Method
How AI visibility is measured across Google and AI engines
AI visibility is not one number. It is a separate observation of how each system reaches and reports a site's content, kept source by source.
If you run a site and want to understand how AI visibility is measured, start with a concrete point: the same page can be read by Google one way and returned by ChatGPT, Perplexity, Gemini, Grok or Google AI another way. AI visibility is therefore not a single number. It is the separate observation of how each system reaches and reports the site's content. RankAI organizes that observation by source, so that Google's reading and the AI engines' answers are not added together into one indistinct indicator.
Why a site is read differently from system to system
Google indexes and organizes pages according to its own search model. The AI engines we name — ChatGPT, Perplexity, Gemini, Grok and Google AI — produce answers, not results pages. That means AI visibility has to be measured as the site's presence in answers, not as a position in a ranking. The in-depth page describes how this works in general; here we focus on what to observe and how RankAI keeps sources separate.
One observation, one source
The operating rule is simple: every system is a source in its own right. Gemini and Google AI Overviews are not the same observation, even though both belong to the Google ecosystem. Treating them as one data point would distort the measurement. Likewise, an API response is not a person's screen: what a system returns through a programmatic interface does not match what a user sees.
How RankAI organizes the observation of AI visibility
RankAI starts from the site's public pages and relates them to the sources we name: ChatGPT, Perplexity, Gemini, Grok and Google AI. Claude and Grok are measured only after you connect them, so their observation is not available before that step. This distinction is a stated limit of the method, not a technical detail.
The contribution of this piece is an operating method proposed by RankAI, not a universal standard: read AI visibility as a matrix of sources, not as a single score. In practice, for each public page it helps to build one row per source — Google, ChatGPT, Perplexity, Gemini, Grok, Google AI — and one column per thing you want to check: whether the page can be reached, whether the content is reported, whether the context stays correct. The matrix prevents two common mistakes: adding different sources into a single indicator, and attributing to one engine what belongs to another. Because Gemini and Google AI Overviews are not the same observation, they belong on separate rows; because an API response is not a person's screen, the observation should be labeled for what it is. The method does not promise rankings: it makes comparable what is often confused today.
Concrete steps for observing AI visibility
- List the public pages to observe. Checkable criterion: every URL on the list can be reached without a login and returns the expected content.
- Give each page one row per source. Checkable criterion: Google, ChatGPT, Perplexity, Gemini, Grok and Google AI appear as separate entries, with no merging of Gemini and Google AI Overviews.
- Note how the observation was made. Checkable criterion: for each reading, state whether it comes from an API response or from a screen, because the two do not match.
- Mark the sources not yet connected. Checkable criterion: Claude and Grok count as measured only after connection; before that step their box stays empty, not estimated.
A limit to keep in mind
RankAI observes the sources that public pages name and organizes results by source, but it does not turn that observation into a guarantee of presence or ranking in AI answers. The measurement stays tied to what the systems return at the moment of observation and to the sources actually connected.
For the full picture of how a site is read and how RankAI organizes the observation, see the in-depth page.