Axiomera · The Semantic Intelligence Layer for Enterprise Data

01 · The problem

Most AI fails before the answer.

The models are rarely the issue. The data they consume does not mean what everyone assumes it means, and that ambiguity is inherited by every system downstream.

The same lab result is coded one way in the EHR, another in the claims feed, and a third in the pathology report. A patient identifier is stable at one site and refreshed nightly at the next. A vibration threshold lives in Hz here and RPM there. None of it is wrong. All of it is incompatible.

Standard ETL moves these records. It does not make them mean the same thing. So teams spend thousands of engineering hours reconciling meaning by hand, once per source, once per use case, and each new integration adds complexity faster than it adds value.

Axiomera resolves meaning once, upstream, and makes it available to every consumer after. It is production software, not a consulting method or a wrapper on a language model: it classifies fields against ontologies, maps source representations to canonical schemas, harmonizes records across sites, and emits an evidence graph that explains every decision.

Data does not need more models. It needs to mean the same thing in the first place.

"EGFR L858R mutation" as it actually arrives

"EGFR L858R +"EHR note · free text

EGFR_L858Rpathology · structured

variant: c.2573T>Ggenomics panel

↓ resolved ↓

EGFR L858R · LOINC 62862-8canonical

02 · The pipeline

Four models. One governed pipeline.

Axiomera is not a single model. It is four cooperating systems, each peer-reviewed, each versioned, each auditable end to end through Semantic Evidence Graphs.

Step 01 · Classify

SCE

Semantic Classification Engine

Binds raw fields to ontologies, CDT, SNOMED CT, LOINC, ICD-10-CM, ISO 20816, or your own taxonomy, with calibrated confidence and a traceable evidence graph for every decision.

out → semantic types · ICIDs · SEG

Step 02 · Standardize

DIM

Data Interoperability Model

Maps each understood concept into the standards your systems actually speak, FHIR R4, HL7 v2, C-CDA, X12, or your canonical schema, all at once. Standards are targets, not constraints, so one record can emit many formats with round-trip fidelity.

out → FHIR · HL7 · X12 · canonical schema

Step 03 · Harmonize

DHM

Data Harmonization Model

Unifies the same patient across many sources at once, records sitting in Snowflake, in Google Cloud, and On-Prem, then stitches them across time into one longitudinal record. Conflicts and duplicates resolve under governance; meaning stays consistent across every site.

out → unified patient record · across sources & time

Step 04 · Orchestrate

EMI

Emergent Mapping Intelligence

The coordination layer. Cross-model conflict resolution, governance boundaries for propagation, composite activation tickets, and feedback loops back into learning.

out → activation decisions

In Heterogeneous records: PMS, EHR, sensors, claims, lab, imaging

Out Harmonized bundles, FHIR resources, evidence provenance, consumable APIs

03 · Watch it run

The architecture, in flight.

Raw source data enters on the left. Ontology-bound, provenance-preserving canonical records exit on the right. Same pipeline, three industries.

One bearing-fault signature. Three sensor vendors. Three formats, units, and timestamps. Axiomera normalizes against ISO 20816 and ATA iSpec 2200, injects fleet priors, and emits a governed health record ready for federated learning across operators.

sensor_feed.raw3 vendors · units mixed

// Modern digital feed
{ "sensor_id": "VIB_3A", "freq": 2847.3,
  "amp": 0.412, "units": "Hz/g" }

// Operator export
{ "tag": "vib-bearing-03", "rpm": 170838,
  "displacement": "0.81mm" }

// Legacy analog CSV
"VIB_03A,HIGH,12/21,0845,2847Hz,bearing-F"

Axiomera

SCE

DIM

DHM

EMI

health_record.jsonISO 20816 · fleet-ready

{
  "observationType": "VibrationSpectrum",
  "sensor": { "iso20816_class": "Class II",
    "ata_chapter": "72-30-00" },
  "measurement": { "frequency_hz": 2847.3,
    "severity": "caution",
    "fault_class": "bearing.outer_race" },
  "timestamp": "2025-12-21T14:00:40Z",
  "_axiomera": { "confidence": 0.994,
    "prior_from_fleet": true,
    "governance_gate": "shadow_validated" }
}

One NSCLC patient. An EHR narrative, a pathology report, a claims record, three fragments of the same truth that do not talk to each other. Axiomera resolves identity, binds concepts across ICD-10, SNOMED, LOINC, and RxNorm, and emits one semantic patient state.

patient_fragments.rawstructured + free text

// EHR progress note (unstructured)
"67F NSCLC dx Aug 2024, s/p 4 cycles
 cisplatin/pem. Progression 03/2026.
 EGFR L858R mutation +."

// Pathology (structured)
{ "stage": "IIIB",
  "biomarker": "EGFR_L858R" }

// Claims (admin)
{ "icd": "C34.90", "cpt": "96413" }

Axiomera

SCE

DIM

DHM

EMI

patient_state.jsonunified semantic record

{
  "patient_ref": "ICID-3b9f12e4",
  "diagnosis": { "icd10": "C34.90",
    "snomed": "254637007",
    "stage": "IIIB" },
  "biomarkers": [{ "gene": "EGFR",
    "variant": "L858R",
    "status": "positive" }],
  "treatment_history": [{
    "regimen": "cisplatin + pemetrexed",
    "cycles": 4 }],
  "_axiomera": { "confidence": 0.951,
    "sources_merged": 3 }
}

04 · Where it runs

Axiomera runs inside your data platform.

