Calculate the ROI of AI-Powered CRE Research & Comp Reports

Most CRE brokerages rely on a patchwork of subscription services and Excel. Analysts log into CoStar for sales comps, Reis for demographic data, and internal systems for proprietary deal history. Each platform is a closed data silo. There is no API to connect them, forcing analysts into a cycle of manual exporting, copying, and pasting data into a master Excel workbook.

This manual process is the primary bottleneck. For example, a 15-person investment firm evaluating a retail property needs a market overview. An associate spends 90 minutes pulling and cleaning data from three different portals. They spend another 2 hours formatting this data, taking screenshots, and pasting values into a PowerPoint template. The entire 4-hour process is repeated for every new potential deal, consuming thousands of high-cost analyst hours annually and introducing a high risk of data entry errors.

Even with powerful platforms like Argus for valuation, the inputs are still manual. The tools do not talk to each other. An analyst might use a Python script for a small piece of the workflow, but it often breaks when a website layout changes, and it is not integrated into a central process. The core problem is the lack of a system that can bridge these data sources automatically, a task that off-the-shelf software is not designed to solve for a specific firm's unique workflow and data subscriptions.

The first step would be a complete audit of your report generation process. Syntora maps every data source you use, from subscription services with credentialed logins to public property appraiser websites and internal PDFs. We identify which sources have APIs and which require browser automation, defining the most reliable extraction method for each. You receive a technical plan detailing the approach before any build work begins.

The system's core would be a data pipeline built in Python. The Claude API would handle unstructured data, parsing PDF lease abstracts and offering memorandums to extract key terms like rent rolls and cap rates. All extracted data, structured and unstructured, would be stored in a Supabase Postgres database for querying. A FastAPI service would orchestrate the process, running data collection tasks in parallel to minimize wait times. This architecture provides a central, auditable system for all market data.

The final deliverable would be a simple internal web app deployed on Vercel. An analyst enters a property address, and the system queries all configured sources, compiles the data, and generates a structured output. This output can be a formatted PDF or a data file that feeds directly into your existing presentation templates. You receive the full source code and a runbook detailing how to maintain the system and add new data sources.