Manual lease abstraction is a high-cost, high-risk bottleneck in commercial real estate operations, consuming hundreds of analyst hours per portfolio and introducing financial exposure through missed dates or misinterpreted clauses. A custom automation workflow directly attacks this by deploying a multi-stage document AI pipeline. Specialized agents first parse PDFs using OCR and layout understanding, then extract structured data for rent, escalations, options, and CAM reconciliations using fine-tuned LLMs with retrieval-augmented generation (RAG) against clause libraries. This architecture integrates with platforms like Yardi or MRI to normalize and validate data, flagging exceptions for human review and creating a system of record that supports portfolio valuation, accounting, and critical date alerting.




