Fan-Out Query Explorer
Use cases
Surfaces the fan-out queries (sub-questions) that AI platforms generate when researching answers to queries in your topic space.
Queries the DataForSEO LLM Mentions Search endpoint by keyword or domain, deduplicates discovered fan-out queries, ranks them by frequency, then clusters them by semantic similarity using sentence-transformers (all-MiniLM-L6-v2 with community_detection).
Use this to plan content that answers the sub-questions AI systems break complex queries into.
Platform
Python script (requires Python 3.x)
Input
DataForSEO API credentials (login and password)
A keyword or domain to explore
Platform, location, language, and results limit settings
Output
Fan-out queries ranked by frequency with parent question mapping, plus a parent questions table with fan-out counts, AI search volume, and source counts.
Features
- Search by keyword topic or by domain mentions
- Platform selector: Google AI Overview or ChatGPT
- 12 location and 8 language options
- Fan-out query deduplication and frequency ranking
- Semantic clustering via sentence-transformers (all-MiniLM-L6-v2, community_detection, threshold 0.65)
- Cluster summary table with query counts and total frequency per cluster
- Parent question mapping (which questions spawned each fan-out)
- Summary metrics: parent questions, total fan-outs, unique fan-outs, average per question
- Clustered and unclustered CSV downloads
How to use
- 1 Enter DataForSEO credentials in the sidebar
- 2 Select target type (Keyword or Domain)
- 3 Choose AI platform, location, and language
- 4 Enter your keyword or domain
- 5 Click Explore Fan-Out Queries
- 6 Review fan-out queries ranked by frequency
- 7 Download fan-out queries or parent questions CSV
Frequently asked questions
- What are fan-out queries?
- Fan-out queries are the sub-questions an AI model generates internally while researching its answer to a user prompt. They represent the decomposition of a broad question into narrower information needs. If your content directly answers these sub-questions, you are more likely to be cited as a source.
- How much does a request cost?
- Each request costs approximately $0.10 via the DataForSEO LLM Mentions Search endpoint, regardless of whether you search by keyword or by domain.
- What does the frequency ranking mean?
- Fan-out queries are ranked by how often they appear across different parent questions. A higher frequency means the sub-question surfaces under multiple parent prompts, making it a higher-priority content target because answering it increases your citation chances across several queries.
- What is the results limit slider for?
- The slider (range 10 to 500, default 100) controls how many fan-out query results the API returns. A higher limit gives more comprehensive coverage but still counts as a single API call at the same cost.
- Can I search by domain instead of keyword?
- Yes. The tool supports both Keyword and Domain search modes. Domain mode returns the fan-out queries that led AI models to cite pages on that domain, which is useful for understanding why a competitor is being referenced.
- How should I use the parent question mapping?
- Each fan-out query maps back to the parent question that generated it. Use this mapping to build content clusters: group fan-out queries by their parent, then ensure your content answers the full set of sub-questions beneath each parent prompt.
Want me to run this for you?
I run this tool as a managed service, or build something custom around your data. You get the insights without touching the code.
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