Overmind

Overmind trains small models on your own data to make an AI step in your product more accurate, cheaper or faster, then serves them through an OpenAI-compatible API or lets you download the weights. It turns any company into an AI lab, where training a model takes one prompt in your coding agent.

The problem

When an AI agent runs every task on a large frontier model, each call costs more and takes longer than the task needs, and the steps it gets wrong are hard to fix with prompts alone. Training a smaller model for one task usually needs ML engineers, a clean dataset and a fair test against the current model, which product teams rarely have.

How it works

Connect Overmind to your coding agent and ask it to improve a step. Starts with one prompt.

Overmind turns your production data into a dataset and tests the step as it runs today. It then tries a better prompt or fine-tunes an open model, and compares the result with your current model. If the new version wins, you switch.

Key features

Turn your agents’ production data into training and test datasets Test prompts and models on real tasks against the model you use today

• Fine-tune an open model on your own data, with training and deployment handled for you
• Serve it through an OpenAI-compatible API, or download the weights and run it yourself
• Run the whole flow from Claude Code, Cursor or Codex
• Self-host the open-source platform with Docker Compose

What makes it different

The whole job, from raw data to a served model, runs on one open-source platform. You keep the weights of every model you train.

Use cases

• A step in your agent, such as labelling emails or calling APIs, is wrong too often and prompt changes have stopped helping
• Your large-model bill grows with every user and you want a smaller model that does the same job for less