Automate Commercial Lease Analysis with a Custom AI System

Many commercial property owners rely on property management software like Yardi or MRI. While these platforms are excellent for accounting and operations, their lease abstraction capabilities are often limited. They can store key dates that are manually entered, but they lack the ability to perform deep linguistic analysis on the PDF documents themselves. They treat the lease as an attachment, not a source of structured data.

Consider a portfolio manager who acquires a new property with 75 existing tenants. Each lease is a 50-page PDF with unique amendments. A junior analyst or paralegal must read every document, find the specific clauses for CAM charges, renewal options, and co-tenancy, and then manually type that data into a spreadsheet. This process takes over 150 hours of work, is prone to human error, and the resulting spreadsheet is immediately out of sync with the legal source of truth.

Off-the-shelf lease abstraction tools exist, but they impose their own rigid data schemas. If you need to track a specific non-standard clause unique to your retail properties, like a radius restriction, you cannot simply add it. These tools also charge per-document or per-user fees that become expensive as a portfolio grows, without offering the flexibility to integrate with your other internal systems.

The structural issue is that existing software treats lease abstraction as a data entry problem, not a data extraction problem. The intelligence still resides with the human reader. This manual dependency creates a permanent operational bottleneck, slows down due diligence for acquisitions, and introduces financial risk when a critical date or clause is missed.

The engagement would begin with a discovery phase to audit your current lease documents. Syntora would analyze 10-20 representative lease agreements from your portfolio to identify the 20-30 critical data points you need to track, from commencement dates to HVAC maintenance responsibilities. This audit produces a definitive data schema and a fixed-price proposal before any development work begins.

The core of the technical solution would be a Python-based processing pipeline. A FastAPI service would provide an endpoint to receive a new lease PDF. The service would use the Claude API, chosen for its large context window capable of handling 80+ page documents, to read the lease and extract the data based on the schema defined in discovery. The extracted data, returned as a clean JSON object, would then be written to a dedicated Supabase database. We've built similar document processing pipelines using Claude API for financial documents, and the same pattern applies directly to complex legal contracts like commercial leases.

The delivered system would be a simple, secure web application where your team can upload leases and view the structured, extracted data. It provides a searchable, queryable database of every clause across your entire portfolio. The system runs on your own cloud infrastructure (AWS Lambda and Vercel), and you receive the complete Python source code and a runbook for maintenance.