Query fanouts are the related searches that an answer engine makes from one user question. An answer engine such as ChatGPT, Claude, Gemini, or Perplexity almost never uses only the exact question to make an answer. This page explains how query fanouts change AI answers and how Genezio uses them. With query fanouts, the AI system examines a topic from different angles, gets more relevant sources, and makes a more complete answer.

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What is a query fanout?

A query fanout is an additional search query that the AI system makes internally. The AI system uses it to help answer the question of the user. The system does not search for one phrase only. It makes different variants and related questions. These help the system get information from different sources. For example, a user can ask this question:
User query: What are the best running shoes for marathon training?
Internally, the AI system can make queries such as these:
  • best marathon running shoes
  • long distance running shoe reviews
  • nike vs adidas marathon shoes
  • best racing shoes for marathon runners
  • top cushioned marathon shoes
With these queries, the system gets content that shows different perspectives on the topic.

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Why answer engines use query fanouts

User questions are frequently wide, ambiguous, or incomplete. One search query can get too little information for a high-quality answer. Query fanouts help the AI system do these tasks:
  • Examine many interpretations of a question
  • Get information from different sources
  • Compare alternatives
  • Validate claims across many documents
When the model makes the search space larger, it gets a more complete understanding of the topic before it makes its answer.

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How query fanouts change the answers

The query fanouts control which documents and sources the AI system gets when it collects information. Those sources then have an effect on these items:
  • Which brands occur in the answer
  • Which sources the answer cites
  • Which claims the answer includes
  • How the answer compares the competitors
For example, a query fanout can be this one:
Query fanout: best CRM for startups 2024
The AI system can get comparison pages, review articles, and vendor websites about CRMs for startups. Thus, the brands that those sources mention have a higher probability to occur in the final answer. For this reason, query fanouts have a strong effect on AI Recommendations and AI Visibility.

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An example of query fanouts in practice

Look at this question:
User query: Which CRM is best for startups?
The query fanouts can include these:
  • Query fanout: best CRM software for startups
  • Query fanout: HubSpot vs Pipedrive for small businesses
  • Query fanout: affordable CRM tools for startups
  • Query fanout: startup sales management software
  • Query fanout: CRM comparison for early stage companies
Each of these searches can get different articles, reviews, or vendor pages. Then the AI system combines the information from those sources into one answer.

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Why query fanouts are important for brands

Query fanouts control which sources the AI system gets. Thus, they also have an effect on which brands occur in AI-generated answers. If the sources for those query fanouts frequently mention a brand, the brand has a higher probability to occur in the final answer. If a brand is absent from those sources, it can be absent from the answer. This can occur also when the brand is a strong product in the category. Thus, when organizations understand query fanouts, they can identify these items:
  • The questions that AI systems examine for a topic
  • The sources that have an effect on the answer
  • The areas of the information landscape where their brand is missing

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How Genezio uses query fanouts

Genezio extracts and analyzes the query fanouts that answer engines make during AI conversations. For the reports in the platform, read Query fanouts. With this data, teams can understand these items:
  • What the AI system really searches for
  • Which queries control the sources that the AI system gets
  • Which topics have an effect on brand visibility
  • Where there are new content opportunities
For example, Genezio can find that AI systems frequently examine queries such as these:
  • “best CRM for early stage startups”
  • “CRM tools for SaaS founders”
If a brand has little content or coverage about those topics, that gap can be an opportunity to increase AI Recommendations and AI Visibility.

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Query fanouts compared with SEO keywords

Query fanouts are similar to keywords. But they show how AI systems examine a topic, not how users type short search queries. Traditional SEO frequently optimizes pages for individual keywords. In AI search, the wider set of query fanouts can be more important. These queries control which sources the AI system gets when it makes the answer.

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Next steps

To learn how query fanouts change AI answers, read these pages: These pages tell you how Genezio analyzes AI answers and changes them into structured insights.

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In the API

The public API lists the query fanouts of a brand: List the searches.