Why does query fan-out matter for GEO strategy?

Understanding query fan-out reveals optimization opportunities most brands miss. When a user asks 'best CRM software', AI platforms may fan out into 50+ sub-queries: 'CRM for startups', 'affordable CRM pricing', 'CRM integrations', etc. If you only optimize for the core query, you miss citations from these fan-out queries. By targeting the full fan-out pattern, you maximize your chances of being retrieved and cited. Platforms with higher fan-out (Perplexity, ChatGPT Search) offer more citation opportunities across diverse sub-queries.

How can I discover the fan-out pattern for my target queries?

GEO platforms like Qwairy use Search Intelligence to automatically detect fan-out queries generated by AI systems when processing your core prompts. You can also manually analyze AI responses with citations—each cited source often represents a fan-out sub-query. Look for patterns: if 10 different sources are cited, the system likely executed 10+ fan-out queries. Track which sub-queries your competitors rank for to identify gaps in your coverage.

Do different AI platforms have different fan-out behaviors?

Yes, dramatically. Perplexity uses high fan-out (50-200+ queries) to gather comprehensive evidence from diverse sources. ChatGPT Search uses moderate fan-out (10-30 queries) balancing depth and speed. Training-based models (base ChatGPT, Claude) have zero real-time fan-out—they rely solely on training data. RAG-based platforms fan out more aggressively, creating more opportunities for your content to be retrieved. Optimize for high fan-out platforms (Perplexity) when maximizing citation opportunities.

NEW v1.9: Insights Platform + Content Opportunities + Content Studio 🚀v1.9: Insights + Opportunities + Studio = 🚀

Optimization

Query Fan-out

The number of sub-queries or downstream requests that a system issues in response to a single user query.

Get Insights with AI

What is Query Fan-out?

Query Fan-Out refers to the number of sub-queries or downstream requests that a system issues in response to a single user query.

When a user submits one query (for example, to a search engine or LLM), the system may 'fan it out' into multiple smaller queries sent to various back-end services, indexes, APIs, or databases — and then aggregate all the results before returning an answer.

In large language model-based systems (like ChatGPT, Claude, Perplexity, or Arc Search):

• Query fan-out indicates how many sources or documents are retrieved for one user query.

• In RAG (Retrieval-Augmented Generation) systems, it's often the number of document 'chunks' fetched from a vector database. Example: retrieving the top-20 most relevant passages → fan-out = 20.

• In AI search engines (e.g., Perplexity), fan-out may reach hundreds of simultaneous web queries to gather diverse evidence before synthesis.

How Qwairy Makes This Actionable

Qwairy's Search Intelligence feature reveals the query fan-out patterns of AI platforms. When you monitor a core query, discover which sub-queries and related searches AI systems generate behind the scenes. This intelligence helps you create content targeting not just the main query, but the entire fan-out of related searches AI platforms use to build comprehensive answers.