Dataset Ops
Make your data work at scale
Manage massive sensor streams with built-in versioning, lineage tracking, and search—engineered for production workloads, not prototypes.
Connected to the same dataset record
For robotics and embodied AI
Fleet Learning Infrastructure for Physical AGI
Turn raw robot, vehicle, and edge-system recordings into versioned training signal—then connect model results back to the scenes that need the next round of work.
One operating record connects sensor data, annotation decisions, quality evidence, dataset versions, and model feedback.
MCAP and ROS data
LiDAR and point clouds
Multi-camera video
Telemetry and trajectories
One continuous loop
From sensor capture to model improvement
Each stage writes back to the same governed record, so teams can move faster without losing context, quality, or provenance.
- 1Ingest
- 2Curate
- 3Label
- 4Quality control
- 5Publish
- 6Train
- 7Improve
Annotation Ops
Turn human expertise into production data
Route tasks, enforce labeling guidelines, and coordinate distributed teams with QA that's measurable—not subjective.
Connected to the same dataset record
Model Management
Understand model behavior
Monitor for drift, track real-world performance, and trace any release back to the data, labels, and decisions that produced it.
Connected to the same dataset record
One control plane
The system of record between sensors and models.
Mission Control keeps data, decisions, and model feedback in the same operating context. Teams can inspect the evidence behind a release instead of reconstructing it after delivery.
Dataset identity
Know exactly what trained the model
Give every dataset and version a stable identity, with lineage back to its source scenes and quality decisions.
Control plane
Operate the workflow
Run annotation, review, consensus, and release policies from one control plane instead of stitching together project tools.
Managed execution
Scale the hard parts
Add specialist human operations where the workflow needs judgment or throughput, without turning the system of record into a services handoff.
Built for Physical AI programs
One layer across the model lifecycle.
The same governed data loop supports perception, behavior, and embodied-model teams without forcing every program into the same schema or review policy.
Multimodal perception
Curate and label synchronized camera, LiDAR, and telemetry data for detection, tracking, segmentation, and scene understanding.
Manipulation and behavior
Structure demonstrations, action segments, trajectories, and temporal context for manipulation and vision-language-action training.
Edge cases and safety
Find rare scenarios, preserve review evidence, and release approved dataset versions for safety-critical evaluation and retraining.
Fleet feedback and drift
Turn model failures and production drift into targeted scene searches, new work queues, and traceable dataset releases.
Training-ready outputs
Publish signal your training stack can trust.
Publish model-ready data without severing its connection to the raw scenes, transformations, annotation history, and acceptance criteria that produced it.
- 2D, 3D, and temporal labels
- Versioned dataset manifests
- Quality and consensus records
- Portable export pipelines
Deployment and governance
Your data boundary stays yours.
Keep sensitive data private while teams work from one governed record. Avala runs on Avala Cloud with BYOS keeping source data in your bucket and region, with access controls, auditability, and data isolation built into the workflow.
Explore securityBuild the loop
Make every dataset improve the next model.
Bring us your sensor stack, model workflow, and hardest data bottleneck. We will map the shortest path to a repeatable model-improvement loop.