Case Study
RidgeCap
Capital source matching and deal underwriting platform for commercial real estate brokers. AI-powered lender matching, geospatial market intelligence, portfolio stress testing, and pipeline management — purpose-built for CRE debt professionals.
Tech Stack
Problem
CRE brokers manage deal pipelines across dozens of lenders, property types, and markets using spreadsheets and memory. There's no intelligence layer that matches deals to capital sources, stress-tests portfolios against macro scenarios, or gives brokers a unified view of their book.
Approach
Built a full CRE debt OS: AI capital source matching using property type, LTV, geography, and deal structure; an underwriting engine with DSCR/LTV analysis; geospatial intelligence via FRED CRE market data (15 free series, 7 tables); and a pipeline management layer. Designed around the broker workflow.
Outcome
In active development — CRE market data infrastructure shipped, AI matching and underwriting engine complete. Target pricing Scout→$1,200→$2,500→$5,000/mo. Paused pending NexusWatch traction milestone.
Key Highlights
- AI capital source matching across the CRE lender universe
- Geospatial market intelligence and deal underwriting engine
- Portfolio stress testing with live FRED macro data integration