# Momentum

> Momentum is the platform that gets physical-AI teams to production faster. Deployed models collect real-world data from the field; Momentum turns it into the right training data, trains the next model, runs the right evaluations, and closes the loop by feeding deployment data back in — so every release reaches production faster and performs better.

Momentum is built for physical-AI teams (drone inspection, fleet monitoring, field robotics, grid inspection). The core idea is a closed loop with three pillars — **collect** real-world data from deployment, **train** the right model on it, and **evaluate** it against reality — that compounds every time it turns. It is sometimes described as "Databricks for Physical AI," but the outcome is what matters: robots to production faster.

## The loop

- [Capture what you don't know](https://www.momentumbots.io/#how-it-works): Served models run on live field data and flag every low-confidence or out-of-distribution (OOD) event at inference — like a Sentry error for perception. Events are normalized against a versioned asset schema and routed automatically.
- [Verify into the corpus](https://www.momentumbots.io/#how-it-works): A critic samples and routes uncertain events to the team's chosen verification mechanism (human adjudication, a critic model, or another mechanism). Each adjudicated event becomes a verified label. The verified-label corpus is the asset that compounds; the model is a regenerable rendering of it.
- [Improve and ship with proof](https://www.momentumbots.io/#how-it-works): Retrain the next model or evaluate the current one against a benchmark that grows with reality. Promote only when eval proves improvement. Every deployment carries full lineage from raw field capture to champion model.

## Evals and governance

- [Living evaluation](https://www.momentumbots.io/#evals): The benchmark is not frozen — it grows with every failure mode reality surfaces, so "is the new model actually better?" stays a meaningful question. Eval runs directly on the verified corpus with no infrastructure to stand up, and is regression-aware.
- [Governed promotion](https://www.momentumbots.io/#evals): Nothing reaches production until the eval gate passes. Every promoted model carries full lineage. Zero ungoverned rollouts; every promotion is auditable.

## Platform

- [Composable modules](https://www.momentumbots.io/#features): Swap labeling providers, training backends, or eval frameworks without rewriting the loop. The pipeline is Source → Label → Dataset → Build → Eval → Deploy.
- [API-first orchestration](https://www.momentumbots.io/#features): The whole platform is programmable — trigger runs from CI, integrate with your stack, and build custom operator surfaces.
- [Log in](https://app.momentumbots.io): Sign in to the operator app to start closing the loop.

## Contact

- Email: hello@momentumbots.io