The clinical data foundation your AI deserves.
We build clean, normalized, linked datasets — with de-identification, governance and FHIR / OMOP modeling — so your analytics and ML teams stop fighting CSVs and start shipping models.
Most healthcare AI fails on the data, not the model
Inconsistent identifiers
Subjects, providers and encounters scattered across systems with no resolved keys.
Unmodeled context
Labs without units, meds without RxNorm, diagnoses without ICD-10 mapping — models learn noise.
Unsafe data sharing
Teams ship raw PHI to notebooks because the platform has no governed sandbox.
From raw clinical data to ML-ready features
Ingest
2–3 weeksStream EHR, EDC, claims, labs and devices into a governed lakehouse with schema evolution and replay built in.
- Source access & data sharing agreements
- Lakehouse landing zone
- PHI / consent inventory
- Streaming & batch connectors
- Bronze + silver lake zones
- Schema registry & replay tooling
What ML teams notice first
A foundation your AI roadmap can build on
- Patient / encounter master index
- FHIR R4 + OMOP CDM dual modeling
- RxNorm, LOINC, SNOMED CT and ICD-10 mapping
- De-identification (Safe Harbor + Expert Determination)
- Feature store with versioning and lineage
- Governed notebook / Vertex AI / SageMaker sandbox
- Model evaluation harness and bias monitoring
- Audit-ready access logs and consent enforcement
Built on the cloud you already use
GCP
BigQuery + Cloud Healthcare API + Vertex AI — plus help applying for GCP credits where eligible.
AWS
HealthLake + Redshift / Lake Formation + SageMaker with Bedrock-ready data.
Azure
Health Data Services + Synapse / Fabric + Azure ML with Purview governance.
AI dataset questions
Do you do de-identification?+
Yes — both Safe Harbor and Expert Determination paths, with documented methodology and re-identification risk reports.
FHIR or OMOP?+
Usually both. FHIR for clinical interoperability and OMOP for analytics and ML — modeled from the same source of truth.
Can you support LLM / RAG use cases?+
Yes. We build governed embedding pipelines, terminology-aware chunking and retrieval indexes over clinical content.
How do you prevent PHI leaking into models?+
Through environment segmentation, de-id at ingest, column-level policies and pre-deployment scanning of artifacts.
Give your AI team the data foundation it actually needs.
Tell us your use case — we'll send a reference architecture and a 6-week starter plan.
- ✓ Senior engineer reviews your inquiry
- ✓ Reply within 1 business day
- ✓ US-based, HIPAA-aware
- ✓ Scoped plan in 48 hours