Legacy System Modernization: ColdFusion to Python/Flask

A live recipe and meal-planning platform — recipe import from PDFs, URLs, and photos, AI-assisted categorization, meal planning, shopping lists, and analytics — ran on an aging ColdFusion backend of roughly 5,600 lines across 11 files that the client's modern Linux hosting could not run natively. The only lift-and-shift path meant carrying a Java-based CFML engine forward: a second runtime and 1–2 GB of RAM the client specifically wanted to avoid. Database passwords, API keys, and Windows file paths were hardcoded in source. And the system had to keep running the entire time — there was no appetite for a risky big-bang cutover.

We rewrote the entire backend in Python/Flask at exact behavioral parity, kept the existing React 19/TypeScript frontend, and deployed to high-availability Linux infrastructure. The work was parity-first and test-driven: each ColdFusion component became a Flask blueprint one-to-one, the new API kept identical endpoints and JSON response shapes so the frontend needed only a configuration change, and a response-diffing harness compared new endpoints against the original to prove they matched. The rewrite happened entirely on a copy against a cloned database, so the original system stayed live as reference and fallback throughout. AI features moved from fragile shell-out subprocess calls to in-process Python behind a provider abstraction, a full adversarial security audit drove a hardening pass, and stateful data — relational records, user uploads, and secrets — was each migrated by its own strategy.

ColdFusion was eliminated, with all 80 backend methods across 8 components re-implemented at endpoint parity. The Java/JVM runtime and its 1–2 GB overhead were removed entirely. The codebase was roughly halved (about 5,600 lines of ColdFusion replaced by about 3,000 lines of Python) with roughly 145 automated tests that never existed before. Reading every method to match it surfaced four latent bugs the old system had carried for years — each corrected rather than faithfully reproduced. The engagement delivered zero data loss and zero disruption, and a one-push Git deployment pipeline replaced manual file management.