Entity Resolution Platform

Managed change-data-capture streams records from source registries into a cloud warehouse continuously—no batch ETL, and no load on production systems.

Incremental dbt models publish clean, versioned views with generated change markers, so every downstream consumer ingests updates and deletes reliably.

A managed ML pipeline blocks, generates candidate pairs, and scores them—backed by a partitioned vector store of embeddings, phonetic keys, and blocking keys for fast lookup.

A ports-and-adapters service owns the consolidation-run lifecycle, triggers the pipeline idempotently, and drives a self-healing queue-based loader with a readiness gate that prevents silent data loss.

A side-by-side review workspace ranks candidate pairs by confidence, surfaces per-field evidence, and lets experts lock, confirm, or reject—every decision immutably recorded.

A versioned async SDK and governed API give ML pipelines and downstream tools a single, contract-first surface, with streaming bulk ingestion that stays memory-flat at millions of records.

Vector similarity, phonetic and fuzzy name matching, and deterministic attribute agreement combine into per-pair evidence—so every match is explainable, never a black box.

The model auto-confirms high-confidence duplicates and routes only genuinely ambiguous pairs to reviewers, keeping precision high without burying the team.

A commit watermark and late-binding published views hide partial or failed runs, so consumers always read one fully committed snapshot—no rebuilds, no blue/green swaps.

A readiness gate defers dependent records until prerequisites land, and streamed, keyset-paginated exports keep memory bounded no matter how large the dataset.

Model scores, per-field match flags, and every reviewer decision are captured with actor and rationale in immutable tables—defensible end to end.

Source systems stay authoritative and untouched; matching runs downstream on replicated, governed data with least-privilege access throughout.

Tens of millions of person and entity records matched across independently managed registries into a single reconciled view.

Incremental delta scans and per-run delete reconciliation complete in a fraction of a second, even at scale.

Server-side parallel export cut a full-view read from roughly a day to a few minutes—with a fraction of the memory.

Confidence ranking and automated blocking let reviewers focus only on genuinely ambiguous cases instead of the full population.

Fragmented, overlapping entries collapse into one reliable identity per person, giving every team the same source of truth.

Every decision, evidence payload, and mutation is captured with actor and rationale—the accountability regulated environments demand.

Confidence scoring concentrates scarce expert attention on ambiguity, cutting review effort and cost while improving consistency.

Streaming ingestion, incremental transforms, and serverless auto-scaling absorb growth into the tens of millions of records—no redesign.

A versioned SDK and change-feed contract let new registries and downstream tools integrate against a stable surface—a bespoke build becomes a product.

Least-privilege access, encryption everywhere, private networking, and multi-tenant scoping protect sensitive identity data at every layer.