Data Types We Support
Relational Data
Complex, interconnected datasets that capture business relationships:- Customer Networks: Account hierarchies, subsidiary relationships, partnership structures
- Transaction Chains: Multi-party transactions, payment flows, supplier relationships
- Event Sequences: Customer journey data, interaction timelines, lifecycle events
Tabular Data
Structured datasets from your operational systems:- Customer Records: Demographics, firmographics, account details
- Transaction History: Payments, purchases, service usage, billing events
- Outcome Data: Defaults, renewals, upgrades, churn events
Critical Success Factors
Data Quality Requirements
The quality of your Relational Foundation Model and every downstream model trained from it depends directly on the quality of your historical data: Good Data Definitions: Clear, consistent definitions of outcomes, customer states, and business events across your historical dataset. Event Time Columns: Every table should expose event timestamps (for example,created_at, updated_at, effective_at, or occurred_at) so we can reconstruct the state of the world at any point in time. These fields are critical for leakage-safe training runs and for replaying historical decisions.
Accurate As-Of Dates: Complement event timestamps with clear “as-of” semantics indicating when the information became available for decision-making.
Sufficient History: Adequate volume of historical outcomes to enable robust model training and validation.
Preventing Data Leakage
Data leakage occurs when future information accidentally influences past predictions. Our data ingestion process includes:- Temporal Validation: Ensuring all features were available at the time of decision
- As-Of Date Enforcement: Strict temporal boundaries for training data
- Outcome Window Definitions: Clear separation between prediction time and outcome measurement