The method, in four questions
How AI recommendations really work
“GEO,” “AI visibility,” “AI SEO” — under the jargon, one question: when a customer asks AI who to hire, does it say your name? Here is what actually decides that, in plain terms.
01What is this, really?
When someone asks ChatGPT or Perplexity for a plumber, the AI composes an answer naming three or four businesses — and stops. There is no page two, no position eleven. You are in the answer or you are not. “AI visibility” is nothing more mysterious than how often that answer includes you.
02How does the platform decide?
Two machines produce answers. For current questions the AI searches the live web and composes from what comes back — and what comes back is mostly not your website. In our measurement of 100 local businesses, Yelp appeared in more answers than any other source; Perplexity leans on Reddit, Gemini reads state license registries, and the winning businesses were readable in both places — their own site and the directories. The second machine is the model's trained memory, which updates on the platform's schedule, not yours.
One more thing most owners don't know: about 8% of small-business sites block AI crawlers outright — which can restrict the named crawler’s access. This does not establish that every AI search system is unable to find the business; other crawlers, indexes and sources can still supply information.
03What can you actually affect?
The inputs to the live-search machine: a site the crawlers can read, an identity that agrees with itself everywhere (name, phone, hours, services), pages that actually answer the questions customers ask, and presence on the directories your trade's platforms read. None of it is instant — we measured the lag ourselves, and even a directly-submitted page takes days to enter the indexes the AI platforms search.
04What can't you affect?
There is no submission form and no way to buy placement — and no technique has a demonstrated, guaranteed effect on what an AI says. Anyone promising a specific AI answer is guessing. The answers are also stickier than the daily-chart industry implies: 77.5% of business/question/platform combinations were strictly always- or never-mentioned in the cited study’s repeat runs. That observation is specific to its sample and settings; it does not establish how every business changes over time or the effect of an intervention.
∴Which is why the honest tool is a measurement
If nobody can promise the outcome, the only thing worth paying for is knowing where you stand, what to change, and whether it worked. That is the whole product: we ask every question repeatedly on every platform — asked once, a miss could mean almost anything; asked fifteen times, it means something — publish the rate with its 95% range, and freeze your questions on the first run so a re-check measures a change in the answers, not a change in the questions. Moved or didn't, with the error bars showing.
The instrument itself, measured: sensitivity 85% (95% 79–90%) against an independent adjudicator across 60 businesses; platform failures excluded from rates rather than counted as absences; every published correction stays published. The full protocol ships inside every report, and the deep studies live on the research page.