PitchFit - AI Financial Modeling Platform

The Problem

PitchFit needed an AI backend that could analyze financial data, generate projections, and build investor-ready models automatically. Founders were doing this in spreadsheets: inconsistent outputs, hours of manual work per client, and no way to scale. The product vision required taking raw financial inputs (revenue, burn rate, headcount plan, market size) and producing investor-grade projection models without a human in the loop. The challenge was encoding the financial logic accurately enough that the outputs would hold up under investor scrutiny.

Our Approach

Built the AI inference layer in FastAPI for the compute-heavy forecasting models and Flask for lighter API endpoints. React frontend connects through an Express.js API gateway. The core ML pipeline takes raw financial inputs, runs them through sector-specific revenue forecasting models and valuation algorithms, and outputs structured JSON that the frontend renders into reports and pitch deck slides. Edge cases required significant handling: negative cash flow periods, pre-revenue projections, and multi-currency inputs all needed separate normalization paths before hitting the ML layer.

Pipeline Breakdown