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Query Fanouts

See How AI Expands Your Queries

Discover how ChatGPT, Perplexity, and other LLMs expand user queries into multiple sub-queries. Understand the hidden search behavior that shapes AI recommendations.

Multi-engine
Cross-platform
Sub-query
Extraction
Pattern
Analysis
Competitive
Comparison

app.demandsphere.com – Query Fanouts
QUERY FANOUTS — SUGGESTION EXPLORER
Explorer
Trends
Mapping
FANOUT QUERIES
34,200+8,100
SOURCE QUERIES
4,800+620
AVG FANOUTS/QUERY
7.1+0.8
NEW DISCOVERIES
2,400+18%
FANOUT VOLUME BY SOURCE
TOP FANOUT CHAINS
best seo tools
12
enterprise rank tracking
9
ai search optimization
8
llm visibility platform
7
crawl budget analysis
6

Query Fanout Visualization

A single user query triggers multiple internal searches. We capture and analyze these expansion patterns.

Commercial
best winter tires
Query fan-out
Which brand is the best for winter?
Query fan-out
Why are Blizzak tires so good?
Query fan-out
Which is better for snow, AWD or snow tires?
Query fan-out
Do snow tires really make a difference?

Mine Query Expansion Intelligence

Understand exactly how AI engines decompose and expand user intent.

Fanout Pattern Mining

Capture the exact sub-queries LLMs generate from user prompts. See how "best CRM software" becomes 5-10 specific comparison and feature queries.

Cross-Engine Analysis

Compare how ChatGPT, Perplexity, Gemini, and Claude each expand the same query differently - revealing unique optimization opportunities per engine.

Trend Detection

Track how fanout patterns evolve over time. New sub-queries appear as models retrain - catch emerging topics before competitors.

Competitor Visibility

See which brands appear in fanout queries for your category. Understand who AI considers relevant to your space.

Content Gap Analysis

Identify fanout queries you don't rank for. These represent content opportunities that directly influence AI recommendations.

BigQuery Export

Export complete fanout datasets via Search Intelligence. Build custom analyses, join with your own data, feed ML models.

The Hidden Layer of AI Search

Users see one query. AI engines execute many. Understanding this expansion is critical to optimizing your visibility in AI-generated answers.

  • AI doesn't just match keywords - it synthesizes answers from multiple internal searches. Your content needs to appear across these fanouts.
  • Fanouts reveal true intent - see the specific angles and comparisons AI considers important for any topic.
  • Different engines, different expansions - ChatGPT and Perplexity may generate completely different sub-queries for the same prompt.
  • Content strategy goldmine - each fanout query is a potential piece of content that could improve your AI visibility.
5-12
Average fanout queries per user prompt
73%
Of fanouts differ between ChatGPT & Perplexity
Fanouts tracked across all engines

Uncover the Queries Behind AI Answers

See exactly how LLMs expand user prompts - and optimize your content to appear across every fanout.

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See it with your own data.

30-minute demo. We'll run it on your domain - no prep required.

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