Three steps turn a moving, per-query answer into a fixed, auditable record you can put on a product page. Here is exactly what happens — and what it does not claim.
We ask neutral buying questions for a real shopping need — many phrasings, no brand names — across ChatGPT, Perplexity and Gemini in their web-grounded modes. For each answer we record which products are named, and how often.
A count on its own proves nothing, so we test it. Frequency is compared to an honest per-intent null baseline (the chance level for that need) with a conservative bootstrap interval, and we check source diversity so a single affiliate can't manufacture a signal.
A record is Verified only when a product clears its baseline across engines, on fresh evidence, and holds over time. Full criteria (C1–C7) are on the methodology page.
When a product is consistently recommended for a real need, it earns a category record. You show it two ways, and both link back to the evidence:
aiproofhub.com/verify — the sealed record and the raw answers behind it, reachable from every badge. Here is what one looks like (a sample, with an invented brand).See the badge on a live demo store → — a fictional Shopify shop.
Here is the badge as it lands in a store — quiet, monochrome, and always carrying its category, denominator, engines, date and evidence link:
"Frequently recommended by AI for office" — a measured frequency, on a date, across named engines.
It does not mean "the best", "AI's #1 pick", or that any provider endorses the product. It is not a promise of sales. If the signal drops below its baseline, the record is revoked and the badge stops showing. If its evidence simply ages past its 30-day window, the badge shows its date instead of a current claim — it never turns negative.