What Is Query Fan-Out? How AI Search Turns One Question Into Many

Query fan-out is how AI search answers a question that one search cannot cover. ChatGPT, Gemini, and Google’s AI Mode split the question into smaller sub-queries, run them at the same time, and merge the results into one answer. Every sub-query is a separate search, with its own winners and its own sources.

For a real estate agent, that changes the math on visibility. A seller who asks for the best agent in their neighborhood triggers a set of quiet searches for agent names, reviews, recent sales, and home values. Your name appears in the answer only if your content wins some of those searches.

Key Findings

• In a September 2026 test by The Paperless Agent, three identical ChatGPT runs of a Circle C Ranch listing-agent question produced 16 unique sub-queries, and none of them appeared in all three runs.

• In a September 2026 test by The Paperless Agent, Google’s Gemini held a Circle C Ranch realtor recommendation intent in 3 of 3 runs while the wording of its searches changed each time.

• In a single September 2026 ChatGPT run by The Paperless Agent, 6 of the 9 sub-queries searched for a specific agent by name.

How Does Query Fan-Out Work?

Query fan-out works in five stages. The model breaks a question into parts, writes a search for each part, runs those searches in parallel, reconciles what comes back, and writes one answer with citations. Search Engine Land’s guide to query fan-out lays out the sequence:

1. Decomposition: the model identifies the core topics and implied follow-up questions inside the original query.

2. Expansion: it writes sub-queries, each one aimed at a different facet of the question.

3. Execution: it runs every sub-query at the same time.

4. Synthesis: it reviews the results and resolves contradictions between sources.

5. Answer generation: it returns a written answer with citations attached.

Google describes the speed in plain terms. Dounia Berrada, Search Senior Engineering Director at Google, wrote in a March 5, 2026 company blog post that AI Mode does “a dozen searches for you in the time it takes to do one.”

A Google patent, US11663201B2, lists eight types of sub-queries a single question can generate. The list includes equivalent phrasings, follow-up questions, broader and narrower versions of the topic, and requests for clarification.

What Does Query Fan-Out Look Like for a Real Estate Search?

A real estate fan-out searches for the agents a model already suspects are the answer. In a September 2026 test by The Paperless Agent, one ChatGPT run of a Circle C Ranch listing-agent question fanned out into 9 sub-queries, and 6 of the 9 searched for a specific agent by name next to the neighborhood name.

The prompt was the question a Southwest Austin homeowner would type: “I’m selling my home and need a good realtor. My home is in Circle C Ranch in Austin, TX. Who are agents you recommend?”

The other three sub-queries looked for seller reviews from this year, homes marked sold in the neighborhood, and the phrase “real estate agent” tied to Circle C Ranch. The model checked which agents already had a record in the neighborhood before it wrote a word.

A search this specific already has an incumbent, and the fan-out looks for that incumbent by name. Production volume changes nothing in that process until it appears on a page the fan-out can read.

The wider map of fan-out intents

One run shows a slice of what the same question can produce. For example, a Circle C Ranch agent search can reach intents covering the zip code, top realtors, realtor reviews, home values, selling a house, brokerages, competing Southwest Austin neighborhoods, HOA fees, and how to find an agent in the first place. That list is an illustrative model built by The Paperless Agent, and each real run covers a different subset.

An agent with a homepage and one listing page answers none of those intents directly. An agent whose reviews, business listings, and content cover half of them starts appearing across several sub-queries, and repeated appearances are what put a name in the final answer.

Why Does Query Fan-Out Change Every Time You Search?

Query fan-out changes because each attempt is a fresh decomposition, drawn from a wider pool of possible sub-queries than any single run reveals. In a September 2026 test by The Paperless Agent, one Circle C Ranch seller question run three times on ChatGPT produced 16 unique sub-queries, an average of 5.7 per run, and none of them appeared in every run.

In the same September 2026 test by The Paperless Agent, the closest thing to a stable core was one generic “top real estate agents” search, which showed up in 2 of 3 runs. Content built to answer only the sub-queries from one test misses most of what the next attempt will generate.

