Master How AI Models Decompose Buyer Prompts - Map Sub-Queries & Win Citations

Understand how AI engines break buyer prompts into sub-queries. Map the full query fan-out graph to maximize brand citations across Perplexity, ChatGPT, and Google AI Overviews.

Behind the Scenes of an AI Recommendation

Complete Sub-Query Mapping & Analytics

How search & content strategists win

The ROI of Fan-Out Sub-Query Coverage

3 steps to complete fan-out coverage

Select your plan for Fan-Out Intelligence

Frequently Asked Questions

What is Query Fan-Out in AI search?

Query Fan-Out is the process where an AI engine (like Perplexity or Google AI Overview) takes a user\

Why does Query Fan-Out matter for brand visibility?

Even if your brand ranks well for the main keyword, if you are missing from the specific sub-queries the AI model fires behind the scenes (e.g. pricing, integrations, security), the AI will recommend a competitor who satisfies those sub-queries.

How does Astiva discover fan-out sub-queries?

Astiva analyzes retrieval logs, search API calls, and LLM reasoning steps to reconstruct the exact sub-query graph generated for any given seed prompt.

Can I use Query Fan-Out insights to improve my existing blog posts?

Yes! Incorporating fan-out sub-queries as subheadings (H2/H3) and FAQ sections in existing articles is one of the fastest ways to boost AI citation rates without writing new pages from scratch.

Is there a free tool to try Query Fan-Out mapping?

Yes! You can try our free interactive Query Fan-Out Generator tool anytime at astiva.ai/tools/query-fanout-generator.

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