Each agency has its own system, its own identifiers, and its own view of the person in front of them. The same individual can claim benefits from two programs under three identities — and no one agency will ever see the full picture. LangOptima connects them into a single knowledge graph, with governance built in.
The siloed data that protects citizen privacy also shields duplicate identities, synthetic personas, and cross-program fraud. The answer is not less privacy — it’s better connection, under governance.
Each agency assigns its own identifiers. The same individual is a different record in every system. Without entity resolution, nothing is ever connected.
Cross-agency checks today are manual, episodic, and low-signal. Most duplicate identities and synthetic personas never trigger a review.
Organized benefit fraud relies on agency-local data. Shared addresses, shared bank accounts, and shared digital identifiers sit in the gap between systems that no one is connecting.
LangOptima models people, organizations, addresses, programs, and transactions as connected entities in a governed knowledge graph. Agency data remains owned by each agency. The graph is the layer where entity resolution happens — with role-based access control, full audit logging, and minimum-necessary exposure by default.
Cross-agency data unified at the semantic layer without moving or copying it. Role-based access control and audit logging built in from day one.
Surface the same individual under multiple identities, detect benefit claims in two programs simultaneously, and uncover networks connected through shared addresses or bank accounts.
Investigations move from months to weeks. Referrals come with complete evidence chains, ready for prosecutorial review.
Fraud investigators, program integrity teams, and cross-agency task forces stop rebuilding context on every case. The graph becomes the shared working memory of the investigation.
Representative scenarios for public-sector programs with your profile — ask us what applies to yours.
A government agency identified $12M in duplicate benefit payments within the first pilot by modeling people, addresses, programs, and transactions as a connected knowledge graph. Individuals operating under three identities across agencies were surfaced within the first month.
Agency data never leaves agency control. The knowledge graph federates access at the semantic layer with role-based governance, audit trails, and minimum-necessary exposure — the opposite of a data lake dumping ground.
However complex the data landscape underneath, the engagement itself stays simple.
We ingest the documents and data you already hold, straight from the systems you already run. Nothing is replaced — your teams keep working where they work today.
A scoped 8–12 week pilot structures your first decision context. Your domain experts contribute the knowledge; we do the engineering.
You start asking the questions — and every answer carries a citation back to the source, so you can check it yourself.
Start with a 30-minute conversation. Tell us about the program, the duplicate-identity problem, or the cross-agency referral that is hardest to complete today — and we’ll show you how a governed knowledge graph would approach it.