What Gemini did differently

Google’s Gemini held the intent steady while the wording moved. In a September 2026 test by The Paperless Agent, Gemini ran 12 Google searches across three runs of the same question, and a Circle C Ranch realtor recommendation intent appeared in 3 of 3 runs.

In that September 2026 test by The Paperless Agent, one of the 3 Gemini runs narrowed toward a “top listing agent” search and also ran a site:reddit.com/r/austin search of Reddit’s r/Austin community. Gemini checked a public forum for local confirmation before it settled on an answer.

One agent’s name recurred as the strongest named signal inside Gemini’s searches, with two competitors surfacing less often. We left the names out on purpose. The pattern carries to your market: the fan-out checks for an incumbent, and new content has to appear where it is checking.

How We Tested

The Paperless Agent ran one prompt, verbatim, on ChatGPT and on Google’s Gemini: “I’m selling my home and need a good realtor. my home is in circle c ranch in austin tx. who are agents you recommend?” Each platform received the identical prompt three times in September 2026. ChatGPT ran in a logged-in account on purpose, to capture what a real user sees, so its results can reflect some personalization. Gemini ran through Google’s API with no signed-in account, and the Gemini report was generated on September 16, 2026. Our fan-out tracking tools logged every sub-query ChatGPT issued and every Google search Gemini ran. A separate single ChatGPT run, also in September 2026, produced 9 sub-queries. Three runs per platform on one neighborhood question is a small sample, so treat these patterns as signals to test in your own market.

Why Does Query Fan-Out Decide Which Agents Get Named?

Query fan-out rewards published answers, which is why top producers often go unnamed. In Local Falcon’s Realtor AI Visibility Index, a study of 37,500 AI searches across the 100 largest cities in the United States in August 2026, 91.5% of top-producing agents with websites were never cited once by any of the five AI platforms tested.

Local Falcon CEO David Hunter explained the gap in Inman’s coverage of the study: “The models aren’t reading MLS data or production rankings; they’re reading pages.”

Each sub-query ranks the pages, reviews, and profiles that answer it, one search at a time. An agent with no content for a given sub-query is invisible to that search, whatever their sales volume.

Right now, someone in your market is typing a long-tail question about your neighborhood into ChatGPT, Google’s AI Overviews, or Perplexity. Each tool runs its own fan-out on whatever it finds. If none of the results lead back to you, the model has no way to name you.

How Do You Map the Query Fan-Out for Your Market?

You can map your own fan-out in an afternoon. Write the question a buyer or seller would ask, list the sub-queries it would trigger, and check which of them your content answers.

1. Write the question as a stranger would. Use the conversational version a buyer or seller in your market would type, taken from a listing presentation or a call transcript where possible.

2. List the sub-queries it would fan into. Agent names, reviews, recent sales, home values, HOA information, competing neighborhoods, brokerages, and the direct “how do I find an agent” question.

3. Check your consistency. Your name, license, and service area need to read identically across your website, your Google Business Profile, and every directory that lists you.

4. Check your reviews. Look for reviews that name the neighborhood, a price point, or a specific situation, since each one answers a sub-query directly.

5. Mark what is missing. For each sub-query, ask what a stranger typing exactly that question would need to find. That is the content to build.

Name consistency matters because the fan-out searches for you by name. A name or service area that varies from one listing to the next forces the model to guess which entity it is reading about.

Platforms also differ in where they look. OpenAI says ChatGPT search works with third-party search providers, so ChatGPT can retrieve different sources than a Google-focused test shows. Build your answers for every platform a buyer or seller might use.

What Content Answers Each Sub-Query?

One well-built neighborhood page can answer several sub-queries at once, and it usually should. A page covering the zip code, home values, HOA details, recent sales, and nearby brokerages does more work than five thin pages that each answer one question.

Business listings carry the most citation surface. Yext’s analysis of 17.2 million AI citations from Q4 2025 found that listings made up 54.53% of the distinct URLs cited, while first-party websites drew more citations per URL. Keep both current.

The Principle Behind Query Fan-Out

AI search answers a stranger’s question one sub-query at a time. Your name appears only where you have already answered the specific question being asked.

