Send
What you send
- Schema and target fields
- Coverage targets and row counts
- Privacy limits and excluded fields
- Output format and downstream use
App Data / Synthetic Data
Available nowTier 1
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.
Send
schema and coverage brief
Run
Synthetic Data Delivery File
Get
Synthetic dataset files
App path
Synthetic Data Delivery File
Available now
InputSchemaFields, slices, privacy limits
JobDataset generationRows, coverage, fixture state
OutputDataset filesData, notes, checksums
NextCoverage gapsWeak slices carried forward
Synthetic Data: Synthetic dataset files
Synthetic data is only useful when the team can see the schema, coverage, privacy limits, weak slices, generation mode, and files being delivered. This app keeps those pieces together.
Customer problem
The customer problem.
Synthetic data is only useful when the team can see the schema, coverage, privacy limits, weak slices, generation mode, and files being delivered. This app keeps those pieces together.
App
Synthetic Data Delivery File
Buyer
Data science, privacy, and AI evaluation teams
Send
schema and coverage brief
Get back
Synthetic dataset delivery file with privacy checks, slice notes, and delivery files.
Status
Available now
Run
What AuraOne does
- Runs the dataset job against the requested schema
- Checks slice coverage, missing fields, and weak segments
- Labels fixture or test mode instead of presenting it as live data
- Packages dataset files, notes, and checksums for delivery
Receive
What you get back
- Synthetic dataset files
- Schema and privacy notes
- Slice coverage summary
- Delivery files and checksums
Steps
How it works.
01
Define the dataset
Set the schema, row count, target slices, output format, and privacy limits.
- Fields are explicit.
- Excluded data stays excluded.
- Coverage targets are visible.
02
Generate the data
AuraOne runs the dataset job against the requested schema and slices.
- Schema and slices stay connected.
- Generation mode is labeled.
- Fixture mode cannot masquerade as live output.
03
Check coverage and privacy
Weak coverage, privacy risk, or missing fields are called out before delivery.
- Slice issues are visible.
- Privacy blockers stop unsafe handoff.
- Quality notes travel with the data.
04
Deliver the files
The final package includes the dataset files, notes, and checksums.
- The receiving team sees what was generated.
- Weak areas are named.
- Next runs can target the gaps.
What this is good for
- AI eval datasets
- Schema-driven test data
- Synthetic population studies
- Coverage gap analysis
What this is not
- Selling synthetic data as real people
- Replacing real-world measurement when real data is required
Current readiness
Available now for scoped schema-and-slice dataset jobs. Fixture mode is labeled and blocked from live claims.
Start this app