What is Query Fan-out and Why Does It Matter?
What exactly is Query Fan-out?
Query fan-out is a technical process used by AI engines like ChatGPT, Perplexity, and Google AI Overviews to answer complex or vague questions. Instead of searching for the exact string of text a user types, the AI "fans out" that single prompt into multiple, more specific sub-queries.
For example, if a user asks: "What are the best running shoes for flat feet?" the AI might internally generate sub-queries like:
"Top-rated stability running shoes 2026"
"Podiatrist recommendations for flat feet footwear"
"Comparison of Brooks vs. ASICS for overpronation"
"Price range for professional orthotic-friendly shoes"
The AI then searches for these sub-queries simultaneously, gathers "shards" of information from different websites, and synthesizes them into one cohesive answer.
Why does Query Fan-out matter for your brand?
In traditional SEO, you might rank #1 for a specific keyword but still be completely invisible in an AI response. This happens because the AI didn't use your main keyword to find its information—it used a sub-query.
Relevance over Keywords: AI visibility depends on answering the entire cluster of sub-queries, not just the main topic. If your content only covers "running shoes" but ignores the "price range" or "podiatrist view" facets, the AI will cite a competitor who covers those sub-topics.
The "Winner-Takes-Most" Dynamic: AI models typically only cite 3 to 8 sources. A website that provides authoritative answers to 6 out of 8 sub-queries is significantly more likely to be the "Top Citation" than a site that only excels at 1 or 2.
Factual Grounding: AI uses fan-out to cross-check facts. By retrieving evidence from multiple sources for each sub-query, the model reduces the risk of "hallucinating." Being the consistent, factual answer across these sub-queries builds Machine Trust.
How AI Visibility helps you master Query Fan-out
Because the fan-out process happens in the "black box" of the AI's reasoning, it used to be impossible to know which sub-queries were being used. Scalenut’s AI Visibility module brings this process to light.
Visualizing the Reasoning: In the Prompt Insights tab, you can click on any tracked prompt to see the Query Fanout. This reveals the exact logic the AI used to find (or miss) your brand.
Identifying Gaps: If you see the AI searching for a sub-query that you haven't covered on your site, you’ve found a "Content Gap".
Actionable Recommendations: The Recommendations module uses this fan-out data to suggest specific new articles or sections you should add to your site to ensure you are the "best ingredient" for every sub-query the AI generates.
