Automate Commercial Real Estate Lead Qualification

Most small commercial real estate brokerages manage their pipeline in a specialized CRM like Apto, Buildout, or Rethink CRM. These platforms are powerful for tracking deals once they are qualified, but their intake process is entirely manual. The deal flow does not start in a structured form; it arrives as an unstructured email with a PDF offering memorandum attached.

For example, a broker gets an email with the subject 'Off-Market Industrial - 75k SF'. The broker must open the attached PDF, find the address, building specs, and asking price, then switch to the CRM. They create a new property record, a new contact for the listing broker, and a new deal, manually copy-pasting over a dozen fields. This 10-minute workflow, repeated 15 times a day, consumes hours that could be spent on calls with qualified prospects. Data entry errors are frequent, leading to inaccurate reporting.

Brokers often attempt to solve this with Outlook rules or basic parsing tools. These tools fail because they cannot handle unstructured text. An email rule can flag a keyword like 'multifamily', but it cannot extract the unit count, year built, or in-place NOI. The core architectural problem is that CRMs are databases designed for structured data, while CRE deal flow is fundamentally unstructured. Off-the-shelf tools cannot bridge that gap effectively.

The consequence is that valuable deals are missed because junior brokers are too buried in data entry to respond quickly. Senior brokers either waste time on triage or operate with an incomplete view of the pipeline because the CRM is always out of date. The first firm to respond to a good deal often has the advantage, and manual processes create a permanent speed disadvantage.

Syntora would start with a two-day audit of your last 100 inbound deal emails and attachments. The goal is to map out every critical data point you need to make a qualification decision. We would work with your team to define a clear, machine-readable 'ideal deal profile' that will form the basis of the scoring algorithm. You receive a schema document detailing the exact fields to be extracted.

We would build the technical pipeline using Python and deploy it on AWS Lambda for efficient, event-driven processing. An inbound email to a dedicated address (e.g., deals@yourfirm.com) would trigger the function. The Claude API would parse the email body and any PDF attachments, extracting the data points defined in the audit. We've built similar document processing pipelines for complex financial reports, and the same pattern applies directly to offering memorandums. Pydantic schemas enforce a consistent JSON output from the AI, preventing bad data from ever reaching your CRM.

The delivered system is a 'digital intake desk' that runs 24/7. When a broker forwards an email, a fully populated record appears in your existing CRM in under 60 seconds, tagged with a qualification score from 1-100. The system is invisible to your team; they just see high-quality, pre-vetted deals in the tool they already use. You receive the full source code, a runbook, and a Supabase dashboard to monitor processing history and any exceptions.