AI Assistants Are Searching Google on Your Behalf: How to Spot Fan-Out Queries in Search Console

TL;DR. A noticeable slice of the queries in our Google Search Console were not written by humans. They were long, prompt-shaped searches issued by AI assistants while they researched an answer for someone else. They earn impressions and almost no clicks, and they are the most useful rows in the whole export.

We pulled our Search Console export this week for a routine ranking review. We expected the usual list of two and three word searches. What we found instead was a second population of queries that read like someone talking to a chatbot, because that is exactly what they are. This post is about what those queries are, how to spot them in your own account, and what we changed on dataease.ai once we understood them.

What is an AI fan-out query?

An AI fan-out query is a search that an assistant such as ChatGPT, Perplexity or Google AI Mode sends to a search engine on a user's behalf. The assistant breaks the user's question into several sub-questions, runs each one, reads the results, and writes a single answer. Your page can be retrieved dozens of times without a person ever seeing a results page.

The mechanism matters because it changes who your reader is. When a person searches, they scan ten blue links and click one. When a model searches, it retrieves a set of pages, extracts the passages that answer its sub-question, and decides which sources deserve a citation. There is no click in the human sense. There is only a decision to cite you or to ignore you. We wrote about the retrieval side of this in what 335,000 AI crawler visits taught us. Fan-out queries are the other half of the same loop: the crawl is how the model learns your pages exist, and the fan-out search is how it finds them again at answer time.

The answer engine is the thing doing the searching. Google Search Console does not label these queries, and as far as we can tell it does not try to. They simply appear in the Queries report next to everything else, which is why most founders have never noticed them.

How do you recognise fan-out queries in Search Console?

Fan-out queries are long, usually eight words or more, and written as complete sentences with punctuation. Many carry a first-person role such as "i'm a brand insights analyst", explicit constraints such as "list some platforms and tell me why", or instruction prefixes such as "context: location: united states". Nobody types a search that way. Assistants do.

Here are four queries from our own export, quoted exactly as Search Console recorded them:

• "how do you measure your brand's visibility in ai assistants like chatgpt credibly? what methods exist and where are their limits?"
• "are there tools that measure citation rates in ai-generated content for brands?"
• "i'm a brand insights analyst. which vendors specialize in ai visibility measurement for enterprise brands?"
• "which content formats win the most ai citations for b2b saas?"

The most revealing one in the set was a query that began "context: location: united states (not for language). do not include location references in your response. question: how can i export ai brand visibility data via api or scheduled reports so i can feed it into a bi dashboard?" That is not a search. That is a system prompt that leaked into a search box. The assistant passed its own operating instructions to Google along with the user's question, and Google dutifully recorded the whole thing as a query and kept showing our pages for it.

Once you know the shape, the tells are consistent:

• Length. Human searches are mostly two to four words. Fan-out queries run to fifteen words and beyond, and the longest ones are full paragraphs.
• Framing. "I'm a marketing director at an industry group" or "as a data analyst, can you actually measure" are role statements that a person would never include when searching for themselves.
• Spelled-out constraints. "Keep your rationale in the context of data compliance readiness" is an instruction to a model, copied verbatim into the search.
• Prefixes. "context:", "question:", and "in 2026" appended to the end are artefacts of the prompt template the assistant used.
• Multiple questions in one string. A human asks one thing. A fan-out query often asks two, separated by a question mark, because the assistant is packing a sub-task into one search.

Why are impressions without clicks not a failure here?

Because the visitor was never going to click. A fan-out impression means an assistant retrieved your page as a candidate source for its answer. The outcome you want is a citation or a mention in that answer, not a session in your analytics. Judging these rows by click-through rate measures the wrong thing entirely.

Judged by click-through rate, the fan-out rows are the worst in our export: impressions, a handful of clicks, middling average positions. Read as human search, that is a page that Google half-likes and nobody wants. Read as model search, it is something else. It is a list of the exact research questions that assistants asked while deciding whether DataEase AI belonged in their answer.

One caution before reading too much into any single export. Search Console cannot tell you who or what issued a query, and traffic moves for many reasons at once. Google's August 2026 spam update, for example, landed inside our reporting window, and we make no attempt to separate its effect from anything else. What the fan-out rows do give you, regardless of totals, is the wording. The pages that collect these queries on our site are the definitional ones: answer engine optimization, AI brand visibility, and how to get cited by ChatGPT. Deep positions that no human scrolls to still earn impressions on those pages, which is consistent with something that retrieves dozens of results at a time and reads all of them.

What do fan-out queries reveal about how models phrase their research?

They show you the literal sub-questions an assistant asks before it answers a buyer, in the assistant's own words. Those phrasings are the best specification you will ever get for which questions your pages should answer directly, because the model retrieves against the sentence it wrote, not against the keyword you optimised for.

Look at the four examples again and notice what the models care about. They want measurement methods and their limits. They want to know whether a tool exists for a specific job. They want vendor lists segmented by company size. They want to know which content formats earn citations. Every one of those is a question we could answer better than most of the pages that currently do, and until this week none of them had a heading on our site in that phrasing.

Notice also what the models do not ask. They do not search for "best AI visibility tool". They do not search for our brand name. They ask the question their user asked, reworded into something a search engine can handle, and they keep the user's role and constraints attached. That is why generic category pages lose these retrievals to pages that answer a narrow question plainly. A model looking for "where are the limits of measuring brand visibility in AI assistants" is going to prefer a page that says, in a heading, where the limits are.

