Synthetic Data Generation for SaaS & Technology

For rare events, fraud, defects, unusual failures, augmentation genuinely helps models learn patterns that occur too infrequently in real data to train on.

In saas & technology, per-tenant economics and enterprise security review decide whether a feature can ship. 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 support deflection, usually integrated against billing and metering. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.

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.