The Ground-Truth Loop
Learn the closed loop that turns raw fleet data into models you can trust: ingest, visualize, annotate, curate, train, deploy — then route every failure back to the data that caused it. One real dataset, followed end to end.
Deploy feeds back into Annotate — failures in the field become the next dataset.
- 01
What is Avala?
FoundationsA robot did something wrong yesterday — and the labels that would explain it don't exist yet. This is the data problem Avala was built to close.
3 min read - 02
The Ground-Truth Loop
FoundationsCollect, visualize, annotate, curate, train, deploy — then route every failure back to the data that caused it. The loop is the product.
3 min read - 03
Ingest & Visualize
Ingest & VisualizeUpload a raw multi-sensor recording and watch it become a synchronized 4D scene in the browser — point clouds, multi-camera playback, and splats on one timeline.
2 min read - 04
Annotate
AnnotateThe stage that makes the loop a loop. Models trained on your data auto-label the bulk; human experts verify the hard cases through consensus. The output is deterministic 4D ground truth.
3 min read - 05
Curate & manage
CurateVerified labels are only useful if you can find, slice, version, and trust them. Curation turns a pile of annotations into datasets you can reason about.
2 min read - 06
Train
TrainA verified, versioned dataset flows into your training pipeline. You train on ground truth, not raw sensor dumps — and the format meets your framework where it lives.
2 min read - 07
Deploy & close the loop
DeployThe model ships, the fleet runs, and the failures it produces become the next dataset. This is the arrow that turns a pipeline into a flywheel.
2 min read - 08
Make it yours
FoundationsThe loop is an API. Wire every stage into your own systems with the SDKs, the CLI, and an MCP server your coding agents can drive directly.
2 min read