Behavioral data for models that need market context
AI teams can use aggregated sequences, entity relationships, temporal signals, and task-specific labels to add observed market context to models. Delivery and permitted use are defined before feature construction.
Request industry data ↗Decisions the data can support
- Which signals improve the target task?
- How stable are features across markets?
- What bias and coverage limits apply?
- Which uses require additional review?
What a scoped dataset may contain
- Ordered journey features
- Domain and category graph edges
- Research depth and recency
- AI discovery channel movement
Outputs shaped for the workflow
- Parquet feature tables
- Graph nodes and edges
- Label definitions
- Coverage and quality notes
Intent graph data from searches, clicks, and journey transitions
A practical guide to intent graph nodes, edges, weights, time windows, model applications, quality checks, and privacy limits.
Continue ↗ProductModel-ready behavioral features with documented context
Use aggregated sequences, graph edges, labels, and temporal features in ranking, forecasting, and recommendation systems.
Continue ↗Use caseBehavioral signals for AI models that need real journey context
Shape query-to-click paths, journey sequences, graph edges, and temporal features for ranking, retrieval, recommendation, and model evaluation.
Continue ↗ResearchChatbot adoption in 2026 is broad, but not evenly distributed
BGraph analysis of Pew Research Center data on U.S. chatbot adoption by age, with explicit treatment of the 2026 survey wording change.
Continue ↗Start with one question.
Define the right signal.
Coverage, fields, cadence, and safeguards are confirmed before delivery.
Request a scoped sample ↗