Your data already lives in Snowflake, Google Cloud, AWS, or Azure. Axiomera deploys as a container that runs next to that data, so raw records never leave your security boundary. No extract. No round-trip. No new place for your most sensitive data to sit.

For regulated industries, that is the difference between a year of legal review and a deployment your compliance team can sign off on.

Your security boundary

Your dataSnowflake · GCP · AWS · Azure

Axiomeraruns in-place

Governed outputFHIR · bundles · APIs

Only governed artifacts cross the line. Raw data stays put.

SnowflakeMarketplace · native app in build

Available on Snowflake Marketplace, with a native app running on Snowpark Container Services so enrichment happens inside your Snowflake account. Data is harmonized where it sits and never leaves the environment.

Google CloudMarketplace · finalizing

Google Cloud Marketplace listing in final stages, giving GCP and BigQuery customers a procured, in-environment path to semantic harmonization.

Supported environment

Deploys into your AWS account as a containerized service. Common for embedded partners, payers, and defense programs that standardize on AWS.

Microsoft AzureSupported environment

Runs inside Azure for multi-cloud enterprises. The same container is portable across clouds and open table formats, so a multi-region, multi-cloud footprint stays interoperable.

05 · The proof

Three verticals. One infrastructure.

The proof that Axiomera is infrastructure, not a domain app, is that the same pipeline runs underneath three very different products, each in its own regulatory world, each with its own ontologies.

Vertical 01Dental interoperability

Conduit

The intelligence behind every translation in a standards-based dental data exchange.

Conduit is HeyDonto's exchange platform connecting medical EHRs, dental practice systems, payer networks, and patient-access endpoints over FHIR R4, SMART on FHIR, and TEFCA-aligned protocols. Every record it moves, a referral, a prior auth, a cardiac-clearance request, is identified, bound to CDT, SNODENT and SNOMED CT, and emitted as canonical FHIR by Axiomera. One connector per system, not one per pair, because FHIR is always the center.

Visit Conduit →

Axiomera's role

Binding

CDT · SNODENT · SNOMED CTSCE identifies dental, medical, and administrative concepts regardless of source system.

Output

FHIR R4 canonicalEvery exchange emits a FHIR resource with full provenance.

Context

OHIA founding memberHeyDonto is a founding member of the Oral Health Interoperability Alliance; Conduit is the reference implementation.

Vertical 02Aerospace · predictive maintenance

Nadir AI

Every aircraft teaches the fleet. Axiomera is how the lessons travel.

Nadir AI turns heterogeneous aircraft vibration data, collected on the ground, in flight, and across operators, into semantically governed health records that feed federated fleet learning. Axiomera binds raw sensor features to ISO 20816, ATA iSpec 2200, and S1000D, so a fault signature found on one aircraft is recognized across a fleet without raw data ever leaving the operator. The diagnostic foundation is anchored by the TEC-USA / ACES vibration standard.

Visit Nadir AI →

Axiomera's role

Binding

ISO 20816 · ATA iSpec 2200Maps heterogeneous vibration features to a common aerospace ontology with provenance.

Federated

Privacy-preservingOperators share governed learnings, never raw telemetry, under differential-privacy aggregation.

Governance

Shadow → approve → rollbackAviation-grade gates: local improvements propagate only after validation, and roll back fast.

Vertical 03Oncology · clinical intelligence

Quantara Health AI

A unified record for the most heterogeneous data in medicine.

Healthcare data is the hardest semantic problem: EHR narratives, pathology, genomics, imaging biomarkers, claims, and trial criteria all have to resolve to the same patient and disease state at the same point in time. Axiomera delivers that unified record, grounded in peer-reviewed research across hepatocellular carcinoma, non-small cell lung cancer, and preoperative glioma grading. It is the infrastructure behind Quantara's eligibility gap detection, surfacing when a patient crosses a threshold for a therapy, re-authorization, or trial.

Visit Quantara Health AI →

Axiomera's role

Binding

ICD-10 · SNOMED · LOINC · RxNormUnifies oncology concepts across EHR, pathology, genomics, claims, and trial criteria.

Validation

47 institutionsPublished federated framework validated across 47 US healthcare institutions.

Research

Peer-reviewedTranslational work in HCC, NSCLC, and multi-center glioma grading anchors the portfolio.

06 · Why it holds

What separates infrastructure from a methodology.

i.

Peer-reviewed and patented

11+ publications and 11 patents pending across US, European, and PCT jurisdictions. The mathematics is published. The applications are protected. Security teams can audit both.

ii.

Production software, not a deck

Event-driven on Kafka and Schema Registry, orchestrated with Temporal, versioned and pinned end to end. A deployed platform, not a consulting framework.

iii.

Vertical-agnostic by design

The ontology is configured, not hardcoded. Dental, aerospace, and oncology all run the same SCE → DIM → DHM → EMI pipeline with the same governance guarantees.

iv.

Governed for regulated industries

Active-control gates, Semantic Evidence Graphs, and fairness-preserving constraints mean every decision is auditable back to the evidence that produced it.

Resolve meaning before your data is consumed.

Most AI fails before the answer.

Four models. One governed pipeline.

The architecture, in flight.

Axiomera runs inside your data platform.

Three verticals. One infrastructure.

Conduit

Nadir AI

Quantara Health AI

What separates infrastructure from a methodology.

Peer-reviewed and patented

Production software, not a deck

Vertical-agnostic by design

Governed for regulated industries

Infrastructure for the next decade of enterprise data.