01
Research roots. Real-world rigor.
Built by researchers from UC Berkeley, the University of Oxford, and Stanford University, StressBench helps teams understand where AI fails and build the data to make it better.
We create controlled synthetic datasets, test models under demanding conditions, and connect every result to reproducible evidence. Our focus is simple: give engineering teams a clearer path from model failure to better training data.
02
Our approach
Synthetic data is useful when it answers a specific engineering question. Which conditions are missing from the training set? Where does recognition deteriorate? Can the next model recover those failures on an unchanged holdout?
StressBench connects targeted generation with reproducible evaluation. The goal is to make failure modes inspectable and the next data decision explicit.
03
Evidence before claims
Our technical foundation includes a sealed 10,000-sample v1.0 document corpus and a separate historical 1,000-document OCR benchmark. Dataset releases and benchmark measurements have independent versions and evidence records.
We publish the scope and limitations alongside measurements. Synthetic cohort performance is not a production failure-rate estimate, and a proposed training mix is not proof of improvement.
04
Document AI, then beyond
Electronics / PCB, manufacturing and UAV / drone stress environments are coming soon. Current commercial work is document-focused.
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Work with us
We work with engineering and applied AI teams through dataset licenses and scoped robustness engagements. Tell us the document classes, model boundary and failure modes you need to evaluate.