How does Mindflow measure ChatGPT?
Mindflow measures ChatGPT by running it under fixed conditions and writing down what comes back, on the quarterly cycle set out on our methodology page. ChatGPT is in our counted set. We sample it directly, three runs per question, signed out, in a fresh session with no history, location set explicitly and recorded.
Why is ChatGPT the one AI surface you can partly verify yourself?
Because it sends visits, and visits land in your own analytics, where nobody has to take our word for the number. It also refers live traffic. Where a business is being recommended, referral visits show up in analytics, which makes this the one AI surface where the effect is partly independently observable.
What appears to move a ChatGPT recommendation?
Four signals, and we watch all four on every engagement: entity clarity, presence on the sources ChatGPT consults, specific content on your own domain, and review substance. The table below sets out what each one means. None of it comes from published platform documentation; it is what we see in our own runs.
| Signal | What we observe |
|---|---|
| Entity clarity first. | The same foundation as everything else, a business it cannot confidently identify is one it avoids naming. |
| Presence on the sources it consults. | Much of what it knows about your category comes from third-party surfaces rather than your site. |
| Specific, checkable content on your own domain. | Generic advice is uncitable because it is available everywhere; there is no reason to attribute it to you. |
| Review substance. | The substance of the reviews, not only the star average. |
What will Mindflow not claim about ChatGPT?
Two claims are missing from this chapter on purpose, and the first is this. That any technique guarantees a recommendation. The ranking logic is not published, it changes without notice, and anyone describing a reliable method is describing a product rather than an observation.
The second refusal is about evidence, and the number inside it comes from our own sampling rather than from any platform disclosure. We also will not claim a single screenshot proves anything. Around one in ten surfaced domains differs between identical runs minutes apart, which is why we run three times and report splits as unstable.
Rankings are never guaranteed. Anything we could not trace to a primary source is absent from this page, not estimated. The audit runs the same six layers described on the pricing page, and Share of Answer is scored quarterly.
Read in order, or jump
Twelve chapters in working order. Four form the spine, and the platform chapters at the end name two surfaces we leave unscored and explain the decision.
01 How AI search picks local businesses 02 Eligibility: can AI even see your site? 03 Robots.txt: block AI crawlers or allow them? 04 Measuring AI visibility honestly 05 Entity foundations for local businesses 06 Reviews as AI input 07 Citation surfaces: where AI engines look 08 Getting ChatGPT to recommend your business 09 Showing up in Perplexity 10 Showing up in Google AI Overviews 11 Showing up in Gemini 12 Copilot, and why we don't sample itSee where you actually stand
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