Automate CRE Data Entry with a Custom AI Agent

Commercial real estate runs on documents, not structured data. Most brokerages use a CRM like Apto or a heavily customized Salesforce instance as their central database. While these tools are great for tracking relationships and deal stages, they rely entirely on manual data entry. A broker gets an 80-page PDF offering memorandum (OM) and someone has to spend 20 minutes finding and copy-pasting the address, square footage, NOI, and cap rate into dozens of separate fields.

Consider a mid-sized brokerage that receives 40 new OMs per week. A junior analyst or broker spends over 13 hours every week just on data entry. Generic data extraction tools fail because they don't understand the specific language of CRE. An off-the-shelf parser might find a dollar amount but won't know if it's the T12 NOI or a pro-forma Year 5 projection. This context is everything, and errors in this data lead to flawed underwriting and bad comps.

The structural problem is that CRMs are databases with rigid schemas, but deal flow arrives as unstructured text in PDFs and emails. Tools like Buildout help create marketing materials but don't solve the inbound data problem. You are left with a painful choice: burn expensive analyst hours on manual entry or work with an incomplete and inaccurate deal pipeline. The workflow cannot be fixed with off-the-shelf software because it requires a specific understanding of real estate finance terminology.

The engagement would begin with a document audit. Syntora would review 20-30 of your recent deal documents, including offering memorandums and deal-related emails, to create a definitive data dictionary. This defines every field to be extracted, from basic property stats to complex lease terms. This initial step ensures the final system captures exactly what your team needs to track.

The technical core would be a Python data processing pipeline deployed on AWS Lambda. When a document is received, it's sent to the Claude API, which is uniquely suited for parsing long, dense documents like OMs. A custom, fine-tuned prompt directs the model to identify and extract specific CRE data points. The system uses a FastAPI wrapper to handle requests and Pydantic for data validation, ensuring the output matches your CRM's schema before attempting an update.

The delivered system integrates directly with your CRM's API. A deal email forwarded to a specific address or a PDF uploaded to a folder would trigger the pipeline. Within 60 seconds, the relevant record in Apto or Salesforce is created or updated with dozens of fields populated automatically. You receive the complete source code, a runbook for maintenance, and a serverless architecture that costs under $50 per month to operate.