Query Fan Out Simulator
See how AI search engines internally expand a single query into multiple sub-queries. Understand the hidden search process behind every AI answer — and optimize your content to appear in each sub-query.
How Query Fan Out Works
What is query fan out?
When you ask an AI search engine a question like "best CRM for startups," it doesn't just search for that exact phrase. Internally, it decomposes your query into 8-15 sub-queries: "top CRM tools 2026," "CRM pricing comparison," "CRM features for small teams," "CRM user reviews," etc. This process is called "query fan out" — the AI fans out your single question into a tree of related queries to gather comprehensive information before generating its answer.
The 5 sub-query types
Each sub-query serves a different purpose: Factual queries gather hard data (features, pricing, specs). Comparative queries look for side-by-side comparisons. Evaluative queries assess quality and reputation. Contextual queries understand the broader landscape. Temporal queries check for recent developments. Your content needs to satisfy ALL these types to appear in the final AI answer.
Why this matters for GEO
If your brand only appears in one sub-query out of 10, you might not make it into the final answer. The AI synthesises information from ALL sub-queries — brands that appear across multiple sub-query types have a much higher chance of being mentioned. This tool shows you exactly which sub-queries AI generates for prompts in your space, so you can create content that covers each angle.
How to optimize for fan out
- Create comparison pages that answer comparative sub-queries directly
- Include concrete data (pricing, features, specs) for factual sub-queries
- Build trust signals (reviews, case studies) for evaluative sub-queries
- Keep content fresh and dated for temporal sub-queries
- Write comprehensive guides that cover the full context of your industry