What is AI Search Visibility?

Buyer research is shifting from search results to AI answers. When a buyer asks ChatGPT "what's the best tool for X", the answer is a synthesized shortlist — three to five names, framed and ranked by the model. There's no page two. Brands in the shortlist inherit the buyer's trust in the assistant; brands outside it are invisible at the exact moment of consideration.

Unlike search rankings, this happens out of sight: you cannot see what AI engines tell your buyers unless you run the prompts yourself, engine by engine, day by day.

• Mentions — the engine names your brand in a relevant answer.
• Position — where you sit in list-style answers; top-3 drives most consideration.
• Recommendation — the engine endorses you, not just lists you.
• Sentiment — how the engine frames you: leader, budget option, legacy tool.
• Citations — the engine uses your pages as sources for its claims.
• Share of voice — all of the above, relative to competitors.

Two mechanisms. Model knowledge: what the engine learned about your category in training — moved by long-term presence in the sources models train on. Retrieval: what the engine fetches at answer time in grounded mode — moved by crawlable, structured, trustworthy content, and by presence in the third-party sources engines cite. The two move on different clocks, which is why serious measurement tracks them separately.

Fix a bank of real buyer prompts, run them across engines on a schedule, and compute visibility rate, position, recommendation rate, sentiment, and share of voice per engine — keeping the verbatim answers as evidence. The full method is in How to Measure AI Visibility, and Seeqly automates it across five engines daily.

Cover your buyer questions with structured, direct pages; earn presence in the sources engines cite; keep entity facts consistent; publish original data worth quoting. Start with the five-step framework.