 18,335 visits in one day from a Reddit campaign
• Drove 1.4M+ impressions and thousands of brand mentions
• Helped clients get picked up in AI model answers by seeding authoritative threads

Agencies helping brands optimize for LLMs and AI overviews

AI-generated answers are becoming a big part of how people find brands online. Tools like Google’s AI Overviews, ChatGPT, Perplexity, and Gemini often show answers before users even click on a website. That means getting your brand mentioned in these answers is now just as important as ranking high on Google.

Why it matters

Several market projections suggest that LLMs (large language models) could account for up to 15% of search traffic by 2028. Google’s AI Overview is already affecting billions of searches. When these tools pull from your content, they often include a direct mention or summary, which can bring real traffic and build trust in your brand.

What these agencies do

Specialized AI marketing agencies now offer services to help brands:

• Optimize content for LLM visibility and citations
• Add structure and markup to make content easier for AI to read
• Engineer prompts and authority signals that sound natural in AI responses
• Monitor and track brand mentions across AI platforms

These strategies go beyond regular SEO. They focus on how AI finds, summarizes, and shares your content. Agencies help organize content by topic, include unique insights, and make sure everything is easy for AI tools and accessible for retrieval-augmented generation (RAG) platforms.

Quoleady’s edge

Quoleady is ahead of the curve when it comes to AI-aware content. We’ve helped brands get featured in Google’s AI Overview, Gemini, Perplexity, and ChatGPT by focusing on clean structure, original data, and natural authority signals. We also help clients monitor AI mentions and stay ahead as things change.

Book a call and turn your B2B SaaS content into a magnet for both algorithms and customers.

Comparison table of all best 8 AI marketing agencies

How to сhoose an AI marketing agency?

Picking the right AI marketing agency can make a huge difference in your results. In today’s competitive space, top AI marketing specialists help brands go beyond basic automation, using artificial intelligence technologies like natural language processing to improve customer engagement, uncover customer insights, and craft personalized messages that resonate across multiple channels. Here are a few practical tips to make the best choice for your business:

1. Assess experience and industry fit

Look for agencies that have worked with leading brands or businesses similar to yours. This ensures they understand your marketing activities, your customer journey, and can design marketing campaigns tailored to your needs. Industry experience means they’ll know how to translate consumer insights into measurable results.

2. Evaluate AI and technology expertise

Choose a team that’s truly leveraging AI, not just using it as a buzzword. Ask about their AI powered tools, from Albert AI to advanced digital advertising platforms, and how they apply natural language processing for customer behavior analysis. A strong tech stack can give you a competitive advantage in today’s crowded market.

3. Range and depth of services

Consider whether you need help with email marketing, ad campaigns, Google Ads, or a complete content strategy. Some agencies focus solely on SEO, while others provide full-service marketing solutions, including performance marketing and cross screen targeting to ensure brand safety. The right mix will help you optimize campaigns for maximum ROI.

4. Flexibility and innovation

The best top AI marketing agencies combine data with human creativity to produce engaging content that works across marketing campaigns. They adapt quickly to shifts in customer behavior and use tools like Google Docs for seamless collaboration. Agencies willing to test new tactics will help you stay ahead.

5. Reputation and recognition

Read reviews, explore case studies, and see if the agency has been recognized for helping leading brands deliver exceptional results. Proven expertise in AI-driven marketing campaigns is a strong indicator of future success.

Conclusion: The future of AI marketing

AI marketing is evolving rapidly, and in 2026 it’s not just about automation – it’s about delivering smarter, real-time customer experiences across multiple channels. With AI powered tools, predictive analytics, and natural language processing, marketers can better understand customer behavior and create more relevant, high-impact marketing campaigns.

The most effective agencies are leveraging AI to unify data, personalize outreach, and improve customer engagement while protecting brand safety. By combining technology with human creativity, they can turn marketing activities into growth drivers.

Quoleady is among the top AI marketing partners for SaaS and B2B companies, offering marketing services that blend AI precision with strategic vision. From email marketing automation to ad campaigns and Google Ads optimization, we help leading brands deliver exceptional results. Our expertise in customer journey mapping, customer insights analysis, and content strategy ensures every initiative is backed by actionable data.

