Deploying ML models on-device is still a huge pain. Most teams default to cloud inference because:

Testing a model on different devices is manual and error-prone

Device constraints aren’t obvious from model exports

Rewriting deployment code for multiple platforms wastes days or weeks

I built Refactor AI, an infrastructure tool that:

Analyzes a trained ML model and flags ops that won’t run on the target device

Refactors the model where possible

Generates deployment-ready code for CoreML, ONNX Runtime, ONNX.js, and TFLite

This reduces deployment time from days to minutes, and lets teams run inference natively on-device, saving on cloud GPU costs.

Open to feedback, testing on real models, or ideas for improvement.