What it is
STICKBUG produces labeled, fully synthetic mortgage documents for the teams building document AI. Every document ships with field-level ground truth, so what your model extracts can be checked against a known answer from the first training run. No hand-labeling, no redacting real files, no PII in your pipeline.
What you get
Ground Truth: Every field is labeled at the moment of generation, so your OCR, extraction, and classification models train and validate against known answers instead of hand-labeled samples.
Volume: Generate large sets of realistic documents on demand rather than scraping or anonymizing scarce production files.
Coverage: Produce the rare document variants and edge cases that are almost impossible to find in legally restricted production data.
Realistic Degradation: Optional image quality and degradation controls (blur, skew, noise, scan artifacts) so your models learn on documents that look like what they will see in production, not just clean renders.
Safety: No real borrower PII enters your training pipeline or leaves your control.
When to use it
Training and testing OCR, document classification, and data extraction models
Building labeled test sets to measure extraction accuracy
Augmenting thin or imbalanced document types in your training data
Sharing realistic, labeled documents with vendors or partners without privacy exposure
Why it works
Document AI teams need labeled files to train and test, and labeling is the slow, expensive, PII-exposed part of the job.
Stickbug removes that bottleneck: realistic documents, labeled from the start, with the ground truth already attached. Your team spends its time on the model, not on annotating loan files it was never supposed to touch.