If you’re ready to future-proof your marketing and gain a competitive advantage, partner with a team that knows how to optimize campaigns using AI. Book a call with Quoleady today and start transforming your marketing activities into measurable wins.

The post Top 11 AI marketing agencies in 2026 appeared first on Quoleady.

Our SaaS clients are getting 𝟎.𝟓-𝟑% of total traffic from LLMs.

Here’s how to create a simple LLM referral report in GA4:

𝐒𝐭𝐞𝐩 𝟏: 𝐀𝐜𝐜𝐞𝐬𝐬 𝐆𝐀𝟒 𝐚𝐧𝐝 n𝐚𝐯𝐢𝐠𝐚𝐭𝐞 𝐭𝐨 e𝐱𝐩𝐥𝐨𝐫𝐞

Start by logging into your Google Analytics 4 account. From the left hand menu, click on Explore — GA4’s feature for building custom reports and session segments. Then select “Blank” to create a new report from scratch.

This section of GA4 is ideal for tracking specialized traffic in GA4 that standard reports might miss. By leveraging the dimensions and metrics section, you can build a report tailored to monitor LLM referral traffic and analyze traffic trends over time.

Related: How Retrieval-Augmented Generation (RAG) Impacts AI Recommendations And What SaaS Marketers Should Do to Stay Ahead

𝐒𝐭𝐞𝐩 𝟐: 𝐒𝐞𝐭 u𝐩 𝐭𝐡𝐞 c𝐮𝐬𝐭𝐨𝐦 e𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧 r𝐞𝐩𝐨𝐫𝐭

1. Rename the report

▪️ Click on “Untitled Exploration” and rename it to something like:

“LLM Referral Traffic & Key Events”

Read also: Structuring Web Pages for AI-First Indexing

2. Add dimensions

Click the “+” button in the Dimensions section to add new dimensions. Search for and include:

• Session source/medium — this is a crucial session segment that identifies where your traffic originates, including referrals from AI tools.
• Page referrer — helps pinpoint exact pages and sources sending referral traffic.

Adding these dimensions lets you track traffic specifically from AI search platforms and other LLM powered tools.

3. Add metrics

Next, click “+” in the Metrics section to add key metrics like:

• Sessions — total visits driven by LLM referrals.
• Key events — important user interactions tagged as conversions or engagement signals.
• Any other relevant key metrics such as bounce rate, average engagement time, and session duration to gauge user engagement and conversion rates.

These metrics provide deeper traffic insights into how users interact with your site after arriving via AI-driven referrals.

You might be interested in: LLMs vs SEO: What’s Changing in Search Discovery

4. Add rows and values

Drag Session source/medium into the Rows area, and Sessions into Values to structure the report for clear visibility of traffic sources and volume.

𝐒𝐭𝐞𝐩 𝟑: 𝐒𝐞𝐭 u𝐩 f𝐢𝐥𝐭𝐞𝐫𝐬 𝐭𝐨 i𝐧𝐜𝐥𝐮𝐝𝐞 o𝐧𝐥𝐲 𝐋𝐋𝐌 r𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬

To focus your report exclusively on LLM referral traffic, drag either Page referrer or Session source/medium into the Filters section. Then apply a “Matches Regex” filter using this regex string to capture known LLM referral domains:

.*openai.*|.*chatgpt.*|.*copilot.*|.*perplexity.*|.*gemini.*|.*bard.*|.*gpt.*|.*neeva.*|.*copy\.ai.*|.*writesonic.*|.*nimble.*|.*outrider.*|.*edgeservices.*|.*astastic.*|.*bnngpt.*

Using regex filters like this in GA4 allows precise tracking of AI powered tools driving traffic, filtering out unrelated sources to keep your data clean. This step is essential for understanding traffic in GA4 specifically generated from AI search and other LLMs beyond traditional referral sources.

Note: you can use the source/medium contains filter to exclude anything that you want to remove from the report.

