AI Visibility

Where classic search visibility is a position for a keyword, AI visibility is presence inside a generated answer, and it behaves differently. The same question can produce different brands from one run to the next, from one engine to the next, and from one user to the next, because answers are synthesized on the fly rather than served from a fixed index position.

AI visibility has three layers. First, retrieval: were your pages fetched into the model's context at all? Second, citation: did the answer actually name or link you? Third, prominence: are you in the first sentence and the recommendation, or a footnote at the bottom? A page can be retrieved and still lose if it isn't quotable enough to be cited.

This is why AI visibility is hard to just "look at." There is no single results page to check. Answers are personalized, non-deterministic, and spread across ChatGPT, Perplexity, Gemini, Claude, Copilot and Google AI Overviews. Measuring it means sampling many buyer prompts across those engines, repeatedly, and trending the results, not eyeballing one answer once.

For a store, AI visibility is a demand channel. When a buyer asks an engine what to buy and it names three brands, the brands it skips are out of the consideration set before a single click happens. Losing there is losing earlier, and more quietly, than losing a Google ranking.

Because answers are generated per question and per product, visibility has to be understood at the level of specific buyer prompts and specific SKUs, not just the site as a whole. That granularity is also the opportunity: it tells you exactly which pages to fix.

Illustrative scenario: a store ranks on page one of Google for "minimalist running shoes," but when a buyer asks an engine "what are the best minimalist running shoes," the answer names three other brands. Strong search visibility, zero AI visibility for that intent, a gap you can only see by checking the answer, not the ranking.