Automate Lease Abstraction and Critical Date Tracking

Small commercial property management companies often rely on spreadsheets or basic property software like AppFolio or Buildium for lease administration. These tools are effective databases for residential properties but cannot handle the complexity of commercial leases. They require a property manager or paralegal to manually read a 60-page PDF, find the renewal notice period, and type it into a static field. The software stores the data but offers no help in extracting it.

Consider a manager at a 10-person firm with a portfolio of 50 mixed-use properties. When a new tenant signs a complex lease, that manager spends an hour abstracting 25 key dates and clauses into an Excel sheet. They misinterpret the CAM reconciliation deadline. Six months later, the firm misses the deadline, creating a tenant dispute and costing thousands in unplanned expenses. This error is silent until it becomes a financial liability.

The larger, enterprise-grade platforms like Yardi or MRI offer automated abstraction modules, but their six-figure annual contracts are prohibitive for smaller firms. The structural problem is that most available tools are built to store structured data, not to create it from unstructured documents. They solve the 'where to put the data' problem, not the much harder 'how to get the data accurately' problem. They lack an AI component capable of reading and understanding legal language.

The process would start by auditing your existing lease portfolio. Syntora would analyze 15-20 representative lease documents to identify the most common clauses, formats, and variations. This audit defines the exact data points to be extracted (e.g., Commencement Date, Rent Abatement Period, Notice for Renewal) and establishes the 'source of truth' for the AI model. You would receive a proposed data schema for approval before any code is written.

The core of the system would be a Python service using the Claude API for its large context window, which is critical for parsing 80-page lease documents. The service would run on AWS Lambda for cost-effective, event-driven processing. When a new lease PDF is uploaded, a Lambda function triggers, sending the document to the Claude API with a prompt engineered to find and format the required clauses. Results are stored in a Supabase Postgres database, and processing for a typical lease would complete in under 2 minutes.

The delivered system would expose a simple web interface for uploading leases and viewing or editing extracted data. A daily sync process would check for upcoming critical dates (e.g., renewal notice periods within 90 days) and automatically create events in your team's Google or Outlook calendar. You receive the full Python source code and direct control over the AWS and Supabase accounts, with typical monthly hosting costs under $50.