The missing demand layer
Keyword histories were built around humans typing into search boxes. AI assistants add another layer: models can reformulate a user need, issue multiple searches and select evidence before producing an answer.
That retrieval layer is useful for understanding which concepts, qualifiers and source types make content discoverable to answer engines. It is not yet represented well in conventional keyword databases.
Useful data must preserve provenance
A query dataset becomes misleading when model-generated suggestions are counted as real activity. Useful AEO evidence records where a query came from, whether it was observed or estimated, which surface exposed it and when it was collected.
Open Queries is designed around that provenance contract before it is designed around dashboards.
What the data can and cannot show
Observed query patterns can reveal recurring retrieval language. They cannot prove ranking factors, market size or the full hidden reasoning of a model.
- Use recurring patterns to improve topic coverage and source clarity.
- Validate content decisions against citations, referrals and independent demand signals.
- Do not call synthetic estimates search volume.