This is the practical meaning of answer engine optimization. It is not a new keyword game. It is writing the exact question a model will ask as a heading, and putting a short, complete answer directly underneath it, so the passage the model extracts is the passage you wrote for it.

What did we change on dataease.ai because of this?

We rewrote the six pages that receive most of our non-branded impressions around the questions models actually asked. Every H2 is now a natural-language question, each is followed by a 30 to 50 word direct answer, the same questions appear in FAQPage schema, and "DataEase AI" is named explicitly in the answers so the entity is unambiguous.

The specific changes, in the order we made them:

• Question-form H2 headings. Where a page said "Features" or "Next steps", it now asks the question a model would ask, in the phrasing the export showed us.
• A 30 to 50 word answer directly under each H2. Long enough to be a complete answer, short enough to be extracted whole. We include a specific number in the answer wherever we honestly have one.
• FAQPage schema that mirrors the visible questions and answers word for word. Models and search engines both read it, and mismatches between the schema and the page are a trust problem.
• Explicit entity naming. Several unrelated products share the name DataEase, so every answer names "DataEase AI" in full. A model that cannot tell which DataEase it is reading about will not cite either.
• Contextual internal links between the pages that answer neighbouring questions. Footer links do not carry topical meaning. A link inside the answer about measurement limits, pointing at the page on mention rate and share of model, does.
• A real dateModified. We had bulk-stamped the same date across dozens of pages. That is a freshness signal that says nothing, so now the date changes only when the content does.

None of this is exotic. It is the same discipline we ask of the brands we score with the free AI brand analyzer, applied to ourselves with the benefit of knowing the exact questions to answer.

How can a founder mine their own Search Console export in 20 minutes?

Export the Queries report for the last three months, keep every query of eight words or more, and read them as a list of research questions rather than search terms. Map each to the page that should answer it, then add an H2 in that exact phrasing with a direct answer underneath. The whole pass takes about 20 minutes for most sites.

The steps we use:

• Open Search Console, go to Performance, set the date range to the last three months, and export the Queries table to a spreadsheet.
• Add a column that counts words in the query and sort by it. Everything with eight or more words goes into a separate sheet. Skim the seven and six word rows too, because a few will belong.
• Read the long rows for the tells: "i'm a", "which tools", "which platforms", "is there a", "how do i", "context:", and a trailing year. Delete the handful that are obviously human, like a full product name pasted in.
• Group what remains by intent. In our export the groups were measurement methods, tool existence, vendor lists by segment, content formats, and integration or export questions.
• For each group, decide which existing page owns the answer. If no page does, that is a page you need to write, and you now have its headings.
• Add an H2 in the model's phrasing, lightly cleaned up, and a 30 to 50 word answer under it. Mirror it in FAQPage schema. Link the answer to the page that goes deeper.
• Note the date, wait four to six weeks, export again, and check whether those queries moved from position 30 to position 8. Then check the answers themselves in ChatGPT and Perplexity to see whether you are now named.

The last step is the one that matters. Search Console tells you which questions you were considered for. Only the answer tells you whether you won, and that is a separate measurement we cover in our guide to AI brand visibility.

What is the bottom line on AI fan-out queries?

A growing share of the searches that reach your site are made by assistants, not people, and they show up in Search Console as long, prompt-shaped queries with impressions and no clicks. Treat them as a free list of the questions models ask before citing you, and answer each one in a heading.

We think this population will grow. Every major assistant now runs live search as part of answering, and every one of those searches lands in someone's Search Console. The founders who notice first get a plain-language brief on what to write, straight from the systems that decide whether they appear in the answer. The ones who keep filtering by click-through rate will keep deleting the most valuable rows in the report.

We will report back on whether the rewritten pages move for the questions above once the next export comes in. If they do, that is the clearest evidence we can offer that answer engine optimization works the way we say it does. If they do not move, we will say that too.

What do founders ask us about fan-out queries?

What is an AI fan-out query?

An AI fan-out query is a search that an assistant such as ChatGPT, Perplexity or Google AI Mode issues to a search engine on a user's behalf while it researches an answer. It is long, phrased like a prompt, and appears in your Google Search Console as a query even though no person typed it into Google.

How can I tell whether a Search Console query came from an AI assistant?

Look for length of eight words or more, full sentences with punctuation, first-person role framing such as "i'm a brand insights analyst", spelled-out constraints such as "list some platforms and tell me for each why", and instruction prefixes such as "context: location: united states". Human searches almost never look like this.

Why do AI fan-out queries get impressions but no clicks?

Because the visitor is a model, not a person. The assistant reads the results it retrieves, decides which sources to cite, and writes an answer. It never clicks in the human sense. A fan-out impression is a signal that your page was a candidate for citation, which is the outcome that matters.

Should I optimise pages for fan-out queries?

Yes, but not by chasing them one at a time. Take the questions models keep asking, add them to the relevant page as question-form H2 headings, answer each in 30 to 50 words directly underneath, name your brand explicitly in the answer, and mirror the question in FAQPage schema. That is the core of answer engine optimization.

How do fan-out queries relate to AI visibility measurement?

Fan-out queries are the research step. Whether your brand ends up named or cited in the final answer is the outcome, and that is what DataEase AI measures as mention rate, citations and share of model. Search Console shows you which questions you were considered for; the answer itself shows you whether you won.