Automate Lease Abstraction and Eliminate Data Entry Errors

Most CRE firms rely on manual data entry into property management systems like Yardi or MRI. These platforms are excellent systems of record, but they are not data ingestion tools. They expect structured data, but a lease is a 75-page unstructured document. This forces lease administration teams into a high-stakes, manual copy-paste workflow from PDFs into system fields, a process that is both slow and notoriously error-prone.

Consider a brokerage with three lease administrators processing 120 new leases a year. A new lease arrives as a scanned PDF. An administrator must read the entire document to find the Commencement Date, Rent Abatement period, CAM charges, renewal options, and insurance requirements. They key these 25+ fields into a spreadsheet, then a second admin double-checks their work. This two-step process takes 45 minutes and still, errors happen. Last quarter, a mis-typed renewal notification date caused a client to miss their option window, a costly and preventable mistake.

Some off-the-shelf abstraction tools exist, but they often present a fixed data model. They are trained to find a standard set of clauses, but if your business depends on tracking a non-standard term, like a co-tenancy clause, you cannot add it. These tools are also built for large enterprises, with pricing and feature sets that don't fit a firm processing 100 leases a year. You end up paying for a massive system just to use one feature that doesn't fully fit your needs.

The structural problem is that you cannot solve an unstructured data problem with a structured data tool. Generic OCR software can convert a scanned page to text, but it has no semantic understanding of a lease. It cannot distinguish the 'Lease Execution Date' from a random date mentioned in an exhibit. This gap between raw text and structured, business-ready data is where manual labor and critical errors accumulate.

The first step would be an audit of your existing lease documents. Syntora would analyze a sample of 10-20 leases, covering different property types and vintages, to identify the core data fields you need to extract. This discovery phase produces a detailed data schema and a fixed-scope proposal, so you know exactly what will be built and what data points the system will capture before any code is written.

The system would be a Python service using the Claude 3 Opus API for its large context window, which is critical for parsing 50+ page lease documents accurately. For scanned documents, AWS Textract would perform the initial OCR. The Python script would then pass the text to Claude with a specific prompt engineered to find and format fields like 'Base Rent,' 'Renewal Option Notice Date,' and 'Tenant Improvement Allowance.' All extracted data is structured as JSON and stored in a Supabase Postgres database for easy querying.

The deliverable is a simple web interface where your team can upload a lease PDF. Within 90 seconds, the interface displays the extracted data next to the source document for a quick human-in-the-loop validation. Once confirmed, the data is pushed to your primary system via its API or delivered as a CSV. You receive the full Python source code, a runbook for maintenance, and it runs on your own AWS account for a hosting cost under $50/month.