See Demand Structure Behind AI Questions

Query Fanout refers to the research path an AI system expands into when generating an answer — including the number of sub-queries it creates and the external sources it references. In Dageno AI, the Query Fanouts is built on a RAG architecture and multi-agent workflow. It simulates AI query decomposition and parallel retrieval, tracking sub-query counts, cited sources, and trend changes to accurately reconstruct how AI “researches” a question.

A higher Query Fanout means the AI must break the question into more sub-problems and consult more sources, indicating greater research depth and decision complexity. In Dageno AI, high-fanout topics typically signal higher decision value. If brand citation rates are low within these queries, they often represent high-priority opportunities for strategic content positioning.

Keyword analysis focuses on what users search for; Query Fanout focuses on how AI researches a question. Dageno AI simulates AI’s decomposition and retrieval workflow, visualizing sub-query structures, source distribution, and platform differences. This allows you to see not just search demand, but the actual AI decision pathway.

High-value opportunities typically appear as prompts with high fanout but low brand citation. Dageno AI automatically identifies these high-research-depth yet underrepresented scenarios, helping you prioritize content and product positioning to increase visibility and citation probability within AI-generated answers.

Query Fanouts can be used to judge whether a question is worth investing in, formulate content and GEO priorities, and predict the user's next questioning direction in advance, helping the brand take a position in the AI answer chain ahead of time.