STRESSBENCHAIStressBench AI · Synthetic data & model robustness
Generate targeted synthetic data, expose model failure modes, and measure robustness before your customers do.
Built for AI teams that need evidence, not demos.
Deterministic generationExact ground truthReproducible evaluationCapability 01
Build controlled synthetic cohorts around the conditions your model needs to handle. Keep the data, labels and generation recipes connected.
Explore synthetic data
Capability 02
Go beyond a clean baseline. Measure how performance changes under controlled stress and identify the conditions behind lost recognition.
Explore stress testingMeasured on DocTorture-1K v0.1 (1,000 documents), separate from the 10K v1.0 dataset. Unpaired synthetic cohort means.
Capability 03
Use the failure analysis to recommend a targeted training mix. Generate the next corpus, then measure what changed on the same frozen holdout.
See the workflow
Capability 04
Make every result inspectable. Preserve provenance, recipe versions, labels and checksums alongside the evaluation that informs your next decision.
Explore the evidence
The StressBench platform
Synthetic data is the foundation. Measured evidence closes the loop.
Run your model or API against a fixed evaluation set. Preserve the original response alongside its input and configuration.
Stress environments
One approach to controlled stress. Environments designed around the systems you build.
Our first environment / DocTorture
Two independently versioned artifacts: the DocTorture-10K v1.0 dataset and the DocTorture-1K v0.1 OCR benchmark. Published model scores come from the 1,000-document v0.1 benchmark; they are not measurements of the 10,000-sample v1.0 corpus.
Inside DocTortureResearch & engineering
Build a deliberate path from model failure to targeted data and measured robustness.
