Rockfish generates fully synthetic data, either from a schema you describe or a model learned from your existing data: structurally sound, statistically faithful, and safe to share on day one. Each report below walks one example end to end.
Data generated purely from a schema. No source data needed.
A microservice call graph becomes a complete, correlated OpenTelemetry signal set: distributed traces, RED metrics, and logs, with a planted incident an SRE can find, and it answers the on-call questions a practitioner actually runs.
The full card-payment lifecycle across eight related tables: 10M transactions with every foreign key resolved, causal ordering, and the distributions a fraud team expects, and no real cardholder data.
A model trained on real source data, then used to synthesize new records.
Point Rockfish at a real electronic health record, train a model on it, and generate a synthetic replica across five linked tables with every foreign key resolved and no real patient, statistically faithful and ready to train real models on.
Point Rockfish at a live MySQL database, train a model on it, and generate a synthetic version that keeps the relationships across tables intact and carries no real personal data.
