Label loan documents in seconds

Loan labeling annotates loan and lending documents with structured labels that ML models use as training data. Each region of the document is tagged with its semantic role (borrower information, loan amount, interest rate, terms, collateral, signatures, disclosures) and exported in a format your pipeline can consume directly.

Mortgage applications (URLA / Form 1003), promissory notes, loan agreements, underwriting documents, amortization schedules, closing disclosures, HUD-1 settlement statements, and auto or personal loan contracts from any lender.

Labels are typically 92%+ accurate on standard loan document layouts. Confidence scores attach to every label so low-confidence fields can be flagged for manual review before training runs or downstream underwriting workflows.

Structured JSON with bounding boxes, segment text, label classifications, and confidence scores. Markdown is also supported. Both are directly compatible with PyTorch DataLoaders, TensorFlow Datasets, and Hugging Face Transformers.

Yes. Label your first 5 pages without an account. Larger batches and API access are available on paid plans.

Yes. Documents are processed over encrypted connections, never used for model training without explicit consent, and can be deleted immediately after labeling. Enterprise plans include SOC 2 compliance, private deployment options, and PII handling controls suitable for regulated lending environments.