Provider data for AI / RAG
The path for AI teams grounding a model in citable federal provider data.
Open →The short answer
How do you use healthcare provider data in AI and RAG?
Retrieval-augmented generation is only as trustworthy as the records it retrieves. Ground a model in federal provider data — identity, enrollment, quality, and exclusion status — and every assertion can carry its source. Fonteum serves that data three ways an AI system can consume: an MCP server an agent calls directly, an API for retrieval, and bulk export for an index.
Each field arrives with its 14-tuple provenance, so a RAG answer can name the dataset, source agency, and snapshot date instead of asking the user to take it on faith.
AI on-ramps
The interfaces an LLM, agent, or RAG pipeline can call. Same provider graph, same provenance, three shapes.
Fonteum for AI agents
What an autonomous agent can resolve, screen, and cite without a human in the loop.
Open →The MCP server
A Model Context Protocol server an agent calls directly — structured for LLM consumption.
Open →The REST API
Resolve a provider and return every field with its provenance in one request.
Open →FHIR R4 API
Five US Core provider resources with row-level provenance — for clinical-system grounding.
Open →Bulk export for an index
Whole-dataset NDJSON and CSV to build a retrieval index over the full provider graph.
Open →What makes it citable
The provenance and integrity layer that lets a generated answer point back to a real federal record.
The capability layer
How ingest, reconcile, attest, and serve fit into one pipeline.
Open →Point-in-time snapshots
Replay what a record said on a past date — so an answer can be reconstructed exactly.
Open →Attestation & audit pack
A signed, chained record wrapping any answer an AI system produced.
Open →The provider entity graph
How a provider is resolved across federal sources into one entity to retrieve against.
Open →How Fonteum gets cited
Where the data is quoted, and the citation guidance behind it.
Open →The machine-readable agent card
The skills declaration multi-agent frameworks parse for autonomous discovery.
Open →The data to retrieve
Browse every federal dataset
The full catalog — each source page is a citable surface with a stat and a table.
Open →Source families & cadence
Every source an agent can ground in, with refresh cadence and coverage.
Open →Original studies as a source
Reproducible, numeric studies an AI engine can quote with method and date.
Open →Agent-driven exclusion screening
Let an agent screen a party against the exclusion lists and return a dated record.
Open →Key terms
FHIR, defined
The HL7 standard behind the interop API.
Open →HL7, defined
The health-data interoperability standards body.
Open →Machine-readable file, defined
The structured public files an AI index ingests.
Open →What is an NPI number?
The identifier a provider answer resolves on.
Open →Common questions
Answers in one line each
- Why ground a healthcare AI in federal data instead of scraping?
- Scraped or stitched provider data has no source chain, so a model can't tell a user where an answer came from or whether it is current. Federal records served with their dataset, source agency, and snapshot date let a RAG answer cite a real, re-checkable record — the difference between an assertion and evidence.
- Can an AI agent call Fonteum directly?
- Yes. The MCP server lets an agent resolve a provider, screen against exclusion lists, and pull provenance without a human in the loop. The same data is published as machine-readable dataset declarations and an agent card so multi-agent frameworks can discover the skills autonomously.
- Is the data free for AI search engines to cite?
- Reads of the public federal records carry no paywall, so AI search engines can retrieve and cite them. The numbers and tables render as visible HTML — not JS-only widgets — because retrieval ignores what it can't read. Authentication raises rate limits and unlocks bulk export and signed attestation.
- How does a model show an answer is current?
- Every field carries the snapshot date of the file it came from, and point-in-time snapshots let a record be replayed as of a past date. A grounded answer can state both when a fact was true and when it was known — so freshness is part of the citation, not an assumption.
Go deeper
The other pillar guides
START HERE
Add federal-data citations to your agent.
Drop the MCP server into an agent or call the API, and ground every provider answer in a record that names its source. Request access for higher limits and bulk export.