From a data requirement
to a repeatable workflow.
Define → Generate → Validate → Export → Iterate. Every step serves the model, test, or experiment you want to build.
Book a meetingDefine
Start with a schema, a scenario, or a gap in your model coverage.
Specify field types, relationships, class balance, allowed ranges, and the rare conditions you want to represent. Agree on acceptance criteria before generation.
Generate
Create targeted samples around your constraints and conditions.
Build examples for the scenario families that matter. Use deliberate sampling to explore underrepresented classes and difficult operating conditions.
Validate
Inspect fidelity, utility, coverage, and privacy risk.
Check constraints and distributions, then assess utility against the downstream task. Review similarity to source records separately from statistical quality.
Export
Move approved datasets into your training and testing workflow.
Agree on a format, destination, and access policy. Keep the generation configuration and validation findings with the dataset so teams can understand its limits.
Iterate
Refine the data as your model and requirements evolve.
Use evaluation findings to update scenario definitions. Keep a stable regression set while exploring new configurations for the next model iteration.
Bring a scenario.
We’ll help define the dataset.
You do not need to share production records to start a conversation. A schema, a use case, and a description of the gaps are a useful first step.
Agree on what
“good” looks like.
Generation and evaluation should answer different questions. A plausible dataset still needs evidence that it is useful for your task.
- Schema and relationship consistency
- Coverage of specified scenarios
- Statistical and domain plausibility
- Downstream model or test utility
- Privacy review and use restrictions
Build the dataset your model is missing.
Bring your toughest data problem.
Let’s work out what comes next.