Definition
AI visibility tracking is repeated measurement of how often AI engines such as ChatGPT and Perplexity mention and cite a brand when answering buyer questions.
In practice
It is the measurement half of generative engine optimization (GEO). A tracker sends a fixed set of buyer prompts to each engine on a schedule, several times per prompt because answers vary from one run to the next, and records who was mentioned, in what order, and which sources were cited. The numbers worth keeping are the mention rate, share of voice against named competitors, and the domains cited when you were left out. The mistake is trusting a single screenshot. One answer is an anecdote; a rate over many answers, compared week to week, is a measurement you can act on.
Example
An agency tracks AI visibility for a local accounting firm. Every Monday an agent asks ChatGPT, Perplexity, and Gemini 15 questions a prospective client might ask, three times each, and logs which firms are named. The firm appears in 12 percent of answers; two competitors appear far more often, mostly because the engines keep citing a regional directory. The agency's next job is getting the firm properly listed there.
Common questions about AI visibility tracking
How often should you run AI visibility tracking?
Weekly is a sensible cadence for AI visibility tracking: often enough to spot a change, rarely enough that noise does not dominate. Ask each prompt at least three times per engine per run, keep the prompt set fixed, and review the prompts and tracked models monthly so the numbers stay comparable over time.
Do tracked answers match what people see in the ChatGPT app?
Not exactly. AI visibility tracking usually queries engines through their APIs, which can differ from the consumer apps in model version, search behaviour, and personalization. Treat the tracked numbers as a consistent trend line rather than a replica of any one person's screen, and spot check the apps by hand now and then.
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