Label legal documents in seconds

Legal labeling annotates legal documents with structured labels that ML models use as training data. Each region of the document is tagged with its semantic role (clause type, party, obligation, citation, effective date) and exported in a format your pipeline can consume directly.

Contracts (NDAs, MSAs, SaaS agreements, employment contracts, leases), court filings (motions, briefs, complaints, orders), case law and judgments, patents and IP filings, compliance and regulatory documents, depositions, and discovery materials.

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

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, attorney-client privilege safeguards, and private deployment options.