See also: How to get your SaaS brand quoted by ChatGPT (and other AI Tools)

𝐒𝐭𝐞𝐩 𝟒: 𝐀𝐝𝐣𝐮𝐬𝐭 v𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (o𝐩𝐭𝐢𝐨𝐧𝐚𝐥)

GA4 offers multiple visualization options to present your LLM referral data:

• Table View — for a detailed breakdown of sessions, key events, and other metrics by source.
• Line Graph — to monitor traffic trends over time and see growth in LLM referrals.
• Bar Chart — for quick comparisons across different ai tools or platforms.

Adding Date as a breakdown dimension helps reveal how traffic insights evolve, showing increases in AI search visibility and changes in user behavior over weeks or months.

𝐒𝐭𝐞𝐩 𝟓: 𝐒𝐚𝐯𝐞 & s𝐡𝐚𝐫𝐞 𝐭𝐡𝐞 r𝐞𝐩𝐨𝐫𝐭

Once your report is configured:

• Click Save in GA4 to keep it accessible anytime for ongoing analysis.
• Optionally, use the Share feature to create a link or export the report as CSV or PDF for team collaboration or presentation.

Tracking LLM referral traffic regularly enables data-driven decisions that optimize how your website engages users discovering content through AI-powered platforms.

You might be interested: 10 Content Formats That Get Picked Up by LLMs

Final notes on key metrics and data interpretation

Keep in mind, key events in this report might be inflated if your GA4 setup tags many interactions as key events by default. Monitor session duration, bounce rate, and engagement rate alongside conversions to get a comprehensive picture of user engagement.

By analyzing session source medium data alongside regex filters and key metrics, you can verify how much traffic is driven by AI search compared to traditional search engines. This emerging source of traffic represents a valuable opportunity to boost your organic traffic and adapt marketing strategies for the future.

With this step-by-step method to create a custom LLM referral report in GA4, you’ll gain crucial insights into how users discover content through AI tools, enabling you to track and optimize your website’s performance in the age of AI search.

The post How to Track LLM Referral Traffic in GA4 (Step-by-Step Guide) appeared first on Quoleady.

That shift is giving rise to a new approach to content visibility: Generative Engine Optimization (GEO). Unlike traditional SEO, which focuses on ranking in search engines, GEO makes your content useful and accessible to AI tools that generate answers.

In this post, we’ll break down what GEO is, how it compares to traditional SEO, and what strategies actually work if you want your content to show up in AI-driven search engines.

What is Generative Engine Optimization (GEO) in digital marketing?

Generative Engine Optimization (GEO) is the process of optimizing content to make it prioritized, cited, and integrated into AI-driven search engines’ responses. The goal is to make content highly relevant and accessible for generative AI to include in its synthesized answers.

How GEO works in the context of AI-driven searches

Generative AI platforms rely on large language models (LLMs) trained on vast datasets to generate direct, conversational answers. When users input queries, these systems:

• Analyze intent: They interpret the user’s intent beyond simple keyword matching.
• Synthesize information: The AI scans its indexed data or real-time web sources, extracts relevant information, and generates a cohesive response.
• Prioritize content: Content that is clear, well-structured, and authoritative is more likely to be cited or featured in these AI-generated answers.

GEO makes sure your content aligns with these systems’ preferences by focusing on clarity, relevance, and structured data formats.

Dig deeper: LLMs.txt Files: The New SEO Hack for AI-Powered Search

Examples: How content appears in generative AI research platforms

ChatGPT: If optimized effectively, your content might be directly quoted or summarized when users search for specific topics like “best digital marketing strategies.”

Read also: 10 Content Formats That Get Picked Up by LLMs

Perplexity: Your content could be highlighted as a trusted source when users select specific focus modes such as “Academic Focus” for scholarly insights or “Web Focus” for general information.

Gemini: Your content might appear as part of a multimodal response that combines text with supplementary visuals or videos for richer user engagement.

Read more: Structuring Web Pages for AI-First Indexing

What is traditional SEO?

Traditional SEO strategy refers to the foundational practices used to improve a website’s visibility and rankings on search engine results pages (SERPs). Its primary goal is to attract organic traffic by optimizing various elements of a website, such as relevant keywords, content, and technical aspects, while building authority through backlinks. Traditional SEO relies on aligning content with user intent and search engine algorithms for relevance and credibility.

