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 approach behind EUR-Lex, where the EU's Publications Office stores every treaty, regulation, and judgment as RDF, so law and policy connect across languages and systems. GOV.UK runs GovGraph domestically, and the City of Zurich queries municipal records, projects, and transit the same way.
The siloed data that protects citizen privacy also shields duplicate identities, synthetic personas, and cross-program fraud. The answer is not less privacy. It is 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.
Modern AI can structure a great deal on its own. Where it stops is the meaning specific to your organization: the concepts, rules, and relationships that make your business yours, and where its real value lives. We structure that layer with you on open, world-standard semantics, not a proprietary schema, so the graph reflects how you operate and stays yours: no vendor lock-in, portable to whatever you run next. More on the structure beneath it →
You don't have to boil the ocean. Start with a single business context and prove it there. Once that foundation is laid properly, the same connected data tends to open opportunities in other departments, so the next team builds on the work already done rather than starting from zero.
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. Illustrative of the pattern, not published client references.
People, addresses, enrollments, and transactions connect in one governed knowledge graph. Duplicate identities and cross-program payments show up as linked records, connections no single agency could see on its own, with each agency’s access rules still enforced.
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 data you already hold, straight from the case, benefits, and registry systems you already run, with governance built in. Nothing is replaced. Your teams keep working where they work today.
A scoped 8–12 week paid pilot structures your first decision context, say resolving one person across agency systems and identifiers, measured against success criteria you set. Your domain experts contribute the knowledge; we do the engineering. If it proves value, you expand from there. If it doesn’t, it doesn’t scale.
You query the connected layer in plain language, and answers come in seconds, accurate and traceable, with citations back to the source so you can check them yourself.
Curious what disconnected data may be costing your organization? The free Data Silo Cost Calculator puts a number on it in about two minutes, with no signup to see the result.
Try the Data Silo Cost Calculator →For a single document with a single answer, plain search or an LLM pointed at your PDFs does the job well, and a knowledge graph would be overkill. The difference shows up on the questions one document can’t answer: whether the same person appears under different identifiers across agencies, or which benefit claims duplicate one another, without moving citizens’ data out of the boundary it has to stay in. Those answers live in the relationships across your agency systems and case platforms and your case files, applications, and correspondence, not inside any one of them. A knowledge graph connects the structured systems and the documents into one model, follows the chain across them, and returns each answer with a citation back to the record it came from. Something you can act on, and defend.
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. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.