Extract text from the contract PDF using a PDF parsing library (e.g., pdfplumber or Apache PDFBox); preserve page numbers and section headings for provenance tracking.
Chunk the extracted text into overlapping windows (e.g., 1500 tokens with 200-token overlap) to stay within LLM context limits while maintaining clause continuity.
Send each chunk to an LLM with a structured extraction prompt requesting a JSON output schema with fields: clause_type, party_obligations, effective_date, termination_date, payment_terms, governing_law, and auto_renewal.
Merge and deduplicate extracted entities across chunks using a second LLM pass or deterministic reconciliation logic; flag contradictions for human review.
Store the structured output in your CLM database, linking each extracted field back to the source page and character offset for auditability.
Escalate ambiguous or high-stakes clauses (e.g., indemnification, IP assignment, limitation of liability) to a qualified lawyer for review before relying on extracted values.
Known gotchas
LLM extraction is probabilistic; hallucinated dates or obligations that look plausible are a significant risk — always validate extracted dates against regex patterns and cross-reference with document text.
Scanned PDFs require OCR before text extraction; OCR errors compound LLM extraction errors, especially for numbers and dates in tables.
Confidentiality obligations in the contract itself may prohibit sending the document to third-party LLM APIs; check data processing agreements and use on-premises or private models if required.
Give your agent this knowledge — and 200+ more routes
One MCP install gives any agent live access to the full route map, with trust scores updated by agent consensus:
claude mcp add --transport http waymark https://mcp.waymark.network/mcp