SaaS LLM Optimization

We use internal tracking tools and platforms like AIScope to monitor your presence in AI responses. We also manually test prompts relevant to your audience to understand how and when your brand appears – or doesn’t.

There’s a broad set of factors at play – LLMs rely on patterns in their training data, so direct control is limited. That said, one of the highest-impact levers is being mentioned in trusted, high-authority sources. These include listicles, expert roundups, media mentions, and well-structured blog content. Our work focuses on optimizing these signals so your brand is more likely to be referenced by LLMs when users ask relevant questions.

Yes – especially if you’re in a competitive niche. LLM visibility can be a shortcut to brand credibility and discovery, even before you’ve built up strong domain authority or traffic.

Both. We’ll review your existing content and optimize pages that are underperforming in LLMs. But we also recommend and create new pieces – including expert-driven content and strategic landing pages – when needed.

LLMO builds on traditional SEO but shifts the focus from ranking in Google to being included in AI-generated answers (like ChatGPT, Perplexity, or Google’s AI Overviews). Ranking high in Google helps, but LLMs often pull information based on source credibility, content structure, and how frequently a brand is mentioned across trusted pages – not just keyword rankings.

So while SEO focuses on SERP visibility, LLMO is about influencing what content and brands LLMs “learn from” and surface in responses. The two strategies overlap, but LLMO requires a broader content and distribution mindset – one that includes listicle placements, digital PR, and technical signals LLMs are likely to pick up on.

In most cases, clients start seeing improvement in AI citations and visibility within 1–3 months, especially when we’re optimizing high-impact pages and listicle presence early on.

No one can guarantee inclusion in AI answers. But we increase your chances significantly by working on the types of sources and signals LLMs tend to rely on.