Ingest the lease document (PDF or DOCX) and extract text using a document parsing library; for scanned PDFs, apply OCR first.
Chunk the document into overlapping segments (e.g., 1500 tokens with 200-token overlap) to handle leases that exceed a single LLM context window.
Prompt the LLM with a structured extraction prompt targeting specific fields: tenant name, landlord name, premises address, lease commencement date, lease expiration date, base rent, rent escalation schedule, security deposit, renewal options, and permitted use.
Request structured JSON output from the LLM and validate the output against a schema (e.g., date fields parse as dates, rent values are numeric).
For multi-chunk documents, merge extracted fields across chunks, resolving conflicts by preferring the chunk where the field is most likely to appear (e.g., rent from the rent section rather than a recital).
Flag low-confidence extractions for human review rather than silently passing through potentially incorrect values.
Known gotchas
LLMs hallucinate lease terms when the document is ambiguous or the relevant clause is buried in complex cross-references; always return source page or chunk reference so a human can verify.
Lease abstraction for legal or financial decisions must be validated by a qualified professional; automated extraction is a first pass, not a final answer.
Some lease provisions (e.g., co-tenancy clauses, exclusivity clauses) require reasoning across multiple non-contiguous sections; single-chunk extraction will miss them.
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claude mcp add --transport http waymark https://mcp.waymark.network/mcp