The Synthetic Engine.
Production-grade synthetic data, on demand.
A machine-learning engine that learns the statistical shape of your data and generates synthetic datasets that behave like the real thing - so teams can build, test, and share without touching a single real record.
How it works
Learn the structure. Generate the data.
The Synthetic Engine learns the distributions, correlations, hierarchies, and constraints between your data layers - then samples entirely new records from that model. It reproduces the analytical shape of your data without reproducing the data itself.
Share it with vendors, train and evaluate models, or stand up realistic dev and staging environments - safely. And because it pairs with Simulation, you can red-team a policy against synthetic adversarial traffic before a single live request is affected.
Under the hood
Cutting-edge generative modeling.
The Synthetic Engine uses cutting-edge deep generative models to learn the joint distribution of your data across columns, tables, and layers. Rather than perturbing or masking real rows, it samples entirely new records from the learned distribution - preserving marginal and conditional statistics, cross-column correlations, and referential constraints. Differential-privacy controls are available to bound any residual leakage, so fidelity and confidentiality are tuned to your risk tolerance.
Context abstraction
Your prompts are data too.
A prompt can carry your most proprietary context: the deal terms, the codebase, the strategy behind the question. The Synthetic Engine extends the same principle it applies to datasets - synthetic out, real never - to the prompt itself. Sensitive values are swapped for format-preserving synthetic stand-ins before a request leaves, and proprietary context is generalized into an abstract, synthetic version of the problem.
The external model solves the abstract problem. On the way back, the response is translated to your real context inside your own environment - so the workflow is seamless for the person who asked, and the model provider only ever saw a synthetic twin of the question. Every translation is recorded to the audit Ledger like any other decision.
What it gives you
Realistic data, none of the risk.
Statistical fidelity
The engine models distributions, correlations, and the relationships between data layers - so synthetic output behaves like the real thing under analysis, not just at a glance.
Referential integrity
Keys, hierarchies, and the constraints between tables are preserved end to end, so generated datasets join and validate exactly like production data.
Zero real records
Because the engine learns structure rather than copying rows, the output contains none of your sensitive data. Privacy is a property of construction, not a redaction step.
Feeds Simulation
Generate synthetic adversarial traffic to red-team a policy before it goes live - so you know exactly what Quest's rules will block, redact, or escalate.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.