Automate Deal Qualification for Your CRE Brokerage

Small CRE brokerages often run their pipeline on a combination of CoStar, Reonomy, and a CRM like Buildout or a customized Salesforce instance. These CRMs are excellent systems of record for storing deal information. They fail, however, as systems of intelligence. Their built-in automation is typically limited to simple, rule-based triggers, like sending an email when a deal stage changes. They cannot perform the complex, multi-step logic needed for true qualification.

Consider a 10-broker firm specializing in Midwest industrial properties. A web lead comes in for a tenant seeking a 50,000 sq ft warehouse near Chicago. The current process requires a junior broker to spend an hour of non-billable time on manual research. They must first log into CoStar to check availabilities, then switch to Reonomy to verify ownership and sales history. Finally, they copy and paste over 20 distinct data points into the CRM before a senior broker can even assess if the lead is a good fit. This 60-minute delay is a competitive disadvantage.

This manual bottleneck exists because off-the-shelf CRMs are not designed to orchestrate external data and apply nuanced logic. Their architecture is built for structured data entry, not for calling multiple third-party APIs, parsing the responses, and running a proprietary scoring model. You cannot configure a standard Buildout workflow to 'Check CoStar for similar property comps, pull the last three sales from Reonomy, and rank the lead based on our firm's specific investment thesis.' This gap forces high-value brokers into low-value data entry work.

Our engagement would begin with a discovery phase to map your exact deal qualification criteria. We would audit your access to data sources like CoStar, Buildout, and Reonomy to understand API capabilities and limitations. This process results in a clear data flow diagram and technical specification that you approve before any code is written.

The core of the system would be a Python service, deployed on AWS Lambda for efficiency. When a new lead is created in your CRM, a webhook triggers this service. The service uses the Claude API to parse unstructured text from an email or web form, then calls the CoStar and Reonomy APIs to gather property, owner, and market data. This information feeds a custom scoring algorithm that reflects your firm’s unique criteria, producing a qualification score from 1 to 100.

The final system would write this score and all enriched data directly back into your CRM, typically within 30 seconds of the lead's arrival. Your brokers would see a 'Qualification Score' field and a detailed activity note without ever leaving their primary workspace. The entire system runs in your own cloud environment, and you receive the full Python source code, API documentation, and a maintenance runbook at handoff.