SearchChampHow we measure
Which engines are queried, how, and where the honest limits are.
The How we measure link in the AI Visibility header opens the methodology page: what's collected, which models are queried, how confident a number is, and where the limits are.
Three collection methods
Not every engine is reached the same way, which matters when you compare a number across engines.
| Engine | Collection method |
|---|---|
| ChatGPT | Assistant answer via provider |
| Gemini | Assistant answer via provider |
| Google AI Mode | Search-results based |
| Microsoft Copilot | Search-results based (Bing organic-SERP-grounded, not the conversational Copilot answer) |
| Google AI Overviews | Search-results based |
- Assistant answer — your prompt goes to the provider's own API and SearchChamp reads the answer and its citations. For some assistants that runs through a third-party data provider against the live assistant; the answer still comes from the assistant itself.
- Search-results based — some AI surfaces have no public conversational API, so they're collected via a third-party search-results provider rather than by scraping search pages.
Search-results-based engines are queried against your site's configured market (country + language), so your visibility reflects the market you actually serve. Where a market can't be determined, results fall back to the United States and are labelled as such.
Shopping
Shopping results are read from the answer already collected for each engine — never a second request — so shopping visibility costs nothing extra against your prompt allowance. An engine is only measured for shopping when its answer genuinely contains a structured product block; where it doesn't, the page says so rather than reporting an empty result as a finding. See AI shopping visibility.
Confidence
Numbers carry their sample size and margin (e.g. n=216 · ±6pp). A small sample is labelled directional rather than presented as precise — read those as a direction of travel, not a measurement.