What AI search intelligence measures
The useful unit is a market pattern. Teams may study how AI-powered discovery grows within a category, which research tasks move to answer platforms, and where audiences continue after an AI-assisted search.
BGraph uses aggregated, privacy-secured behavioral signals for this work. Public product language avoids claims about access to private conversations, personal prompts, or named individuals.
- AI platform adoption by market
- Category-level discovery patterns
- Movement between search channels
- Downstream research behavior
Privacy boundaries for AI search data
AI queries can reveal health, politics, religion, finances, location, and other sensitive subjects. A responsible dataset needs purpose limits, data minimization, category exclusions, retention rules, and controls for each market where data is processed or delivered.
Pseudonymisation alone does not make European data anonymous. BGraph therefore describes the product as aggregated and privacy-secured unless a specific dataset has passed a documented anonymisation assessment.
- No product claim of personal conversation access
- No individual profiling in standard outputs
- Sensitive-category review before delivery
- Jurisdiction and use-case controls
How teams can use the signal
A search team may compare AI-assisted discovery with classic search. A brand team may monitor which categories shift first. A product team may look for changes in research depth after answer platforms enter the journey.
Delivery can follow the buyer's workflow, including API, Parquet, CSV, aggregated tables, or a custom research output. Available fields and geographic scope depend on the agreed legal and privacy review.