Automate Your CRE Deal Pipeline in Salesforce with Custom AI

Many commercial real estate teams run their pipeline on Salesforce but struggle with the initial data entry. An associate receives a 50-page Offering Memorandum (OM) via email and must manually find the property address, NOI, cap rate, and square footage. They then log into Salesforce, create an Opportunity, and type these 10-15 key data points into custom fields. This process, repeated for dozens of deals a week, is a primary bottleneck and a common source of data entry errors.

Salesforce's native automation tools, like Flow or Process Builder, cannot solve this. They can trigger actions when a file is attached, but they cannot open a PDF to read and understand its unstructured content. You cannot build a Flow that says, "Find the Net Operating Income and put it in the NOI__c field." These tools lack the natural language processing capabilities required to interpret complex CRE documents.

Some teams try AppExchange apps for document parsing, but these are often built for generic use cases like invoices or legal contracts, not the specific language of CRE. They require rigid templates that break the moment an OM arrives in a new format from a different brokerage. They also struggle to map extracted data to the highly customized objects and fields that a mature CRE firm uses in Salesforce, leading to more manual work to clean up the output.

The structural problem is that off-the-shelf software is designed for structured data. A CRE deal pipeline is fed by semi-structured documents that are rich with context but lack a consistent format. Solving this requires a system built to understand the language of CRE deals, not just a tool that matches text to a predefined template. Your competitive edge comes from your unique deal evaluation process, and your software should support that process, not force it into a generic box.

The engagement would begin with a document audit. Syntora would analyze 15-20 of your recent Offering Memorandums, rent rolls, and other key deal documents to identify the critical data points your team tracks. We would then map these data points to their corresponding custom fields in your Salesforce instance. This initial step produces a detailed data dictionary that serves as the blueprint for the build.

The technical core would be a Python service running on AWS Lambda. When a team member attaches a document to a Salesforce Opportunity, a trigger fires the Lambda function. The function uses the Claude API with carefully engineered prompts to parse the document, instructing the model to act as a CRE analyst. It identifies and extracts key data, returning a structured JSON object. This approach is robust to variations in document layout, as the AI understands context, not just text position.

The delivered system integrates directly into your team's existing workflow. An associate attaches a PDF in Salesforce, and within 90 seconds, the relevant fields are populated. Each piece of extracted data includes a confidence score, and any field with a score below 95% is flagged for human review. You receive the complete source code in your GitHub repository, and the system runs entirely within your own AWS account.