Build Your Automated CRE Deal Pipeline

Many brokerages try to connect their CRM to email using standard connectors. A generic Salesforce or HubSpot connector cannot parse the unstructured data in an inbound property inquiry. The connector sees an email but cannot extract the property address, square footage, or lease terms needed to create an accurate deal record.

For example, a broker receives an email about a property at "123 Main St." The standard automation might create a new contact, but it cannot link it to the existing property record without an exact address match. The broker still has to manually search for the property, create the deal, copy-paste the email contents, and set a follow-up task. This manual work, repeated 5-10 times a day, introduces errors and delays follow-up by hours.

Off-the-shelf tools are built for standard sales objects like "contacts" and "companies." They lack the context of commercial real estate concepts like "properties," "leases," and "comps." They cannot understand that "123 Main" and "123 Main Street" are the same place, a gap that forces agents back into manual data entry for every new inquiry.

Syntora would start by mapping your entire deal lifecycle, from initial inquiry to closed-won. We would connect to your CRM (Apto, Buildout, Salesforce) via its API to analyze your existing deal flow. Using Python with the Pandas library, we would identify the key data points that signal a high-value opportunity, based on your historical data. This initial data audit would typically take 5 business days and requires appropriate access to your CRM's deal history and any relevant data exports.

The core of such a system would involve a set of serverless functions, often implemented with AWS Lambda in Python. When a new email inquiry arrives, a function would trigger. It would utilize the Claude API to perform entity extraction, identifying property addresses, client names, and key terms from the unstructured text. Syntora has extensive experience building document processing pipelines using the Claude API for complex financial documents, and the same robust pattern applies to commercial real estate documents. A subsequent function would use the extracted address to query public records and internal databases, potentially stored in a Supabase Postgres instance, enriching the deal with relevant tax data and ownership history. This enrichment cascade is designed for rapid execution.

Syntora would develop a small FastAPI application to house the business logic. This service would determine which agent to assign based on factors like property type and agent specialization, a ruleset we would define collaboratively with you. The service would then make an API call to your CRM, creating a new deal record populated with all the extracted and enriched data. The architecture is designed to minimize latency from email receipt to CRM record creation.

The FastAPI service would be deployed on platforms like Vercel, and the Lambda functions would be managed via the AWS CDK. We would configure structured logging using `structlog` to send alerts, for example to Slack via a webhook, if any critical API call fails. The total monthly hosting cost for this serverless architecture is typically estimated to be under $40 per month, excluding any specific third-party API usage fees like for Claude API or premium data sources.