Overmind turns your production traces into specialised models you own, automatically trained, benchmarked, and served.
Your unfair advantage
Understand every atom of your agent
Every prompt, tool, and decision path mapped into one graph, with every production run scored as it lands. Debug and evolve your agent with total context.
Data nobody else has
Every production run becomes an asset you keep. Overmind curates your best traces into audited datasets with full provenance, a training corpus only you can build.
Every improvement, proven
Evals generated from your agent's own design score every change against your baseline, so you ship improvements as reviewed PRs and know exactly what got better.
A model only you can train
Train on your own data and prove the win before traffic moves: a 0.5B specialist beat GPT-4o mini 50% vs 29% on the same product eval. The weights stay yours.
The agent context graph
Your biggest edge is knowing your agent completely. Overmind maps your whole agent into one graph: every prompt, tool, decision path, and dependency, discovered from the codebase and kept honest by traces from a single SDK setup. Evals, datasets, optimisation, and training all draw from that one shared picture.
Dataset curation from production traces
Your agent's best production runs become training and eval data. Every example is validated and checked for fit against the agent it came from, with full provenance back to the trace it started as.
Data pre-processing in the Workshop
Clean data multiplies everything you train on it. The Workshop audits every dataset twice: deterministic checks catch duplicates, broken structure, and PII in seconds, then a coding agent reads the corpus for the problems rules miss. Every proposed fix is verified before you see it, and applying one is a click.
Evals generated from context
Get eval coverage without writing evals. The graph knows what each agent is designed to do and how it can fail, so scoring criteria are generated for exactly that. Every run reports per-metric scores you can track across models, prompts, and releases.
Ship the winning change as a PR
The Optimiser rewrites prompts, tool definitions, and agent logic as real git diffs, each scored against your baseline on the same eval set. The winning change opens as a reviewable PR, and when tweaks stop paying off it tells you it is time to train.
Automate model training
Training your own model takes a couple of clicks, because the hard decisions are already made by your data. Overmind recommends the model tier and hyperparameters, estimates cost and duration before you commit, and charts progress live. Deploy on Overmind's inference or your own stack.
- Quality vs the model you pay for today
- No comparable eval
- Scored on the same eval set, before traffic moves
- Serve, per 1M output tokens
- $10.00
- from $2.50
- Serve 5B output tokens / year
- $50K
- from $12.5K
- Train on Overmind
- Closed weights
- from $0.50
- Weights after training
- API access only
- Yours to download
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