Label financial documents in seconds

Financial labeling annotates financial documents with structured labels that ML models use as training data. Each region of the document is tagged with its semantic role (line item, total, vendor, account number) and exported in a format your pipeline can consume directly.

Invoices, receipts, bank and credit card statements, 10-K and 10-Q filings, balance sheets, income statements, cash flow statements, audit reports, loan documents, and tax forms (W-2, 1099, 1040, K-1).

Labels are typically 90%+ accurate on standard financial document layouts. Confidence scores attach to every label so low-confidence regions can be flagged for review.

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 and private deployment options.