Automate Your CRE Deal Pipeline with Custom AI

Regional CRE brokerages run on a CRM. Many use industry-specific platforms like Apto or a heavily customized Salesforce instance. These tools are excellent systems of record for tracking properties, contacts, and deal stages. Their primary failure is that they require constant, manual data entry from brokers or their assistants. The CRM cannot read an email or a PDF to understand that a deal's status has changed.

Consider a 15-broker firm. A senior broker receives an executed Letter of Intent (LOI) as a PDF attachment in an email. They forward it to their analyst. The analyst must open the PDF, identify the property, find the corresponding deal in Apto, and then manually update 5 to 7 fields: deal stage to 'LOI Signed', the new offer price, the due diligence period, the closing date, and any contingencies. This 10-minute, error-prone task happens dozens of times a week across the firm, consuming hundreds of hours per year.

The core problem is architectural. CRMs are databases with user interfaces, designed for structured input. They have no native capability to parse unstructured text from an email body or a legal document. Off-the-shelf email parsing tools exist, but they are generic. They might extract a date or a dollar amount, but they lack the context to know that '$2.1M' is the 'Offer Price' and '45 days' is the 'Due Diligence Period' in the context of an LOI. This gap forces brokerages into a cycle of manual work that creates data lag and reporting inaccuracies.

Syntora would begin with a discovery process to map your firm's exact deal flow. We would review a sample of 50-100 emails and documents representing key stages, from initial offer to closing. This audit identifies the specific data points that need to be extracted for each document type and how they map to your CRM fields. You receive a detailed data map and a fixed-scope proposal before any development starts.

The technical system would be a Python service running on AWS Lambda, triggered by incoming emails. When a broker forwards a deal-related email to a dedicated address, the service uses the Claude API to read the email body and any attachments. Claude's large context window is well-suited for parsing multi-page PDFs like purchase and sale agreements (PSAs). The extracted data, such as 'Earnest Money Deposit' or 'Contingency Removal Date', is structured into a JSON object. The service then authenticates with your CRM's API (e.g., Apto's REST API) and updates the correct fields on the correct deal record. The entire process, from email receipt to CRM update, would take less than 30 seconds.

The delivered system functions as an intelligent intake layer for your existing CRM. There are no new dashboards for your brokers to learn. The system would include a simple exception queue in a Supabase web app for any documents the AI cannot parse with over 98% confidence. You receive the complete Python source code, a runbook for maintenance, and full control over the cloud infrastructure, which typically costs less than $50 per month to operate.