How we track AI mentions: our methodology in full

This page is the public version of how we measure AI visibility, in full: what gets checked, on which platforms, how findings are reported, and the conclusions we refuse to draw. It exists because almost nobody in this industry publishes their method, and unpublished methods are how junk numbers survive. Two instruments are documented here: the free scan (a dated snapshot) and continuous tracking (the ongoing measurement behind your dashboard).

We measure AI visibility by asking AI assistants the questions customers actually ask, recording exactly what each one answers, and reporting whether a business gets named. The scan does this once, as a snapshot across 10 prompts. Continuous tracking does it on an ongoing basis across a business’s tracked prompts. Neither produces a made-up composite score, and neither claims to prove causation.

The free scan: a dated snapshot

The scan’s method, as confirmed in our methodology document (version 1.1; the method was confirmed at 1.0, and 1.1 is a vocabulary-only revision):

Prompt set. 10 prompts per scan, phrased the way customers ask, covering the business’s primary service and city. Phrasings vary by industry. Why 10 and not 1 is a real design decision: one question is an anecdote, a designed sample is a measurement.

Platforms queried. ChatGPT, Google’s AI answers (AI Overviews / AI Mode), Perplexity, and Gemini.

What gets recorded. Verbatim answer text plus a screenshot, dated, with location context noted. A scan finding you can’t trace to a recorded answer doesn’t go in the report.

The seven signals. The scan reviews the business against seven signals: Your Reviews, Your Google Business Profile, Your Website’s Readability to AI, Your Answers, Your Citations and Directories, What Others Say About You, and Consistency. Each has a published definition, and the signal names in every scan report match these exactly.

Graded findings, no composite score. Each signal is reported as Solid, Needs work, or Missing, with the specific supporting observation under each grade. We don’t produce a 0-to-100 score because an invented composite implies measurement precision the underlying checks don’t have. Graded findings with visible support are more honest and, in practice, more convincing.

Continuous tracking: what your dashboard measures

Continuous tracking applies the same recording discipline to an ongoing panel instead of a one-time snapshot.

What gets checked. A business’s tracked prompts: the questions customers ask AI assistants, tracked so you can see whether your business gets mentioned in the answers. Core tracked prompts are the questions we already know customers ask in the business’s industry; custom prompts are questions the customer picks. Plan tier sets the prompt count.

Platforms covered. ChatGPT, Perplexity, Claude, and Gemini.

How often. Every tracked prompt is checked daily. Daily checking is what turns single answers into trends: one day’s answer is a sample, a month of them is a pattern you can act on.

What counts as a mention. The business named in the answer text itself. A citation of a page about the business without the name in the answer is recorded as a citation, not a mention; the difference between the two outcomes is part of why both get tracked. The derived numbers (mention rate, citation rate, share of voice) are defined in their own reference, which this method feeds.

Trends, not single results. AI answers vary between askings, so tracking reports movement across many samples over time, and single-answer changes are treated as noise until they repeat.

The boundary we keep on purpose

Measurement is explainable, and this page explains it. The reasoning behind playbook recommendations is not published: here’s exactly what we measure, and the prioritization logic is ours. We’d rather state that boundary openly than pretend the whole system is public.

What we refuse to conclude

These clauses appear in every scan report, and they bind everything we publish:

• Answers vary by model, location, phrasing, and week. A scan is a dated snapshot, not a permanent state.
• Where we state that a signal influences AI answers, the support is either observed citation behavior in recorded answers or a named source. Correlation is labeled as correlation.
• The scan does not measure rankings, traffic, or anything requiring analytics access. It does not predict outcomes or promise timelines.

The same discipline applies to tracking: we report whether you’re named, we don’t promise that any action produces any mention, and anyone in this market who does promise that is guaranteeing someone else’s output.

Versioning

The scan methodology is versioned (currently 1.1; the method was confirmed 2026-07-16 at 1.0); material changes bump the version, and this page updates in the same cycle with its changelog visible below. If a number of ours is quoted somewhere and doesn’t match this page, this page is current.