How traditional SEO works

Traditional SEO operates through a combination of on-page, off-page, and technical optimization techniques:

On-page optimization:

• Keyword research: Identifying the terms users search for and integrating them naturally into content, meta tags, headers, and URLs.
• Content creation: Producing high-quality, relevant content that addresses user queries and provides value.
• Meta tags: Crafting optimized title tags and meta descriptions that improve click-through rates (CTR).

Off-page optimization:

• Link building: Acquiring backlinks from reputable websites to boost domain authority and credibility.
• Guest blogging: Writing articles for other sites to generate traffic and establish industry authority.

Technical SEO:

• Site speed: Ensuring fast load times for better user experience and traditional search engine favorability.
• Mobile responsiveness: Optimizing design for seamless usability across devices.
• Structured data: Using schema markup to help search engines understand the content better.

Examples of traditional SEO in practice

A blog post optimized with long-tail keywords like “best hiking trails in California” ranks highly on Google SERPs when users search for related topics.

An e-commerce site uses optimized product descriptions, clean URLs, and alt tags for images to improve visibility in search results.

A business website builds authority by earning backlinks from industry-related blogs or directories.

Related: LLMs vs SEO: What’s Changing in Search Discovery

GEO vs SEO: Key differences

Unique factors influencing GEO visibility

While GEO shares many principles with traditional SEO, there are unique factors that specifically influence visibility in AI-driven platforms. According to our research, these differences highlight subtle shifts in optimization strategies required for generative engines:

1. Unlinked brand mentions: Unlike SEO, where backlinks are critical, unlinked text mentions of your brand or content can significantly improve visibility in large language models (LLMs). AI platforms value these mentions as a signal of authority and relevance.
2. Reduced impact of off-topic links and content: Backlinks from irrelevant websites or off-topic content designed solely for traffic (“site reputation abuse”) have minimal influence on LLM visibility. GEO prioritizes contextual relevance over sheer link volume.
3. Content type variations: Certain content types, such as PDF documents and knowledge bases, have a greater impact on LLM visibility compared to traditional SEO. Conversely, elements like e-commerce listings may be less effective for generative engines.
4. Unique document structures: LLMs benefit from content structured for machine comprehension, such as global document context and clear hierarchies. These formats enhance AI readability but may not directly impact search engine rankings.
5. Non-traditional data sources: Generative engines often train on data outside traditional SEO’s scope, including public GitHub repositories, code samples, and knowledge bases. Optimizing these sources can improve LLM visibility without affecting search rankings.
6. JavaScript limitations: Unlike search engines that can render JavaScript-heavy content, AI crawlers currently cannot process client-side JavaScript. This makes JS-based content invisible to LLMs while still benefiting traditional SEO efforts.

GEO is still rooted in SEO

Despite these differences, managing indexing, crawling, structuring content for machine readability, and building off-page mentions remain core practices of SEO. Brands that perform well in traditional search also tend to excel in LLM visibility due to overlapping optimization principles.

You might also be interested in: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

Top 5 GEO optimization strategies

GEO requires a tailored approach to make your content accessible, relevant, and prioritized by AI-driven platforms. Below are key strategies to optimize for GEO effectively:

1. Get cited with content that AI can reference

• Focus on credibility: Include well-researched data, authoritative quotes, and statistics to enhance content reliability. AI platforms prioritize content that provides clear and credible information.
• Use E-E-A-T principles: Make sure your content demonstrates Experience, Expertise, Authority, and Trustworthiness to meet Google’s standards.

2. Use structured data and clear headers

• Use schema markup: Structured data helps AI understand your content better by providing detailed metadata. Use schema markup to highlight key elements like FAQs, recipes, or product details.
• Organize content with headers: Use clear headings and subheadings to improve readability for both users and AI systems. Well-structured articles are more likely to be synthesized into generative responses.

3. Fix content structure with summaries and FAQs

• Summarize key points: Start with concise summaries that provide quick insights into the topic. AI systems often pull these for direct answers.
• Include FAQ sections: Answer specific, high-intent content questions using a question-and-answer format. This makes your content ideal for AI engines to extract relevant responses

4. Prioritize topical authority and clarity

• Build depth in your niche: Focus on creating pillar content and supporting cluster articles that establish your expertise in a specific area. This signals topical authority to AI platforms.
• Avoid jargon: Use simple, conversational language that aligns with natural user queries for better AI interpretation.

