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Software-led
Your engineers and operators use Mission Control, APIs, and SDKs to run the loop in-house.
Fleet Learning Infrastructure for Physical AI
One platform under every stage of your fleet learning loop.
Avala is independent infrastructure under your model. Observe your fleet, diagnose its failures, evaluate each model version, acquire the data that closes the gap, validate it and release versioned datasets. You train the model and decide when it ships.
Built for robotics, autonomous vehicles, and embodied AI.
MCAP + ROS
LiDAR + point clouds
Multi-camera video
Telemetry + trajectories
The fleet learning loop
Seven stages, each with the product to run it.
Avala runs five stages. You train the model with your own code and compute, and you decide when it ships. One record links every stage, so any result traces back to the data behind it.
Observe
Run by AvalaSee what your fleet did. Recordings arrive with their device, event and alert context, and you can open any sequence in its scene.
- Fleet devices, recordings and events
- Rules and alerts
- MCAP viewer
- 3D and Gaussian-splat rendering
Diagnose
Run by AvalaFind where the model fails and why. Avala groups failures, checks them for overlap and maps them to the coverage cells your data is missing.
- Search across recordings and sequences
- Overlap checks and video fingerprinting
- Failure clustering into a failure taxonomy
- Gap reports mapped to coverage cells
Evaluate
Run by AvalaScore the current model on held-out real-world scenes before anything changes, so every later result has a baseline.
- Held-out scenes, never released for training
- Versioned contamination checks
- Vision-language pre-score with human audit
- Reports with confidence intervals
Acquire
Run by AvalaTurn the gap into a capture campaign. An approved brief names the tasks, places, objects and sequence counts, and the coverage standard sets the bar.
- Capture campaigns from an approved brief
- Managed workforce for capture and labeling
- Coverage cells from the catalog of 175 workflows
- A coverage report with every delivery
Train
Run by youYou train on the new dataset release with your own code and compute. Avala keeps the lineage from sequence to release.
- Python and TypeScript SDKs
- PyTorch data loading
- LeRobot export
Validate
Run by AvalaCheck every sequence against the brief before it reaches a release. Technical validity and behavioral outcome are graded separately.
- Acceptance criteria for each campaign
- Quality targets
- Consensus scoring
- Outcome and recovery labels
Release
Run by youYou decide when the model ships. The data behind it is a versioned release with its slices, exports and full lineage.
- Versioned datasets and slices
- Exports, including LeRobot format
- Lineage from capture to release
- SDKs and the hosted MCP server
After Release, the loop returns to Observe with the fleet running the new model.
Permissions and audit trails apply at every stage.Security
Execution that fits
One control plane. Any operating model.
Use Avala with your own team, add managed capacity where judgment or throughput is constrained, or combine both without changing the system of record.
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Managed execution
Add trained capacity for collection, labeling, and review while workflow, policy, and quality remain visible in Avala.
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Hybrid by design
Keep domain decisions with your experts and scale repeatable work through Avala without another handoff.
Build the loop
Make every dataset improve the next model.
Bring us your sensor stack, training workflow, and hardest data bottleneck. We’ll map the shortest path to a repeatable model-improvement loop.