Identity layer
Give every dataset a durable identity
Version sensor data, labels, schemas, permissions, and exports under one stable record teams can find, reproduce, and cite.
Data infrastructure for robotics and autonomy
Fleet Learning Infrastructure for Physical AGI
Ingest MCAP, ROS, LiDAR, video, and telemetry. Turn fleet runs into 4D data, then curate and label the scenes that matter. Publish reproducible training releases and trace production failures back to the data and labels that shaped the model.
pip install avala- Avala Cloud, with BYOS keeping source data in your bucket and region.
- MCAP and RRD · LiDAR, video, images, audio, and text
- Import LeRobot datasets and ROS bag camera streams through our SDK, or connect custom formats with adapters.
Open and programmable
Build on the infrastructure your Physical AI models learn from.
Connect your agents to the hosted MCP server, build on the Python and TypeScript SDKs, read the metadata on every sequence, and import or export LeRobot datasets.
Backed by
Robots and AI are worth building only if they are good for people: the people who teach them, the people who own them, and the people they work beside.
Why Avala
Infrastructure for the data behind every Physical AI model
Avala gives Physical AI teams one operational system for turning 4D sensor data into governed model inputs, not a chain of disconnected labeling projects.
Operational control plane
Operate the full data-to-model loop
Coordinate ingest, 4D reconstruction, visualization, human and AI labeling, quality control, publishing, training, drift, and re-labeling in one system.
Managed operations
Scale execution without fragmenting the stack
Add qualified people, automated workflows, and rigorous consensus when programs need throughput. Your data and lineage stay in the same control plane.
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.
- Ingest
- Curate
- Label
- Quality control
- Publish
- Train
- Improve
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
Annotation Ops
Turn human expertise into production data
Route tasks, enforce labeling guidelines, and coordinate distributed teams with QC that is 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
Mission Control
See and control the work, not just the final delivery
Explore sensor data, design workflows, inspect quality, manage dataset versions, and trace model decisions in one workspace.
- Data Management
Organize, search & share
Avala manages traceable dataset deliveries today and is building stable references, immutable revisions, sealed manifests, and rights metadata for repeatable training.
- Annotation & Alignment
Label with full traceability
Turn synchronized camera, LiDAR, and telemetry into reviewed training data for Physical AI. Label in 2D, 3D, and across sequences without losing the context your models need.
- Training & Evaluation
Connect training and evaluation
Manage experiment lineage, fork datasets for rapid iteration, and gate releases on automated benchmarks or human review. Each dataset release is published to your training stack with its decisions recorded, and the results route back into the next collection.
- Integrations
Connect your existing infrastructure
Connect your data, training, and deployment stack
Built for Physical AI
One infrastructure across embodiments and environments
Bring the same governed operating model to robotics, autonomous systems, field data collection, and critical infrastructure.
The business case
More model progress from every hour of captured data
Replace handoffs across tools and vendors with compounding, versioned data assets that stay reproducible and ready for the next training cycle.
Preserve model context
Trace a model, benchmark, or production issue back to the exact dataset version, labels, and policies that shaped it.
Shorten iteration cycles
Move from data discovery to correction and re-training without rebuilding context across tools and vendors.
Govern deployment your way
Run in Avala Cloud with permissions, retention, auditability, and enterprise controls built into the workflow. Standard BYOS keeps source data in-region while Avala-managed infrastructure and API metadata remain US-based.
Operations when you need them
Software control with managed execution
Avala can supply the qualified people and automation behind demanding programs. They scale up or down while the dataset identity and control plane remain yours.
Open and programmable
Build with Avala
Python SDK, REST API, and CLI. Manage datasets, trigger annotations, and export results programmatically.
$ pip install avala
from avala import Client
client = Client()
dataset = client.datasets.get("dataset-uid")
People-First AI
Machines learn from people. We built the company around them.
15,000+ registered coworkers teach, check, and correct what models cannot resolve. They work flexibly, are paid for accepted work, and learn on the job. Their judgment is what makes the next release better than the last.
Trained judgment
Coworkers take the cases automation cannot resolve, and their corrections feed the next release.
Paid for accepted work
Flexible hours, pay above the norm for the work and the place, and payment for accepted work.
A path to more skilled work
Training is built into the work, so experienced people stay on the work they know and grow into harder tasks.
Research & perspectives
What Physical AI teams are learning now
Technical analysis, field lessons, and practical guidance for building better data-to-model systems.
Sep 4, 2026Insights
The largest market in history, and our bet on it
Every fleet should learn from its own experience. Why Avala is building the open fleet learning infrastructure for people-first AI and robotics, what we are optimizing for, and how today's work earns it.
Read more- Sep 4, 2026
Sovereign Physical AI: own the data-to-model loop, rent the rest
The shift from renting intelligence to owning it is now measurable in language models. The same four forces apply with more force in robotics, where there is no internet of robot actions and the data is the moat. What to own, what to rent, what is genuinely open to build on, and the infrastructure that makes ownership possible.
Insights - Aug 10, 2026
Four bottlenecks, three workarounds: robot learning in 2026
Robot learning's strongest 2026 papers attack memory, reasoning, dexterity, and world modeling. Three of the four work around data the field cannot collect yet.
Research
Build the data foundation your models can compound on
Turn every sensor dataset into durable model progress
See how Avala can connect your data, operations, and production models on one infrastructure.