5. Publish on trusted, crawlable platforms

• Choose reliable platforms: Host your content on well-maintained websites that are easily crawlable by AI systems.
• Maintain freshness: Regularly update your content to stay relevant and improve its chances of being included in evolving generative responses.

With these strategies, businesses can optimize their content for visibility in AI-driven search environments while maintaining credibility and relevance across generative platforms.

Read also: Schema & Structured Data for LLM Visibility: What Actually Helps?

Using both SEO & GEO for B2B SaaS marketing

If you’re doing B2B SaaS marketing, using both GEO and SEO together helps you get seen in more places – from Google searches to AI tools like ChatGPT. This way, your content shows up whether someone’s doing deep research or just looking for a quick answer.

1. Build once, optimize twice

Start with creating high-quality content that follows SEO best practices like keyword targeting, internal linking, and search intent alignment. Then go back and add GEO-friendly elements:

• Summarize key takeaways at the top or end
• Break down concepts clearly with headers and bullet points
• Use direct, factual language AI models can easily quote

2. Think like an AI, not just a search engine

AI tools prefer clean, structured content that answers questions quickly. If your blog post includes jargon-heavy copy, long intros, or buried insights, it might be skipped over by generative tools. Instead:

• Use clear H2s that reflect common user queries
• Include TL;DRs or executive summaries and actionable insights
• Make your data sources and claims easy to cite

3. Build a strong SEO foundation

Start with the basics. Technical SEO still matters. Make sure your site is fast, mobile-friendly, and easy for both search engines and AI crawlers to index.

• Identify high-intent, industry-specific keywords that align with your audience’s pain points.
• Create pillar pages and supporting content to establish topical authority.

4. Align with the B2B SaaS buyer’s journey

B2B SaaS buyers often engage in extensive research before making purchasing decisions. Your integrated GEO-SEO strategy should address all stages of the buyer’s journey:

• For TOFU stage, write educational blog posts with concise answers to common questions.
• For MOFU stage, create comparison guides, case studies, and webinars that can be cited by AI tools when users search for solution evaluations.
• For BOFU stage, optimize product pages with structured data and clear CTAs for visibility when users are ready to convert.

5. Use trusted platforms for publishing

AI models favor high-authority, crawlable content. Publishing in gated PDFs or complex JavaScript frameworks can limit visibility. Stick to SEO-friendly platforms like your blog or help center, and keep things indexable.

• Publish authoritative content on your own website but also distribute it through trusted third-party platforms like LinkedIn or industry-specific blogs.
• These platforms are often crawled by AI systems, increasing the likelihood of being referenced in generative responses

Related: What is B2B SaaS marketing?

Tools and platforms supporting GEO

Generative Engine Optimization is still evolving, but a few platforms and tools are already playing a big role in how content is discovered, cited, and summarized by AI. For B2B SaaS teams looking to get ahead, here’s where to focus:

Artificial Intelligence search platforms to watch

These are the main platforms where your content can appear in AI-generated responses:

• ChatGPT (with Browsing & Plugins): OpenAI’s ChatGPT can now pull live data with browsing enabled. If your site is crawlable and structured well, it can be cited as a source.
• Perplexity AI: This AI search engine is designed to summarize and cite multiple sources directly. It heavily relies on clear, authoritative content, especially from SaaS blogs, docs, and help centers.
• Google SGE (Search Generative Experience): Google is rolling out AI-generated summaries at the top of search results. Sites with clear answers, structured data, and strong topical relevance are more likely to be referenced.
• You.com and Bing Chat: These AI-assisted tools also pull from live web content, especially when it’s well-structured and easy to quote.

Tools that can help you optimize for GEO

While there’s no all-in-one GEO toolkit yet, several tools can help you track visibility and improve your content:

• SparkToro: Great for identifying where your audience is spending time and which authoritative and credible sources they trust and useful for guiding content strategy and building topical authority.
• AlsoAsked & AnswerThePublic: Use these to surface real questions your audience is asking, which can be turned into answer-focused content that works for GEO and SEO.
• Surfer SEO or Clearscope: These help optimize for readability, clarity, and keyword use. All these are important for getting picked up by generative engines.
• Google Search Console: Still useful for tracking traditional performance, and it can reveal which pages are primed for GEO tweaks (high impressions but low clicks, for example).

