Test fixture privacy workbench

For software QA teams handling customer-shaped data, turn approved schemas and non-sensitive constraints into validated synthetic test dataset. Address this specific problem: realistic fixtures are difficult to create without copying private records. The aim: constraint-valid test data with documented synthetic provenance. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies approved schemas and non-sensitive constraints, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final validated synthetic test dataset before use. Retain source links and a version history for the next cycle.

How the AI works

Generate examples with deterministic relational validation. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.

Safeguards

Protect secrets, customer data and source code. Use controlled environments, technical review and a recoverable deployment process. Synthetic generation only; no production data training. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.

What to build first

Costed pilot: Synthetic generation only; no production data training. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract schema constraints; generate synthetic cases. Support the third task through an assisted review queue: include edge conditions. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of validated synthetic test dataset. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

What it can connect to

Authorized repositories, technical documentation, application APIs and logs. Approved repositories, application APIs, execution platforms and monitoring systems. Validate current API access and behavior during discovery before promising compatibility. Begin with uploads and exports of approved schemas and non-sensitive constraints. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.

The screens in detail

Show a work backlog, proposed changes and verification results. Link each item to its source configuration, code or data mapping. Provide execution logs and an owner-facing health view. Keep environments and approval states clearly separated so a draft cannot be mistaken for a live change. Open with schema canvas; move into fixture generator for the detailed task; finish in validation results for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.