Input
What you provide
- Schema and target fields
- Coverage targets and row counts
- Privacy limits and excluded fields
- Output format and downstream use
App Data / Synthetic Data
Synthetic Data Delivery File
Create a synthetic dataset from a schema.
Send a schema, coverage targets, privacy limits, target slices, row counts, and output format. AuraOne runs the dataset job, checks coverage and privacy limits, and prepares the delivery files. Ready to scope now. Start with one real batch and get back the output your team can use.
Available nowAvailable now. Start with one dataset or population study.
Input, work, and output
Work
What happens
- Runs the dataset job against the requested schema
- Checks slice coverage, missing fields, and weak segments
- Clearly labels synthetic output and its generation method
- Packages dataset files, notes, and checksums for delivery
Output
What you receive
- Synthetic dataset files
- Schema and privacy notes
- Slice coverage summary
- Delivery files and checksums
Decision flow
Move from intake to review and handoff with clear owners at every step.
01
Define the dataset
Set the schema, row count, target slices, output format, and privacy limits.
Fields are explicit.
02
Generate the data
AuraOne runs the dataset job against the requested schema and slices.
Schema and slices stay connected.
03
Check coverage and privacy
Weak coverage, privacy risk, or missing fields are called out before delivery.
Slice issues are visible.
04
Deliver the files
The final package includes the dataset files, notes, and checksums.
The receiving team sees what was generated.
What you receive
Synthetic Data Delivery File deliverables
- Application
- Synthetic Data Delivery FileAvailable now
- Buyer
- Data science, privacy, and AI evaluation teamsProgram owner
- Input
- schema and coverage briefPrompt sets / schema files / dataset slices
- Review
- Dataset ownerRuns the dataset job against the requested schema Checks slice coverage, missing fields, and weak segments Clearly labels synthetic output and its generation method Packages dataset files, notes, and checksums for delivery
- Output
- Synthetic dataset delivery file with privacy checks, slice notes, and delivery files.Checked synthetic datasets, generation controls, and handoff files your team can keep.
- Scope
- $50K-$175K typical engagementTypical engagement
What the workflow supports
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| Input is ready | Schema and target fields Coverage targets and row counts Privacy limits and excluded fields Output format and downstream use | Source, access, format, validation, project rules, and responsible owner. | Best for teams with a defined source, owner, and delivery goal. |
| Work can advance | Runs the dataset job against the requested schema Checks slice coverage, missing fields, and weak segments Clearly labels synthetic output and its generation method Packages dataset files, notes, and checksums for delivery | Stage, owner, checks, exceptions, reviewer notes, and required next action. | Your team can see blockers, owners, and next actions throughout the workflow. |
| Output can be released | Synthetic dataset files Schema and privacy notes Slice coverage summary Delivery files and checksums | Sources, criteria, reviewers, decision, manifest, version, and audit history. | The final package is configured for the application, project, and review requirements. |
Good fit
What this application is for
- AI eval datasets
- Schema-driven test data
- Synthetic population studies
- Coverage gap analysis
Boundary
What this application is not
- Selling synthetic data as real people
- Replacing real-world measurement when real data is required
Availability
Available nowAvailable now for scoped schema-and-slice dataset programs with provenance and validation details included.