Automate Critical Lease Date Tracking for Your CRE Firm

Small CRE firms often start with Yardi Breeze or AppFolio. These platforms offer basic property management but their lease abstraction features are limited. They rely on manual data entry for critical dates, turning them into expensive digital filing cabinets, not active management systems. They cannot automatically read a 60-page PDF and identify a co-tenancy clause or a CAM audit deadline. This forces teams back into Excel.

Consider a 10-person firm managing 80 properties. A lease administrator spends 2-3 hours per new lease manually reading the PDF, finding renewal notice dates, rent escalation schedules, and insurance requirements, then typing them into a spreadsheet. When an LOI is revised, the process repeats. With 5-10 new leases or amendments a month, this single task consumes 25 hours of skilled labor, and a single typo could mean missing a renewal option worth tens of thousands.

The core issue is that off-the-shelf software is built for structured data entry, but lease information is trapped in unstructured text. A tool like Realogic offers powerful abstraction services, but it's a manual service, not an in-house system you control. You pay per lease, and the data still ends up in their system or exported to a spreadsheet. There is no feedback loop to improve your own internal process.

This manual dependency creates a constant operational risk. A missed renewal notice can trigger a holdover penalty. A forgotten rent escalation clause results in lost revenue. The entire system relies on one person's diligence with a spreadsheet, a fragile process that does not scale and exposes the firm to significant financial and legal liability.

The engagement would begin with an audit of 10-20 sample lease agreements from your portfolio. Syntora would identify the key clauses and critical dates you need to track, from common items like expiration dates to specific clauses like exclusive use restrictions. This audit defines the data schema and provides the ground truth for tailoring the AI model. You receive a scope document detailing the exact data points the system will extract.

The technical approach would use a Python pipeline on AWS Lambda. Scanned PDFs are first processed with Amazon Textract for OCR. The extracted text is then passed to the Claude API with a detailed prompt engineered to identify and structure the required data points, like 'Tenant Renewal Notice Period' and 'Base Rent Escalation Percentage'. A FastAPI service would expose an endpoint to trigger this process, and results would be stored in a Supabase PostgreSQL database, taking under 90 seconds per document.

The delivered system is a simple web interface where your team can upload new lease documents. After processing, the extracted data appears in a centralized dashboard and calendar view. This dashboard can be configured to send email or Slack alerts 180, 90, and 30 days before a critical date. You receive the full source code, a runbook for maintenance, and an API for future integration with your primary property management software.