AI search visibility: improving what AI assistants read about you

What AI search visibility means

AI search visibility is whether assistants and AI search features, such as ChatGPT, Perplexity, Gemini and Google's AI Overviews, mention your brand when people ask about your category, describe it accurately, and cite your content. These systems draw on what they learned in training and, increasingly, on pages they retrieve at the time of the question. Either way, they rely on what credible sources say about you.

A brand described consistently across trusted publications is easier for a model to describe confidently. A brand that barely appears, or appears with conflicting descriptions, is more likely to be omitted, hedged or confused with a competitor.

What the service involves

• Visibility audit. We ask the major assistants a defined set of questions buyers in your category would ask, and record whether you appear, how you are described, which sources are cited and which competitors appear instead.
• Mention building. Editorial placements, digital PR coverage and expert commentary on the publications those systems draw on.
• Consistent brand description. Making sure third-party descriptions say what you do, who you serve and how you differ, in the same terms.
• Entity hygiene. Clear, consistent business information on your own site and profiles: what the company is, what it offers, who runs it. Often unglamorous, frequently wrong somewhere.
• Citable content guidance. Recommendations on which of your pages could be cited, such as clear definitions, comparisons and original data, and how to make them easier to quote.
• Re-measurement. Periodic re-runs of the audit questions, reported honestly, including where answers are still wrong.

What is established, emerging and unproven

AI search is new enough that confident claims outrun the evidence. This is how we separate them:

Terms like GEO (generative engine optimisation), AEO (answer engine optimisation) and LLMO describe overlapping parts of the same goal; the label matters less than whether the work rests on established practice.

What we don't promise

No one can guarantee how an AI system describes your brand or whether it cites you. Model behaviour is probabilistic, providers change their systems frequently, and training data is not disclosed. The same applies to technical shortcuts: schema markup, an llms.txt file or a particular page format may help some systems interpret your site, but none of them guarantees a mention. What can be influenced is the evidence: how often, how accurately and where your brand is discussed.

How it relates to link building and SEO

The publications worth being mentioned in for AI answers are usually the same ones worth earning links from, so this work overlaps heavily with editorial link building. The differences are in emphasis: unlinked mentions matter more, consistency of description matters more, and success is measured in AI answers as well as rankings.

Who it suits

• SaaS and B2B brands in categories where buyers ask assistants for shortlists before they search.
• Brands with an outdated or confused public record, such as old positioning, a rebrand or a name shared with another company.
• Challengers whose competitors dominate AI recommendations because they have far more third-party coverage.

Common mistakes

• Treating it as a technical fix. Markup and files on your own site cannot replace third-party evidence.
• Measuring with one prompt. Answers vary between sessions and systems. A fixed question set, repeated over time, gives a far more reliable picture.
• Chasing volume over consistency. Many mentions that describe you differently can make answers less accurate, not more.

Questions