Synthetic Data Generation for Healthcare & Hospitals

We validate transfer. A model that performs on synthetic data and fails on real data has learned the generator rather than the phenomenon, and that check is the deliverable.

In healthcare & hospitals, clinical safety and patient privacy mean nothing ships without human oversight and a complete audit trail. That single fact reshapes how synthetic data generation has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is prior authorisation and insurance paperwork, usually integrated against PACS and RIS. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Multi-model by default, so a provider outage is a routing decision rather than an incident. You own the code, the models where they are open-weight, and the documentation to run it without us.