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Product / Synthetic data
Generate the failures
your training set is missing.
Targeted synthetic document cohorts with exact ground truth, deterministic recipes and a clear path back to every source.
Choose the failure mode.
Control the severity.
Define document classes, condition families, severity bands and ordered mixtures. Generate a corpus whose composition can be inspected, rather than treating augmentation as an invisible training step.
The current document release admits eight idealized conditions. Custom classes and conditions require an agreed scope and validation plan.
How generation is specified
Motion blur / moderate · severity 0.6
Select a cell to inspect the retained specimen.
Illustrative coverage.
Cover a deliberate gap.
Production acquisition failures may be sparse in the training set. A model-specific evaluation can help identify where a targeted synthetic cohort is worth testing.
Record the proposed mix, keep source groups separate from the holdout and measure the result after training.
Synthetic data for model trainingA dataset you can trace.
Delivery includes licensed images, annotations, a generation manifest and checksums. Release version, recipe parameters and source grouping remain part of the data contract.
Rights that fit deployment.
Dataset usage rights are defined in the signed commercial agreement.
Build with evidence
See what breaks
before production does.
Run StressBench against your document model and get a condition-level robustness readout.