Automate Your CRE Deal Pipeline Qualification

Most small commercial real estate brokerages rely on their CRM's basic rules or a junior broker's intuition to qualify leads. Tools like Apto and Buildout are excellent databases for managing deals but offer limited automation. Their rule engines can assign a lead from LoopNet to the industrial team, but they cannot learn that leads for industrial spaces under 10,000 square feet in a specific submarket have a 90% drop-off rate.

Consider this common scenario: A 12-broker firm receives 70 inbound leads a month. An administrator spends an hour each morning manually sorting them. A lead for a 50,000 sq ft warehouse gets immediate attention. An inquiry for a 1,500 sq ft retail space with a generic email address is put at the bottom of the pile. That manual process completely misses that the small retail inquiry is from a regional manager for a fast-growing franchise that plans to open five new locations in the next 18 months. The high-value lead goes cold.

Even with a more powerful CRM like Salesforce, the built-in scoring tools are not designed for CRE nuances. They score based on email opens and form fills, not on property characteristics, tenant profiles, or submarket trends. Getting true predictive scoring with Salesforce Einstein requires an expensive license and more data than most small brokerages possess. The structural problem is that off-the-shelf CRMs are built to store deal information, not to be analytical engines. Their architecture cannot easily incorporate external data sources in real time to make a lead more valuable before a human ever sees it.

The engagement would start with a data audit. Syntora connects to your CRM (Apto, Buildout, Salesforce, or others) and lead sources to map your deal pipeline from first contact to close. This process identifies the key data points that correlate with successful deals, assesses data quality, and determines what external data would be most valuable for enrichment. You receive a report detailing the findings and the proposed features for the model.

The technical approach would use a FastAPI service hosted on AWS Lambda, which is ideal for handling variable lead volume cost-effectively. When a new lead arrives, the service triggers a Python script. For unstructured inquiries, the Claude API parses the text to extract key details like property type and size. The system then queries external data sources to enrich the lead with ownership records or tenant information. The combined data is used by a model to generate a 0-100 score.

The delivered system integrates directly into your existing workflow. The score, along with the reasons for the score, are written to custom fields in your current CRM. Brokers see the prioritized leads inside the tool they already use every day, with no new software to learn. The entire process from a lead submitting a form to a score appearing in your CRM would take less than 60 seconds.