Comparisons
AI Visibility and an Editorial Plan from the Features Page
RankAI's features page is a collection point, not a decision engine. Here is how to use it to connect what Google reads with what AI assistants answer, and turn the gap into an editorial plan.
On RankAI's features page, the data sits side by side: what Google reads on a page, and what comes up in answers from ChatGPT, Perplexity, Gemini, Grok and Google AI. The page is a collection point, not a decision engine. It does not automatically link the two readings. The method below is a proposal from RankAI: you put the two observations next to each other, and you decide what to write or rewrite.
Why the two readings stay separate
AI visibility and an editorial plan only connect if the observations stay distinct. RankAI's public pages name ChatGPT, Perplexity, Gemini, Grok and Google AI. Claude and Grok are measured only after you connect them. Gemini and Google AI Overviews are not the same observation: read them as two different outcomes, not one signal. An API answer is not a person's screen, so what we see in responses does not match what a user sees in the browser.
The method in three steps, with checkable criteria
- Pick a page and record the Google reading. Take a URL from the site and write down what Google reads on it: title, main content, named entities. Checkable criterion: the page record lists the same elements you find when you open the URL, with nothing added.
- Ask the assistants about the same topic. Write a question a reader would ask on that subject and collect the answers from ChatGPT, Perplexity, Gemini, Grok and Google AI. Checkable criterion: for each assistant, you keep the exact question and the answer you got, so you can check it again.
- Compare and decide the editorial change. Put the Google reading next to the AI answers: if an entity or concept appears in Google but not in the answers, or the other way round, that is the point to address. Checkable criterion: every change to the editorial plan cites the starting page and the answer that motivated it.
A filled-in row on the features page
Picture a row on the features page filled in like this: page /guida-alla-visibilit-ai; Google reading = title "Visibilità AI", main section on assistant answers, entities ChatGPT and Perplexity; ChatGPT answer = mentions Perplexity but not Gemini; Perplexity answer = cites Google AI Overviews as a separate observation; Gemini answer = no reference to the page. From this row the editorial plan draws a single action: add a paragraph to the page that separates Gemini from Google AI Overviews, because the distinction is missing in the Google reading and in the answers. The row shows the data side by side, but the decision stays with us: the features page does not produce the link between the two readings on its own.
What this piece adds is a working proposal from RankAI: use the features page as a collection point, not as a decision engine. In practice, the filled-in row in the example works as a reminder: if the Google reading and the AI answers disagree on an entity, that gap becomes an item in the editorial plan. The test for accepting it is simple: it must be checkable, meaning it must cite the page, the question asked and the answer received. That way AI visibility stays a data point to compare with what Google reads, not an impression. The method guarantees no rankings and no results: it only helps you order the observations before you write.
A checkable limit
The limit is in the record: RankAI's public pages name ChatGPT, Perplexity, Gemini, Grok and Google AI; Claude and Grok are measured only after you connect them; Gemini and Google AI Overviews are not the same observation. So we cannot present the features page as a flow that automatically links the Google reading and the AI answers: the link is our method, not a documented feature. For details on the page, see the features page.