Explainable healthcare intelligence, grounded in your knowledge graph
Care-Intel GraphRAG translates natural language into multi-hop traversals over a governed Biolink / UMLS / SNOMED graph, then synthesizes citation-backed answers for analysts, copilots, and agents.
GraphRAG starter sample
Designate a sample in Super Admin → Subgraph Marketplace → Listing: Sample, or configure a free download URL in graph-products settings.
GraphRAG, visualized
Natural-language questions resolve to multi-hop traversals over a live clinical knowledge graph — every answer traceable to the patients, conditions, drugs, trials, and providers it was grounded in.
Challenges we solve
Healthcare AI teams hit the same walls — GraphRAG on a governed knowledge graph is how we break through.
Our approach
We combine knowledge graphs, semantic ontologies, hybrid retrieval, and governed LLM orchestration into one enterprise workflow — the same pattern used by leading GraphRAG platforms, applied to healthcare data.
The GraphRAG reference architecture
Care-Intel integrates structured healthcare data into a semantic knowledge graph that acts as the factual backbone for AI. Every GraphRAG answer is grounded in multi-hop traversals — reducing hallucinations and enabling audit-ready outputs for payer, provider, and life-sciences teams.
Ready to build a trustworthy AI foundation on governed healthcare intelligence?
Our GraphRAG capabilities
Domain ontologies, taxonomies, and controlled vocabularies on every node and edge
Deterministic builds from CMS, FDA, trials, and payer data via Super Admin
Ownership meshes, referral networks, ontology crosswalks, composite keys
Graph traversal + embeddings over governed subgraph slices
Verified entities and relationship paths only
Monitor API routes to your chosen LLM provider
Entity lineage, relationship paths, source documents
Warehouse → graph build → GraphRAG output traceability
Permission-aware retrieval and admin-governed products
Plain English over providers, drugs, conditions, trials
Parquet + JSONL for Snowflake, Databricks, copilots
Super Admin GraphRAG agents + corporate portal integration
GraphRAG in action
Production use cases powered by live subgraph products from our knowledge graph.
Payer & value-based care
ACO networks, provider attribution, county-level reach — multi-hop queries across MSSP participants and geographies.
Request demo →Provider & facility intelligence
Ownership meshes, SNF-hospital affiliations, referral networks with CMS-quality signals.
Explore subgraphs →Pharma & clinical trials
Drug–condition–trial evidence chains with MeSH, ICD-10, SNOMED crosswalks for regulatory retrieval.
Schema docs →AI training & copilots
Parquet/JSONL multi-hop packages — nodes, edges, train.jsonl — ready for RAG fine-tuning.
Talk to us →Grounding slices — live subgraph catalog
Every GraphRAG answer can be grounded in these governed products — each with its own entity types, relationship semantics, and live preview API.
How Care-Intel GraphRAG compares
| Capability | Traditional vector RAG | Care-Intel GraphRAG |
|---|---|---|
| Architecture | Vector-first with optional metadata | Native graph + ontology reasoning + LLM grounding |
| Retrieval depth | Single-hop chunk similarity | Multi-hop traversal — entities, relationships, constraints |
| Explainability | Document citations or none | Claim-level citations, entity lineage, relationship paths |
| Healthcare ontologies | Minimal tagging | Biolink, UMLS, SNOMED, MeSH, ICD-10 built in |
| Data products | Ad-hoc exports | Governed subgraph SKUs — Parquet, JSONL, schema docs, Stripe licensing |
| Enterprise integration | Partial, often duplicated | Monitor API · Snowflake · Databricks · Super Admin workbench |
Knowledge graph foundation
GraphRAG sits on top of a production in-memory graph engine (Memgraph) built deterministically from real healthcare sources — with Biolink controlling semantics and UMLS/SNOMED enriching clinical identity.