Axiomera turns the feeds you already have. HL7v2, FHIR of every flavor, C-CDA, claims. Into one longitudinal FHIR R4 record with LOINC, SNOMED CT, and RxNorm bound to every field, each carrying a calibrated confidence score. Runs in your cloud. Keep your Redox.
{
"resourceType": "Bundle", "type": "searchset", "total": 1,
"meta": { "source": "axiomera/dil", "feeds_merged": 9 },
"entry": [{
"resource": {
"resourceType": "Observation",
"code": { "coding": [{
"system": "http://loinc.org",
"code": "2345-7",
"display": "Glucose [Mass/volume] in Serum or Plasma" }] },
"valueQuantity": { "value": 182, "unit": "mg/dL", "system": "http://unitsofmeasure.org" },
"_axiomera": {
"confidence": 0.97, // calibrated, per field
"lineage": "site-04/ORU^R01/OBX-3", // exact source
"escalation": null // 0.71 would route to a human
}
}
}]
}Two hospital HL7v2 feeds, an Epic FHIR endpoint, an athenahealth endpoint, a C-CDA dump, claims from a payer API, and your own app's data. Same patient, nine dialects.
What actually arrives on the wire. Local code, no unit standardization, vendor-flavored segment layout.
OBX|1|NM|GLU^Glucose Lvl^L|1|182| mg/dL|70-110|H|||F|||20260714
Same observation, standardized and bound. With the score visible. A 0.71 goes to a human queue instead of production. The governance is the feature.
code: LOINC 2345-7 units: mg/dL (UCUM) confidence: 0.97 lineage: site-04/ORU/OBX-3
One call returns the whole longitudinal record. Same layer also lands as warehouse tables for your RAG and analytics pipelines.
GET /fhir/Patient/{id}/$everything
→ 9 feeds merged
→ deduped, coded, lineaged
→ your feature ships
Networks and API vendors get data to you. None of them makes six versions of the same lab test resolve to one LOINC code with a confidence score your model can trust.
Keep the connectivity you have. Axiomera is not a data network, has no patient panel, and moves no data between organizations. It transforms the feeds already landing in your environment.
This is the part that matters if you're shipping LLM features on clinical data.
Not a global accuracy claim. A score on each mapping, so you can set your own thresholds for what reaches users.
Every harmonized field points back to the exact source message and segment. Grounding your RAG pipeline stops being guesswork.
Below-threshold bindings route to a reviewer and never silently commit. Your clinical advisors will ask about this; the answer is built in.
$everything example at the top of this page is real output shape against a synthetic patient, and early-access teams get the full reference.Papers published in Elsevier and Frontiers journals. The harmonization papers are under peer review, and each one is labelled with its status. Read the research →
Federated learning across 47 U.S. healthcare institutions, published in Informatics in Medicine Unlocked. Only differentially private gradient histograms crossed the network. Read the paper →
App 19/181,522, Evolutionary Neural Networks, allowed for issuance. Additional applications are pending.
Native app path on Snowpark Container Services; also AWS, GCP, Azure. SOC 2 Type II in progress.
Docs, sandbox, and self-service mapping profiles are rolling out to early-access teams now. The first cohort works directly with the engineers who built the binding engine.
No phone number, no discovery call. Tell us what feeds you have and we'll tell you what comes out.