One patient record and clinical AI, inside the hospital.
The Healytix Platformturns the systems a hospital already runs into one FHIR record, with no data migration, then reads it with clinical AI that answers with citations and drafts work for clinicians to approve. One license, running on the hospital's own infrastructure.
Hospitals are data-rich and insight-poor.
Data is scattered
A typical hospital runs a HIS, a LIS, imaging, billing and stacks of scanned reports that do not talk to each other.
AI projects stall at integration
Every new use case starts by rebuilding the same data pipes, so most never reach a ward.
Data can't leave the building
ABDM and the DPDP Act in India, and HIPAA in the US, reward systems that keep records in place and prove how they were used.
We fix the data once, as a platform, so every solution after the first one is faster and safer.
One platform. Every capability included.
Relay and Fabric build one record from the systems you already run. Sage, Conductor and Pulse read it to answer, act and watch. CareOps and Lumen are the platform pre-configured for hospitals and cancer centers.
- Your systemsHIS / EMR · Lab system (LIS) · HL7 feeds · Imaging reports · Scanned PDFs
- Spine · Data layerConnectRelay · Interoperability
Legacy databases, HL7 feeds and scanned documents in. Validated FHIR out. No migration.
- RecordFabric · FHIR record store
One versioned FHIR R4/R5 record per patient, served through SMART on FHIR APIs.
- Cortex · Intelligence layerKnowSage · Clinical knowledge
Answers from your records, protocols and SOPs, with the exact passages used.
→ Cited answers: Clinicians
- ActConductor · Agents & workflows
Agents draft prior-auth, discharge and claims work, then wait for a named person to approve.
→ Drafted work: A named approver signs
- SeePulse · Analytics
Live dashboards, bed and revenue forecasts, and anomaly alerts.
→ Dashboards & alerts: Hospital leadership
- Sage gives Conductor its evidence, Pulse alerts can start Conductor workflows, and approved actions are written back to Fabric.
- Solutions on the same platform
Four decisions most health-tech stacks get the other way round.
Zero-migration interoperability
Usually: a long data migration before anything works.
Point Relay at the database you already run. It answers as a FHIR R4 API, and nothing has to move.
AI at design time, determinism at runtime
Usually: an LLM improvising on every request.
AI drafts mappings and rules once, a human approves them, and production runs the approved version the same way every time.
Models travel. Data doesn't.
Usually: pool every hospital's data in a vendor cloud to train.
Federated learning sends the model to each hospital and brings back only what it learned. Patient records never leave campus.
Audit is a query
Usually: weeks of screenshots before every audit.
Every AI action writes a verifiable evidence record: inputs, sources, policy, approver. Proving compliance becomes a search.
A language model that answers from the record, and shows you where.
State-and-Memory Reading Inference with Traceable Information
- Answers from the patient record, with the source for every answer
- Says “not documented” when the record is silent, instead of guessing
- Same effort per question, whether it's 10 notes or 10 years of notes
- Update a guideline or a code once, with no retraining
What was the patient receiving when the scan showed progression?
Third-line cetuximab
What is the HER2 status?
Not documented in the recordNo model skips a rung.
Clinical AI fails when a good benchmark is mistaken for good care. Every Healytix model climbs the same five rungs, and we tell you exactly which rung it is on.
Ask us for the current stage and evidence pack for any model during your evaluation.
- 01
Synthetic & literature
Models are built on synthetic cohorts and published clinical evidence. This proves the pipeline works. It proves nothing about patients.
- 02
Retrospective hospital data
Tested on de-identified records from partner hospitals, with performance reported by age, sex and site rather than as one average.
- 03
Silent prospective
Runs live in shadow mode. Predictions are logged and compared with outcomes, and no clinician sees them yet.
- 04
Clinician-in-the-loop pilot
Outputs reach clinicians as suggestions with cited evidence. Every accept, edit and override is recorded and fed back.
- 05
Regulatory pathway
Models that make clinical claims go through the applicable pathway, such as CDSCO SaMD in India or FDA clinical decision support guidance in the US.
Buy one platform. Keep everything you build.
Start with one capability or solution. Everything you switch on later reads the same record.