Clinical records, claims, provider networks, and clinical and coverage guidelines live in separate systems, tied together by hand whenever a question crosses them. LangOptima ingests the data you already hold and connects it into a single queryable knowledge base, within your existing environment and compliance boundary, so teams answer cross-system questions in seconds, each result cited back to its source.
Built on the open clinical standards FHIR and SNOMED CT, so records, terms, and care pathways connect without re-platforming existing systems.
A question about a patient, a population, or a policy touches the record, the claim, and the guideline at once. In many organizations, joining them is manual work that few have the time to do well.
Records live in one system, claims in another, guidelines in documents. Joining them is manual work, so cross-system questions get asked less often than they should.
Clinical and coverage rules live in manuals and documents. Connecting a rule to the specific cases it applies to is done by hand, so it reaches the point of decision late, or not at all.
Who is in which network, credentialed for what, tied to which claims, changes constantly across systems. The mismatches surface later as denied claims and directory errors.
LangOptima ingests the data your organization already holds, including clinical records, claims, provider and network data, and clinical and coverage guidelines, and structures what is inside them: patients, providers, conditions, procedures, policies, and the relationships between them, within your existing environment and compliance boundary. It sits above your clinical, claims, and network systems without replacing them, so a question that once meant a manual cross-system join becomes a single query with the source attached.
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 →
Records, claims, provider networks, and guidelines unified at the semantic layer, inside your compliance boundary. Nothing moves. Nothing is replaced.
Traverse from a guideline to every case it governs, from a provider to their networks and claims, from a condition to the pathways and policies that touch it.
“What does this policy apply to, and where does it show up?” answered in seconds, with the source to back it. For care-management and operations teams alike.
Every care-management and operations team works with the full picture behind them, so questions that spanned departments resolve as a single query.
Representative scenarios of how provider and payer teams apply a knowledge graph over their own data, within their existing compliance boundary. Illustrative of the pattern, not published client references.
A provider or payer connects its records, claims, and guideline data in a knowledge graph, within its existing compliance boundary. Teams ask questions that span those systems and get answers that cite the underlying source, instead of a manual join across departments.
With provider and network data modeled as connected data, the team traces which providers, credentials, and claims connect, and mismatches surface as one query, traceable to the source records, instead of appearing later as denied claims.
However complex the data landscape underneath, the engagement itself stays simple.
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.
We ingest the content 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. 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.
Wondering what your compliance workload adds up to? The free GDPR Compliance Calculator models it in about two minutes, with no signup to see the result.
Try the GDPR Compliance 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: how a patient’s records, claims, provider network, and the guideline that applies fit together, without moving protected data out of the boundary it has to stay in. Those answers live in the relationships across your records, claims, and provider systems and the guidelines and notes behind them, 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.
A 30-minute conversation about your data landscape: which systems hold your records, claims, and guidelines, and which question a connected view of them, within your compliance boundary, would answer first. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.