See if you get quoted
before you publish the page.

For every engine, ChatGPT, Claude, Perplexity, and Gemini, Ranko asks each of the five most relevant questions in a fresh session, with no framing that would bias the engine toward the article. The engine answers naturally, drawing on whichever sources it would use for a real user. The simulator then checks whether the article was quoted, captures the passage lifted, identifies the competitor picked if not, and records the result. That is twenty fresh, independent sessions per article.

From the same AI Question Mining stream that drives the rest of Ranko. For every article, Ranko identifies the five questions real users are most likely to ask about the topic, pulled from actual ChatGPT, Claude, Perplexity, and Gemini conversation data. They are not made up or standardised, but the real phrasings people type right now, so the simulation tests genuine conditions. The team can add custom questions too.

The article counts as quoted when the engine uses the page as one of its sources. That might be a named source, an inline reference, a footnote, or the bibliography, depending on how the engine surfaces it. The simulator detects all as quotes. Cases where the engine refers to the brand without using the page are recorded separately as brand mentions: a mention is awareness, a quote is the engine reaching for your content.

Use the per question, per engine, per competitor breakdown to find where the article loses and why. A blanket not quoted verdict is rare; usually it wins on at least one engine. The team looks at the questions it lost, identifies the competitors picked, studies the passages they reached for, and refines those sections. The simulator tells you who beat you, on which questions, with which content, so you improve precisely.

Yes, multi-variation simulation is supported and most experienced teams use it. The team creates two or more versions of the same article, with different opening summaries, heading shapes, or FAQ blocks, and runs all in parallel. The results come back side by side, showing which earns more quotes on which engines. The team picks the winner, or combines the best moves. A quote focused A/B test, run before publish.

Opportunity Score measures the topic. AI Mention Simulator measures the article. Opportunity Score tells the team how open the door is on a topic, useful for prioritising what to write. Mention Simulator tells whether a draft will walk through that door, useful for deciding if it is ready. Most teams use both: Opportunity Score to pick topics with room to win, Mention Simulator to verify the article wins first.