Scenario and generation design
A table of scenarios to cover, the schema and relationships, constraints, rules for any real examples used to guide generation, and a recipe matched to the target use.
Synthetic data generation services
We generate examples for the scenarios your existing data does not cover. We check each batch against the required schema and review it for the task it is meant to support.
ScopeFrom a scenario pilot to a repeatable generation workflow
Delivery timingSet after we confirm that generation and validation are feasible
How we check itWe show quality through rule checks, sample review, and relevant independent task results. Privacy claims cover only the controls we actually tested.
Generate the examples your real data is missing.
A missing case might be a rare event, an unusual document layout, or a difficult customer request. We choose a generation method for those cases and check the results against independent reference examples and your task requirements.
Good fit
- AI teams with too few examples, or examples that miss important scenarios
- Software teams that need realistic test data without using production records
Not the right fit
- Teams expecting generated data to be automatically anonymous or accurate
- Projects hoping to replace independent real-world evaluation with scores on generated data
What you get
Validated synthetic collection
Generated examples with stable IDs, a record of how each was generated, validation flags, and reviewed rare or difficult cases.
Usefulness and risk report
Results for rule compliance, variety, usefulness for the task, and relevant checks for reproduced sensitive data. Failures and limitations stay in the report.
How it works
Define the missing coverage
We identify which cases your existing data lacks and agree on baselines for judging realism and usefulness for the task.
Generate and review a batch
We compare candidate methods on a small sample, check facts and relationships, and revise the recipe with feedback from your domain experts.
Validate before scaling
We test the accepted batch against a separate reference set or task evaluation, then produce a versioned release with its limitations documented.
Synthetic data generation by use case
Each data type needs its own generation recipe and acceptance checks.
Structured and relational records
We generate tables and linked records for a defined schema, then validate keys, ranges, totals, relationships, and any statistical patterns that approved reference data supports.
Text and conversations
We create task prompts, responses, and multi-turn conversations from approved source material or a scenario specification, then review accuracy, variety, speaker roles, format, and labels.
Targeted edge cases
We fill a coverage gap with rare or difficult examples, clearly labeled as generated. The generation assumptions stay visible, and we compare behavior against independent reference cases.
Scope, cost, and ownership
What we need from you
- Target scenarios, schema, and intended use
- Approved seed material, or a specification for generating data without source records
- A reference task and a reviewer to judge quality
What affects cost
- Scenario diversity, record relationships, and generation volume
- Model , human review, and privacy or realism testing
- Independent reference preparation and the acceptance threshold
Technical scope
- Rule-based, statistical, simulation, and pipelines selected by task
- Generation seeds kept separate from evaluation references, with model and recipe versions recorded
Support and maintenance
We refresh scenarios and validation as your products change, and rerun usefulness and privacy checks when seed data or generation models change.
Common questions
Is synthetic data always private?
No. A system trained or prompted with sensitive source data can reproduce it. We agree on permitted inputs and test for the relevant disclosure risks. Differential privacy is a formal method that limits what the output can reveal about any one record. When it is in scope, it is a separate technical requirement with explicit assumptions and tradeoffs.
Can you generate data without our production records?
Yes, when the use case allows written rules, simulation, or approved domain material. That data can test schemas and workflows. Matching the statistical patterns of real data requires suitable reference data and measurement.
Will synthetic data improve our model?
Not necessarily. It has to be tested. We compare the training or evaluation approach against a baseline, using a separate reference set. Poorly designed synthetic data can introduce errors and amplify biases.