The loop
Each cycle starts from what your fleet does in the real world and ends with a release you can train on.1
Observe
Your robots run. Rollouts, interventions and failures flow into Avala as fleet recordings and events. See Failure ingestion.
2
Diagnose
Avala segments the rollouts, clusters the failures into a failure taxonomy and writes a gap report.
3
Evaluate
Avala scores your current model version on held-out real-world evaluation scenes. See Evaluation.
4
Acquire
Avala collects the data the gap report calls for. Each batch targets specific coverage cells and counts only accepted hours.
5
Train
You train your model on the release. Training stays on your side.
6
Validate
Avala re-evaluates the new model version on the same held-out scenes and reports the change per failure mode.
7
Release
Avala publishes a versioned, rights-cleared dataset release with sequence metadata and training exports.
8
Observe
The new model goes back to your fleet. Its rollouts start the next cycle.
Who does what
You decide what the model learns and when it ships. Avala supplies the evidence and the data for each decision.
How an engagement runs
Every engagement follows the same five steps. Each step produces an artifact you can inspect.- Failure. You send rollouts where the model failed or needed help. Avala reads them as fleet recordings and events.
- Gap report. Avala groups the failures by failure mode and names the coverage cells that are missing or thin.
- Brief. Avala turns the gap report into a collection brief. The brief lists target coverage cells, concentration limits, acceptance criteria and the evaluation cohort. You approve it.
- Batch. Avala collects and reviews the batch. Each delivery ships with a coverage report and per-sequence metadata.
- Re-evaluation. Avala scores your next model version on the held-out scenes. The capability report shows what changed and sets the next allocation.