Questions about AI Visibility

AI Visibility is how often, how accurately, and how favourably a brand is surfaced when people ask AI assistants for recommendations. Instead of ranking on a page of blue links, you are either named inside the answer or you are not mentioned at all.

SEO optimises for ranked lists of documents. AI Visibility optimises for synthesised answers. An assistant reads across many sources, forms an internal view of a category, and then names a handful of options. You can rank on page one of Google and still be absent from that shortlist.

ChatGPT, Claude, Gemini, Perplexity, and Google's AI-generated search answers. We use the assistants and models your buyers realistically use, in both browsing and non-browsing modes where relevant.

No. The audit is run manually by a human analyst. Automated scrapes miss the nuance of how an assistant frames a category, which competitor it defaults to, and what evidence it cites. We read the answers, not just count the mentions.

Typically three to five working days from the moment we have your website, competitor list, and the recommendation intent you care about.

B2B SaaS companies, AI tools, and agencies whose buyers research with AI assistants before they ever reach a search engine or a sales call.

A written audit covering your current visibility across assistants, the prompts we tested, which competitors are recommended instead of you, why we believe that happens, and a prioritised list of changes to make first.

You keep the findings and can act on them yourself. If you want ongoing help, we offer monitoring of how your visibility changes over time and hands-on optimisation work. There is no obligation.

Start by measuring what assistants say today, then make your positioning legible to a model: one clear use case per page, consistent facts across every source, and presence in the comparison articles, review sites, directories and documentation that answers are built from. Recommendation follows evidence, not persuasion.

Fix a prompt set for your category, run it across the major assistants, and track four numbers plus one qualitative read: mention rate, share of voice against named competitors, accuracy of the description, intent coverage, and the role you are given in the answer. Re-run the same prompts on a schedule so the numbers are comparable.

Usually because the sources an assistant trusts describe your category without describing you: comparison articles, community threads, documentation, reviews, and directories. If those sources do not clearly connect your brand to a specific use case, the model has nothing to recommend you for.

No, and you should be sceptical of anyone who does. Model behaviour changes and no one controls it. What we can do is make the evidence about your brand clearer, more consistent, and easier for a model to use — and measure whether that moves the answers.