Key takeaways

• GEO vs. SEO: GEO focuses on optimizing content for AI-driven platforms like ChatGPT, while traditional SEO targets search engine rankings.
• Top GEO strategies: Focus on credible, well-structured content with clear headers, summaries, and FAQs.
• B2B SaaS strategy: Combine SEO and GEO by building a strong SEO foundation and then adding GEO-friendly elements to your content.
• Tools & platforms: Focus on AI platforms like ChatGPT, Perplexity AI, and Google SGE, and use tools like SparkToro and Surfer SEO to optimize content.

Conclusion

The rise of AI-driven search necessitates a shift towards GEO. It focuses on optimizing content for AI platforms by ensuring it is credible, well-structured, and easily accessible. While traditional SEO remains important, integrating GEO strategies is important to maintain visibility and enhance credibility.

If you’re struggling to get SaaS leads with data-driven content? Let Quoleady handle your content strategy, creation, and promotion. Book a call today!

FAQs

What is the difference between SEO and GEO?

SEO aims to focus on ranking in search engine results pages (SERPs), while GEO makes your content accessible and quotable by AI tools that generate answers, like ChatGPT or Perplexity.

What does GEO mean in SEO?

GEO stands for Generative Engine Optimization. It’s an approach that complements SEO by optimizing content for visibility in AI-generated responses instead of just traditional search rankings.

Will GEO replace traditional organic search strategies?

No, GEO won’t replace SEO but will work alongside it. As Artificial Intelligence tools become more common, brands need to optimize for both GEO and SEO to increase their online visibility.

What type of content quality is most likely to be cited by AI tools?

Clear, factual, and well-structured content performs best. AI tools prefer concise answers, proper formatting (like headers and bullet points), and sources that are easy to reference.

Can GEO and SEO work together in a single content strategy?

Yes. You can create SEO-optimized content and enhance it with GEO-friendly elements like summaries, structured answers, and clean formatting to cover both types of search.

The post SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It) appeared first on Quoleady.

Think of it as a guide written for machines. While traditional websites are built with humans in mind, llm-txt-files give AI a cleaner, clearer version of your most important pages. It helps models pick up context faster, reduce confusion, and get the right information in front of the right people.

What is LLMs.txt?

LLMs.txt is a new proposed web standard designed to help large language models (LLMs)—like ChatGPT, Claude, or Gemini—easily understand your website by providing detailed information about its most important pages.

Think of it like a custom guidebook for AI. While robots.txt controls what bots can crawl and sitemap.xml lists your pages, LLMs.txt tells AI which pages actually matter, and why. It’s a human-readable and AI-friendly Markdown file that lives at the root of your website.

For SaaS companies and content-heavy websites, implementing LLMs.txt can improve AI visibility, drive better citations, and improve user engagement.

Let’s explore how LLMs.txt works and why it’s becoming an important part of modern SEO strategies, particularly with the rise of AI technologies.

Read also: How to Get Your Brand Quoted by ChatGPT and other AI

Why LLM matters for SEO

As AI-powered search engines become more important. LLMs.txt is turning into a key tool for helping websites work better with large language models (LLMs). Here’s why llms.txt files matter for SEO strategies:

• Better visibility in AI search: Guides AI models to prioritize your key information, increasing the chances of being referenced in AI-generated responses.
• Ensures accurate AI citations: Helps control what AI says about your business by highlighting the relevant information for both AI systems and human readers.
• Works alongside traditional SEO: Complements robots.txt and sitemaps by directing AI models to your most valuable pages and acting as an additional index for machine-readable content.
• Easy to implement: It’s just a simple Markdown file you upload to your site, and you can even include links to additional resources for context.
• Drives more referral traffic: Boosts AI citations, leading to more organic visits from AI-generated answers.
• Future-proofs your website: Prepares your site for the rise of AI-powered search, giving you a competitive edge.

