Generate Faster, More Accurate CRE Comps with AI

Most CRE brokerages rely on CoStar as their primary data source. While powerful, it's a closed ecosystem. Exported data is often in PDF format, forcing analysts to manually copy-paste cap rates, square footage, and sale prices into Excel. This is slow and prone to transcription errors. Some teams try to use their CRM, like Apto or Buildout, to track comps, but these are deal management tools, not data aggregation platforms. They cannot pull live data from external sources.

Consider a mid-sized brokerage with a 15-person team. An analyst is tasked with creating a comp report for a 50,000 sq ft office building. They start by pulling 20 comparable properties from CoStar into a PDF. Then they open a separate tab for the county assessor's website to verify tax records for each property. Next, they log into the firm’s shared drive to find internal notes on similar deals from the past two years. The analyst spends three hours copy-pasting this data into a branded Excel template, reformatting charts, and writing boilerplate summary text. A last-minute change to the subject property means redoing half the work.

The structural problem is that CRE data is fragmented and unstructured. CoStar, public records, and internal databases do not talk to each other. Off-the-shelf tools are designed to be authoritative sources, not integrators. Their business model depends on keeping users inside their platform. They have no incentive to build robust APIs that would allow a brokerage to create a unified data warehouse. This forces analysts into the role of human API, manually connecting systems that were never designed to work together.

The result is that senior brokers spend valuable time double-checking analyst work, and analysts are stuck on low-value data entry instead of finding unique market insights. It also creates a key-person dependency; if the one analyst who knows the Excel template leaves, the process breaks. The firm's most valuable asset, its proprietary deal data, remains locked in siloed systems.

The engagement would begin with a data source audit. Syntora would map every source your team uses: subscription services like CoStar, public record portals, internal spreadsheets, and your CRM. We'd identify how to access each one, whether through a documented API, a structured data export, or by using browser automation for portals that lack APIs. This audit produces a clear data ingestion plan.

The core of the system would be a Python data pipeline running on AWS Lambda. It would periodically fetch data from all sources and store it in a unified Supabase database, creating a single source of truth for all property information. When a broker requests a comp report, a FastAPI endpoint would query this database, select the best comparables using a set of defined rules, and pass the data to the Claude API. Claude's large context window is ideal for generating the narrative summary and formatting the final report, which can be delivered as a PDF in under 90 seconds.

The final deliverable is a simple web interface where any team member can input a subject property address and generate a complete, branded comp report with one click. The system integrates into your existing workflow, not replaces it. You receive the full source code, a runbook for maintenance, and ownership of the centralized database. Your proprietary data stays yours.