The goal is coverage: enough of the real sub-queries in your market answered, in your own words, on sources AI already reads, that your name keeps surfacing across the fan-out. Every neighborhood, price point, and objection a buyer or seller raises is a potential sub-query. You have heard most of them at listing appointments for years.

Answer the sub-query the stranger typed.

What to Do Right Now

1. Write down the sub-queries. List ten questions a buyer or seller in your neighborhood would ask about agents, reviews, pricing, HOA fees, and competing areas. Use real questions from your last five listing appointments.

2. Run your own long-tail search. Open ChatGPT or Perplexity and type the conversational version: “I live in [your neighborhood] and I am thinking of selling my home. Can you recommend a top listing agent in my area?” Perplexity shows its sources directly. Note which ones are yours and which belong to a competitor.

3. Fix the weakest link first. If your profiles on Zillow, Homes.com, or Realtor.com are thin or inconsistent, fix those before writing a single new page.

Frequently Asked Questions

What is query fan-out?

Query fan-out is the process an AI search system uses to answer a question that a single search cannot cover. The model breaks the original question into several smaller sub-queries, runs them at once, and combines the results into a single answer. Google’s Search team has described the effect as running a dozen searches in the time it takes to run one. A search for the best agent in a neighborhood becomes separate searches for agent names, reviews, recent sales, and the neighborhood itself, and each one pulls from a different source.

How many sub-queries does one AI search generate?

The number varies by platform and by how complex the question is. A Google patent describes eight types of sub-queries a single question can generate. In a September 2026 test by The Paperless Agent, one ChatGPT run of a Circle C Ranch question produced 9 sub-queries, while three repeated runs averaged 5.7 each. In the same September 2026 test by The Paperless Agent, Gemini ran between three and five Google searches per run on the same question.

Why does query fan-out produce different searches each time?

Each attempt is a fresh decomposition of the question, drawn from a wider pool of possible sub-queries. In a September 2026 test by The Paperless Agent, three identical ChatGPT runs produced 16 unique sub-queries, and none of them appeared in all three runs. Gemini kept the underlying intent stable across runs while changing the wording. Content that covers the full range of likely questions holds up better than content tuned to one test.

Is query fan-out the same on ChatGPT, Google AI Overviews, and Perplexity?

The core idea, splitting one question into several searches, is shared across all three, and the sources each one favors differ. Yext’s research on 17.2 million AI citations found that Claude cites reviews and social media at rates two to four times higher than competing models, while Gemini leans toward first-party websites, consistent with its Google Search grounding. Plan your content and review strategy on the assumption that each platform weighs sources differently.

Can I see the sub-queries AI runs for my market?

You can get close. Perplexity displays its sources for each answer, which shows where the underlying searches found results. On any platform, typing the long-tail version of a buyer or seller’s question and reading which sources the answer cites is the closest substitute for watching the fan-out happen. Dedicated tracking tools, like the ones The Paperless Agent built for its September 2026 test, can log the exact sub-queries ChatGPT and Gemini issue.

Should I build one page or several pages to cover my area’s sub-queries?

One comprehensive page usually outperforms several thin ones. A neighborhood agent search fans into sub-queries covering agent names, reviews, recent sales, and the neighborhood itself, and one well-built neighborhood page can answer most of them at once. A separate page for every sub-query spreads the same authority across more pages, each thinner and harder to keep current. Save individual pages for topics substantial enough to need their own depth.

Do reviews on Zillow and Realtor.com affect query fan-out results?

Reviews are one of the things a fan-out looks for. In a September 2026 test by The Paperless Agent, one ChatGPT run searched specifically for seller reviews of Circle C Ranch agents before naming anyone. A review that names a neighborhood, a price point, or a particular situation answers a sub-query directly. A five-star rating with no detail gives the model nothing to match against a real question.

What is the single biggest fix for showing up across the fan-out?

Consistency across every source an AI system can find. Your name, license, service area, and description need to read identically across your website, Google Business Profile, and every directory or portal that lists you. The fan-out searches for agents by name, so inconsistent listings force the model to guess which entity it is reading about, which discounts every other signal you have.