AI visibility tracking.

AI visibility tracking is the practice of measuring how a brand or product appears in answers generated by large language model assistants — ChatGPT, Claude, Gemini, Perplexity, and others. It captures three things: whether the brand is mentioned in the answer text, whether the brand's domain is cited in the source list, and how the brand ranks against competitors that get named alongside it.

It is distinct from web analytics (which measures who reaches your site) and from traditional SEO (which measures position on a search engine results page). It sits one layer earlier in the funnel: the answer the buyer reads before they ever consider clicking through.

As of 2026, a meaningful share of B2B product discovery happens inside an AI chat rather than a Google query. Buyers ask "what's the best X for Y" and read a four-paragraph answer naming three vendors. The shortlist is set before any site is visited. If your brand isn't in that answer, you don't get evaluated.

The shift matters more than the SEO transition of the early 2000s because there's no second page. An AI answer typically names two to five options. Position eleven on Google was disappointing but recoverable. Absent from the answer is invisible.

A tracking tool maintains a set of prompts that represent the questions your buyers actually ask. It runs those prompts on a schedule against multiple AI providers, parses each response, and records who got mentioned, who got cited, and in what order. Done credibly, this means many runs per prompt per day — single-shot sampling is too noisy to be useful.

The output is a time series: your share of voice in this prompt set, broken down by provider, by persona, and by competitor. Good tools layer opportunity detection on top — surfacing the specific gaps where closing them would move the score the most.

Three questions separate signal from noise. First, how many runs per prompt per day — anything below five is likely to misreport movement as a trend. Second, do they distinguish mentions from citations — collapsing them hides which kind of work will fix the gap. Third, can you audit any number back to the raw model response — if not, the score is taken on faith.

Secondary questions: how many providers, do they support persona framing, do they generate the asset that closes the gap, and is the pricing usage-based or seat-based for the team size you actually need.

A credible AI visibility report tells you, for a given prompt set and persona, your share of voice today, what changed this week, which competitor moved against you, and the top three actions that would close the biggest gaps. Anything less is a dashboard, not a tool.