Known-answer evaluation
Use synthetic inputs with known values to check extraction and downstream results. Test GritTransform or your existing document-processing system against the same cases.
UNSTRUCTURED TEST DATA THAT BEHAVES LIKE PRODUCTION
Test the documents, variations and failures your systems will face.
Build deterministic regression suites for OCR, speech, multimodal automation and agentic workflows using realistic inputs with known expected outcomes.
CHOOSE THE OUTPUT YOUR WORKFLOW NEEDS
GritRedact
Remove sensitive information
Documents, images & audioGritTransform
Extract structured JSON
PDFs & imagesGritRender
Generate synthetic test content
Documents, images & audioFor Quality Engineering
Turn realistic inputs into repeatable tests.
Give your test suite representative content, known expected values and controlled variations. Compare results when an application, model or processing step 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.
Protect sensitive content with GritRedact, extract JSON from PDFs and images with GritTransform, and generate realistic examples with GritRender. All three are available offline on-premises or as sales-assisted SaaS.
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-premises or SaaS
- Auditable
02GritTransformSTRUCTURED DOCUMENT DATA
Make extraction results testable.
Save an extraction schema, call it through an API and compare the returned JSON with expected values in your tests. Use source highlights to investigate mismatches.
- Repeatable configurations
- Structured results
- Source-level investigation
03GritRenderSYNTHETIC 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 your workflow
Put your unstructured data to work.
Tell us what your team needs to achieve. We’ll help you choose the right products, work through your requirements and explore on-premises or SaaS deployment.
Request a working session