Problem
A real-estate development SaaS founder needed an MVP to help developers and investors stress-test their project economics. Spreadsheet-based deal models break under uncertainty — a single IRR number does not tell you what happens when interest rates move 200 bps, refinance windows slip, or hard costs come in 15% over plan.
Approach
Built a Monte Carlo simulation engine over 20 priority project assumptions. Thousands of runs per scenario, outputting IRR distributions, equity multiples, refinance readiness, and downside cases. Output structured into three audience-specific deliverables: Capital Brief (LP investors), Developer Brief (GP team), and Audit Report (due diligence).
Stack
Python on AWS · R-API integration for statistical heavy lifting · FastAPI for the application layer
Outcome
Shipped in 6 weeks from spec to working MVP. Clean separation between simulation core and presentation layer let the founder swap UIs and audience formats without touching the math. The simulation engine became the product's structural moat — anyone can write a deal model; very few can defensibly say what happens to it under 1,000 stress-test paths.