What it is
STICKBUG produces complete, fully synthetic loan files where every value reconciles across every document. For teams building agentic or automated underwriting, that cross-document consistency is what lets a model learn to reason across the whole file instead of reading it one document at a time.
What you get
Cross-Document Consistency: Income, assets, property, employment, and transaction data agree from one document to the next, so your model trains on files that hold together the way real loan files are supposed to.
Complete Files: Full loan package generation so your model sees the whole picture it will have to decision on in production.
Edge Case Generation: Generate the rare and difficult loan scenarios that are hard to source from restricted production data and even harder to assemble by hand.
Ground Truth Lineage: Every file ships with complete data lineage, so what your model decisions against is known and traceable from the start.
Safety: No real borrower PII enters your training pipeline or leaves your control.
When to use it
Training agentic or automated underwriting models on complete loan files
Testing model behavior against controlled, reconcilable scenarios
Building validation sets where the correct outcome is known in advance
Stress-testing decisioning logic against edge cases without real files
Why it works
An underwriting model has to reason across the entire file, and it can only learn that from files where everything actually reconciles. Real loan data is exactly that, and exactly what these teams are least able to use freely. Redacted or stitched-together data breaks the internal consistency a model needs.
STICKBUG generates complete files that hold together across every document, so your model learns to underwrite on data that behaves like the real thing, with no real borrower involved.
