Automate CRE Comp Reports with a Custom AI Agent

CRE analysts often start with platforms like CoStar or Reonomy for raw data. These tools are powerful for lookups but lack workflow automation. An analyst still manually copy-pastes property details, sale history, and tenant information from these portals into an Excel or Word template. This repetitive work is the core bottleneck.

Consider an associate at a 15-person brokerage building a comp report for a Class B office building. They pull data from CoStar for on-market comps, the county assessor's website for tax history, and their firm's internal deal database, often an Airtable base or a shared Excel file. The analyst has 12 browser tabs open, is re-formatting date fields, and manually calculating price per square foot. One typo in a sale price can invalidate the entire analysis.

The structural problem is that these data sources are walled gardens designed for manual lookup, not programmatic access. CoStar's API has limitations, and county record portals are rarely API-first. Off-the-shelf automation tools can't bridge this gap because they lack the ability to handle inconsistent PDF layouts or the complex, multi-step logic needed to verify a property across three different sources. This forces analysts back into the browser, doing low-value data transcription.

The result is that senior brokers spend time proofreading reports for data entry errors instead of focusing on the narrative and strategic advice. The firm’s capacity to produce high-quality analysis is limited by the number of hours analysts can spend copying and pasting data.

Syntora would start with a discovery phase to map every data source you rely on. This includes subscription services like CoStar, public record portals, and your internal databases. We would identify which sources have APIs and which require custom web scrapers or document processing models. This audit produces a clear data flow diagram and a fixed scope for the build.

The core of the system would be a set of Python scripts running on AWS Lambda, triggered on a schedule or by an API call. For structured sources, the scripts would use httpx to query APIs. For unstructured PDFs from county websites, a Claude API pipeline would perform data extraction. All extracted data would be normalized into a standard Pydantic schema and stored in a Supabase Postgres database, creating a clean, unified data asset for your firm.

The delivered system would expose a simple API endpoint via FastAPI. An analyst could input a subject property address, and the system would orchestrate the data gathering in the background. The final output could be a structured JSON file or a pre-populated Word document, delivered to the analyst's email in under 2 minutes. The system integrates with your workflow, it does not replace it.