Use case:
A conversation with financial data models
Replace fragile Excel sheets with a live engine that values deals and scenarios in seconds.
The challenge
Corporate-finance and investment teams rely on sprawling Excel models that break when assumptions change. Analysts spend half their week copy-pasting, updating formulas and triple-checking links—leaving little time for actual insight.
Ramen AI's solution
We built a Self-Updating Financial-Model Engine: a Python back-end plus chat interface where analysts type plain-language scenarios (“What if revenue grows 12 % and interest rises 50 bps?”) and instantly get updated valuations & charts—no cell wrangling required.
How we did it
Fast data layer – Pulls actuals from the data warehouse and market APIs each night.
Model compiler – Translates your Excel logic into robust Python modules with unit tests.
Conversational interface – Analysts ask “Run a DCF at 10 % WACC” and get results plus visuals.
Versioning & audit – Every scenario is stored with inputs, outputs and a diff against baseline.
The results
≈ 1 040 analyst-hours freed per year (per analyst) by eliminating 50 % of manual Excel maintenance—worth ≈ €52 000 annual productivity gain each.