Millions of records. One person each.
High-confidence, person-centric identity across hundreds of millions of records, one authoritative view built from many scattered systems.
Matching, anomalies, and one clean model.
It starts with a hard question: is this person who the record says they are? We build identity resolution at federal scale, linking records across disparate systems into high-confidence, person-centric matches, with anomaly detection that flags what does not fit. The result is one authoritative identity instead of many conflicting ones, wherever a wrong match has real consequences: fraud detection, casework, deduplication, and eligibility.
High-confidence matching
Probabilistic and ML scoring that links records across systems, catching the variants a rules engine misses, without over-merging.
Anomaly detection
The mismatches that matter surface for review instead of hiding in the noise, duplicates, conflicts, and patterns that don't belong to one person.
Person-centric model
One authoritative view of each person, with lineage back to every source record, auditable end to end.
From scattered records to a defensible identity.
Ingest & standardize
Records land from every source system and normalize to one schema, names, dates, and identifiers made comparable.
Match & score
Candidate pairs are scored by the models; every score carries the evidence behind it.
Resolve & persist
High-confidence matches merge into the golden record. Nothing is destroyed, every source stays linked and recoverable.
Steward & learn
Low-confidence pairs route to human review, and every decision trains the next round of matching.
Every match, scored and shown.
No silent merges. Each candidate pair carries a confidence score, the evidence behind it, and a threshold your program sets, above it, records resolve; below it, people decide.
Where this connects.
Built for the mission in front of you.
Software, data, AI, and cybersecurity built around the outcomes your team owns.
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