Production-like content
Statements, claims packets, identity images, call recordings and multi-artifact cases.
GRITRENDER / SYNTHETIC EVALUATION DATA
Generate the difficult unstructured cases production cannot safely supply.
Create realistic documents, images, audio, identities, transactions and edge cases with known ground truth for AI, automation and agent evaluation.
SENSITIVE SOURCE
↓ POLICY
CONTROLLED OUTPUT
More than realistic PDFs
Generate tests—not just assets.
The durable value is the expected behavior attached to every scenario: what should be extracted, transcribed, decided, cited, withheld and escalated.
Expected outcomes
Field labels, transcripts, decision criteria, citations, privacy assertions and prohibited actions.
Controlled difficulty
Rare conditions, damaged scans, noisy audio, conflicting evidence and adversarial content.
Where it fits
Evaluation and regression for systems built on unstructured data.
Use GritRender to measure OCR, extraction, speech, classification, fraud detection, RAG and agent workflows without expanding production-data access.
- Known-answer scenario manifestsMeasure
- Deterministic generation from seedsRepeat
- Rare and negative case catalogsExpand
- Model and workflow comparisonsDecide
Realism must be validated
Synthetic data is useful only when it represents the target workflow closely enough to predict real performance. GritWorks validates structure, media characteristics, distributions and downstream task behavior against approved benchmarks.
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 ↗