Read also: LLMs vs SEO: What’s Changing in Search Discovery

How large language models (LLMS) work

LLMs.txt gives AI a simplified, structured version of your most important content. It highlights your top pages and provides short descriptions for each, helping models utilize web content more effectively—much like how search engines crawl and interpret websites. Here’s a simplified breakdown of how large language models work:

• Trained on massive data sets: LLMs process textual data from books, websites, and other sources to learn language patterns and utilize web content effectively.
• Use deep learning & neural networks: They rely on generative pre-trained transformer models (like GPT and Claude) to predict and generate text based on context, requiring a deep understanding of programming languages and structured HTML pages.
• Understand context & meaning: By analyzing sentence structures, relationships between words, and user prompts, LLMs generate relevant responses—especially when guided by a properly formatted txt file like LLMs.txt placed at your site’s root path.
• Limitations & token vocabulary constraints: LLMs have a fixed memory (token limit), meaning they can’t process unlimited data in one go and prioritize the most relevant indexable pages. This makes it even more important to highlight the following sections clearly within your content.
• Continuously improve with human feedback: Models refine their outputs through reinforcement learning, user interactions, and training data.
• Generate content, summarize, and analyze: LLMs can generate human-like text and create articles, answer questions, translate text, and extract key insights from complex tasks. This content creation is changing many business processes and introduces new dimensions to search engine optimization.

Understanding how LLMs work helps you optimize web content for AI-driven search, ensuring AI automatically generates relevant responses and extracts the right context from your site.

Related: SEO vs GEO: Why Your Content Might Be Invisible in AI Search (And How to Fix It)

How to optimize your content for AI readability

Google, ChatGPT, and AI search engines extract, summarize, and display web content based on structure, clarity, and format. If you’re still not appearing in AI results, then you need to optimize your website for AI readability—making sure your important information is easy for models to find and understand—using the tips mentioned below:

• Clear headings & structure: Use clear headings like H1, H2, H3, and include bullet points to organize content, especially if your site contains complex HTML. This helps AI assistants easily navigate and interpret your pages.
• AI-readable tables: For comparisons, use structured tables that provide additional context and make it easier for AI to extract key information from your content.
• FAQ sections & schema markup: Include FAQ sections and apply schema markup to improve your chances of being featured in Google search results and help AI index your site more effectively.
• Simple, direct language: Use simple and direct language instead of complex jargon to improve AI’s comprehension and boost your SEO performance.
• Internal linking: Internal linking helps AI understand the relationships between different pieces of content on your site, enhancing its ability to rank your pages accurately.
• Optimized metadata: Optimize your titles and descriptions in your metadata to significantly influence how AI algorithms rank and display your content in search results. For example, properly formatted content that follows these guidelines is more likely to be favored by AI-driven search tools.

See also: Structuring Web Pages for AI-First IndexingWhat’s inside an LLMs.txt file

Here’s what a basic structure looks like:

How to create your own LLMs.txt file

Setting up an LLMs.txt file is simple, but doing it right ensures AI models recognize and prioritize your most important information. This helps AI accurately index your site and surface the content that matters most. Follow these steps to create and implement your own LLMs.txt file:

1. Define your objective

Decide which content you want AI models to focus on. For example:

• A SaaS platform may highlight onboarding guides and product documents.
• A blog-focused site might spotlight cornerstone articles and explainers.
• An e-commerce brand could feature key product categories or buyer’s guides.

You might be interested: Schema & Structured Data for LLM Visibility: What Actually Helps?

2. Map your content

Categorize content into sections, such as “Product Information,” “Case Studies,” or “Help Docs.” Structure it in a way that makes it easy for AI models to process.

Make a list of high-value URLs. Skip pages like /login, /cart, /privacy-policy.

Example for a SaaS company:

• /docs/start
• /docs/api
• /tutorials/integrate-slack
• /pricing
• /case-studies/team-growth

Example for a content-heavy blog:

• /blog/how-to-use-ai-in-content-marketing
• /blog/seo-for-ai-search
• /resources/checklists/content-brief-template

3. Write it in Markdown

Use a simple Markdown-style format. Include links, short descriptions, and keywords for clarity. Structure the file like this: