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Model-ready behavioral features with documented context

Model-ready Features converts approved behavioral signals into sequences, graph relationships, labels, and aggregates matched to a defined training or evaluation task.

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Best for

Machine learning teams

Recommendation systems

Forecasting groups

Search and ranking products

Delivery options

Feature tables

Graph nodes and edges

Parquet partitions

Task-specific documentation

Example fields

A schema buyers can inspect

Names and definitions are illustrative. Production fields are confirmed during scoping and documented before delivery.

entity_pair

Approved relationship between domains, categories, or products

edge_weight

Normalized relationship strength

sequence_feature

Derived order, depth, or transition measure

temporal_feature

Windowed frequency, recency, or change signal

label_definition

Documented target or derived class

quality_flag

Coverage or stability note for downstream use

Questions it supports

Use the signal for a defined decision

  • Train ranking models
  • Improve recommendation context
  • Add demand features
  • Build graph-based retrieval
Product boundaries

Controls are part of the product

  • Training purpose reviewed before delivery
  • Bias and coverage documentation included
  • Individual profiling is not a standard output
  • High-risk automated decisions require separate assessment
ExploreConnected topic cluster
Topic guide

Clickstream data for market and journey intelligence

A practical guide to clickstream data, journey analysis, delivery formats, aggregation, quality controls, privacy boundaries, and business applications.

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Behavioral training data for models that understand intent

Build recommendation, ranking, forecasting, and intent models with structured, privacy-secured behavioral sequences and graph-ready data.

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Behavioral data for models that need market context

Build ranking, recommendation, forecasting, and retrieval systems with documented behavioral features.

Continue ↗Research

Chatbot 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.

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Product FAQ

Before you request a sample

Are labels included?

Labels can be designed where the approved behavioral evidence supports the model objective.

Can you deliver graph data?

Yes. Entities, edges, weights, time windows, and quality notes can be structured for graph workflows.

Is this suitable for production models?

The buyer should validate relevance, bias, stability, and permitted use before production deployment.

Define the scope

Tell us the market,
fields, and workflow.

We will verify coverage and propose a sample that matches the intended use.

Request data ↗