Synthetic Data Generation for Pharmaceuticals & Life Sciences

Synthetic is not automatically anonymous. A poorly generated set can leak information about the individuals it was derived from, which is why we test re-identification risk rather than assuming safety.

In pharmaceuticals & life sciences, GxP validation means every system change needs documented evidence before it reaches production. 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 literature monitoring, usually integrated against SAP. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. You own the code, the models where they are open-weight, and the documentation to run it without us.