Known-answer evaluation
Every generated scenario can carry expected fields, decisions, citations and prohibited disclosures.
UNSTRUCTURED TEST DATA THAT BEHAVES LIKE PRODUCTION
Turn unstructured-data variability into a testable system—not a collection of anecdotes.
Build deterministic regression suites for OCR, speech, multimodal automation and agentic workflows using realistic inputs with known expected outcomes.
SENSITIVE SOURCE
↓ POLICY
CONTROLLED OUTPUT
For Quality Engineering
Move from a promising demo to a measurable workflow.
The model is only one component. Reliable enterprise outcomes require representative inputs, explicit expected behavior and evidence when something changes.
Edge-case coverage
Generate damaged scans, missing fields, conflicting evidence, fraud patterns and rare policy exceptions.
Release confidence
Compare model and workflow versions against the same repeatable scenario catalog before production.
Questions your data should answer
Make success observable before production.
- 01Did the system extract the right fields?
- 02Did it reach the expected outcome?
- 03Did it expose prohibited information?
- 04Did it escalate rather than guess?
Product fit
A clean division of work.
Use real examples only when they are necessary. Use synthetic scenarios to scale coverage and repeatability.
01GritRedactCONTROLLED REAL DATA
Prepare approved production examples for defect analysis.
Detect, review, remove and verify sensitive information according to a defined downstream purpose.
- Policy-driven
- On-premise
- Auditable
02GritRenderSYNTHETIC EVALUATION
Turn those patterns into repeatable synthetic regression suites.
Generate realistic documents, images and audio with labels, expected results and controllable variation.
- Known ground truth
- Edge-case coverage
- Repeatable runs
Start with the blocked workflow
Make sensitive content usable—on terms your enterprise can defend.
Show us the source content, workflow and downstream users. We’ll identify where redaction, synthetic data or a different control is actually appropriate.
Request a working session ↗