Core Features

Fanout Queries

When a user asks an AI engine a question, the engine doesn't just search for that exact phrase. It decomposes the query into multiple sub-questions, checks them individually, then synthesises a comprehensive answer. This process is called "fanout". Understanding it is key to maximising your AI visibility.

What fanout means in practice

Take the query: "What is the best project management software for remote teams?"

An AI engine might fanout this into:

  • What makes project management software good for remote teams?
  • What are the top-rated project management tools in 2025?
  • How does Asana compare to Monday.com for remote work?
  • What features should remote teams look for in PM software?
  • Is there free project management software for remote teams?
  • What do enterprise teams use for project management?

A page that answers all or most of these sub-questions is far more likely to be cited as a comprehensive source than a page that only addresses the surface-level query. The fanout map shows you exactly which of these questions your page currently addresses — and which it misses.

Reading the fanout map

The fanout map in your analysis displays related queries in a tree structure:

Covered
Your page directly answers this query with a clear, extractable response.
Partially covered
Your page mentions the topic but doesn't answer the query clearly enough for AI extraction.
Not covered
Your page doesn't address this related query at all. This is an opportunity.
Note
Fanout coverage percentage is a component of the AEO Readiness and Content Patterns scores. Covering more related queries directly lifts both dimensions.

How to improve fanout coverage

  1. Open the fanout map and click 'Not covered' to filter to gaps only.
  2. Group related gaps — several may be answerable in the same section.
  3. Add a FAQ section to your page addressing the most important uncovered queries. Use FAQ schema so AI engines can extract each answer individually.
  4. For each 'Partially covered' query: find where you mention the topic and add a direct answer sentence at the start of that section.
  5. Re-run the analysis after adding content to see your updated fanout coverage score.

Fanout query generation

The fanout queries are generated using an LLM that models the question-expansion behavior of major AI answer engines. The model is given your target keyword and industry context, and generates 15–25 related queries that an AI engine would likely check when answering a user's question about your topic. Queries are weighted by importance — the highest-weight queries are the ones most commonly asked by real users.

Use fanout queries as a content brief
Export the "Not covered" fanout queries as a list — they make an excellent brief for a content writer or for the AI Explainer Bot to help you craft answers. Each query is essentially a missing section heading for your page.