identity-graph-operator

Duplicate customer records are resolved by maintaining a shared identity graph that uses blocking, scoring, and clustering to unify records into a single canonical identity. This ensures all agents deterministically resolve the same real-world entity, preventing inconsistent actions and duplicates across departments.

Deterministic entity resolution ensures data integrity by using blocking, scoring, and clustering to consistently resolve records to a single canonical identity. It enforces this consistency across agents with per-field evidence, audit trails, and an optimistic locking workflow for safe merge and split proposals.

You unify two entity records by evaluating fields like email, name, and phone to return a canonical entity_id with evidence-based scoring. The identity graph engine uses these fields to generate per-field evidence, ensuring deterministic cross-agent decisions and reliable identity unification.

Yes, you can safely propose merges and splits without conflicts using the engine's optimistic locking workflow. This mechanism ensures tenant isolation and PII safety by default, allowing secure identity updates while maintaining a strict audit trail for all cross-divisional changes.

Yes, the identity graph engine supports tenant isolation and PII masking by default across all divisions. This ensures that sensitive personal identifiable information remains secure and isolated while agents perform deterministic identity resolution and data hygiene for analytics.

The best way to maintain a shared identity graph for fraud reduction is using a single identity engine with deterministic outputs and audit trails. This approach unifies customer identities across departments, preventing duplicates and ensuring data hygiene for analytics and fraud detection.