An AI engineer thatwires up your entire ML pipeline.
Point Resonetta at your GitHub repo. Get data loaders, training loops, inference servers, and clean project structure—generated automatically.
Applied ML engineers • Indie hackers • Production pipelines
Ship ML Products Faster Than Ever
Stop building the same boilerplate. Get a complete ML stack generated for your specific needs.
Production pipelines
Build production ML pipelines without repetitive setup and boilerplate code.
Standardize workflow
Consistent project structure and best practices across all your ML projects.
Solo developer power
Ship faster as a solo engineer or small team with automated infrastructure.
Experiment to production
Reduce friction between experimentation and deployment with automated scaffolding.
Complete ML Stack Generation
From data processing to deployment—all generated automatically from your GitHub repo.
Repo-level Reasoning
Analyzes your entire codebase to generate contextually appropriate ML infrastructure.
Data Pipeline Creation
Automatic data loaders, preprocessing, and validation pipelines for your datasets.
Training Script Generation
Complete training loops for PyTorch, JAX, and TensorFlow with monitoring and checkpointing.
Inference API Scaffolding
FastAPI or Flask servers ready for deployment with proper error handling and monitoring.
PR-based Output
All changes delivered as pull requests for easy review and integration.
How It Works
From GitHub repo to production ML pipeline in four simple steps.
Connect your GitHub repo
Point Resonetta at your repository for analysis.
Describe your goal
Explain your model or ML use case in plain language.
Generate pipelines
Receive complete ML infrastructure as pull requests.
Merge and run
Review, merge, and deploy your ML pipeline.
Ready to Let AI Build Your ML Stack?
Join applied ML engineers and indie hackers who are shipping production ML faster with automated infrastructure generation.
Get Early Access to Agentic Engineer