What is query fan-out? The searches behind every AI answer

Query fan-out is the step where an AI engine turns one question into several web searches, reads what they return, and writes its answer from those pages. The brands it names come from the pages those searches found, so the searches are where visibility is won or lost.

In short

• Engines with web search on don’t search your buyer’s exact words. They break the question into sub-questions and search each one.
• Google says both AI Overviews and AI Mode may use this technique, and the Gemini API returns the searches it ran with every grounded answer.
• If no page of yours ranks for the fanned-out searches, the engine never reads you, however good your page for the original question is.
• Recording the searches per answer shows which sub-questions to write for, and which sites the engines read instead.

Where does the term come from?

The term comes from Google. Its guidance for site owners says that “both AI Overviews and AI Mode may use a ‘query fan-out’ technique”, issuing multiple related searches across subtopics and data sources to build a response.

The behaviour isn’t limited to Google’s search products. When the Gemini API answers with Google Search grounding, the response carries the list of searches the model ran (webSearchQueries) alongside the sources it cited. Other engines with web search tools report their searches in similar ways, which is what makes fan-out measurable.

What does a fanned-out question look like?

A fanned-out question looks like a research plan. A buyer asks one thing; the engine searches several narrower things it thinks the answer depends on.

The buyer asks	Searches an engine might run
best project management software for agencies	agency project management software 2026; project management for agencies reviews; PM tool client portal agencies
SOC 2 compliant project management tool	SOC 2 project management software; project management SAML SSO; GDPR project management data residency

The examples are illustrative. The real searches vary by engine, by day and by phrasing, which is why they need to be sampled like the answers themselves.

Why does fan-out decide which brands get named?

Fan-out decides which pages the engine reads, and an engine can only name a brand that appears in, or is supported by, what it read. If the searches for “agency project management software 2026” return two review sites and a competitor’s comparison page, those three pages shape the answer.

That has two consequences:

1. Your page for the buyer’s question may never be read. It can rank well for the original phrasing and still miss every fanned-out search.
2. Third-party pages carry weight. Review sites, comparison pages and forums often rank for the sub-questions, so a mention there can matter more than another page on your own site.

How do you see the searches an engine ran?

You see them by asking the engine through its API with web search on and keeping the search data it returns with each answer. In the Gemini API this is the grounding metadata; other engines’ web search tools return their own equivalent.

AmpliRank keeps the searches for every answer, groups the near-duplicates into buyer questions, and ranks them: how often the engines searched each one, and who they named when they did. A sub-question searched in most answers, where competitors are named and you aren’t, is the clearest content gap you will find.

What should you change once you know the searches?

Write for the sub-questions, not only the head question. In practice:

• Answer the sub-question on a page that can rank for it. A pricing question needs a page that states prices; a security question needs a page that states your certifications plainly.
• Earn mentions on the sources the engines read. If the same review site appears in most answers that leave you out, a listing there is worth more than another blog post.
• Measure again. Fan-out changes as engines change, so the same questions, asked on a schedule, show whether the searches and the named brands moved.

Google’s own guidance adds that there are no special technical requirements for appearing in AI Overviews or AI Mode beyond being indexed and eligible for a snippet. The work is ordinary content work, aimed at the right questions.

Where to start

Take your five most valuable buyer questions and look at the searches the engines run for them. Our product tour shows what that looks like on a sample run, and why one answer isn’t a measurement explains how many answers you need before acting on it.

Sources

• Google Search Central, AI features and your website, accessed 12 November 2026.
• Google AI for Developers, Grounding with Google Search, accessed 12 November 2026.