We build the backend for reliable robot deployments and compounding autonomy.

Research demos keep improving. But real deployments still fail on the long tail, because the learning loop breaks under real constraints.

When a demo looks good, the production questions are unglamorous: what happens when lighting changes, a camera shifts, the network drops, or an operator makes a mistake? Did you capture the right data, and did it arrive intact? When you have weeks of recordings, can you aggregate them into something you can browse, search, and slice again later?

Most teams end up stitching together a fragmented stack: loggers, object storage, ad-hoc scripts, a visualization tool, a training pipeline, and a spreadsheet of “what happened.” It works for a while. Then the fleet grows, the data grows, and nobody can keep the system coherent. People argue about what was collected, what was dropped, and which version of a dataset a result came from.

We think the missing layer is a deployment backend: a data + ops system designed for robotics semantics and edge reality. Its foundations are reliable collection, aggregation you can trust (sessions, manifests, and metadata), and fast retrieval.

Compounding isn’t automatic. If failures force human intervention, intervention drives cost. Cost limits deployment scale. Limited scale means less real data. Less real data keeps the long tail unsolved.

The way out is to make the loop operable: capture data reliably in the real world, aggregate it into coherent recordings and datasets, and ship improvements safely.

That’s what we’re building.