Buy labeling, data collection and datasets for Physical AI

No generic seat ladder. Choose the smallest commercial path that can prove the next dataset release, then expand the same system.

• First release

  Scope one modality mix, one dataset release, and the acceptance criteria that prove it is useful.

• Production loop

  Run ingest, curation, human + AI labeling, QC, versioning, and export as one repeatable system.

• Enterprise control plane

  Standardize how sensor data is governed, released, and connected to training across the organization.

Every proposal names the assumptions behind the scope, so teams can see what is included and what changes as they scale.

• Data

  Modalities, scene volume, capture cadence, retention, and the dataset release you need to publish.

• Workflow

  The stages Avala runs, from ingest and curation through annotation, QC, export, and re-label.

• Infrastructure

  Compute, storage, integrations, deployment model, security requirements, and support level.

• Operations

  Whether your team operates Mission Control or Avala supplies collection, labeling, and QC capacity.

Avala's category is fleet learning infrastructure for Physical AI. Labeling is one stage of the loop Avala runs, not the category. A team that builds robots, vehicles, drones or field systems can still scope labeling and data operations, a collection program, or dataset access as its own engagement. The customer trains and owns the model. Avala runs ingest, curation, human and AI labeling, quality control, versioned dataset releases, and the training handoff. Mission Control is the operating surface where a team inspects source files, designs a workflow, watches quality, manages permissions, and traces a release back to the recording it came from. Lineage stays on one record from the sensor file to the export.

Three engagements are open on this site. Labeling and data operations cover camera, LiDAR and telemetry in two dimensions, three dimensions and 4D, including MCAP and ROS bag recordings, with quality control and versioned exports. Collection is planned from a public catalog of 175 workflows in 26 task families and runs against a written brief: egocentric capture, robot teleoperation, field operations or a custom sensor rig. Dataset access starts from the public samples, which open in Mission Control with no account. The household sample is 236 sequences and 56.4 hours. The bimanual teleoperation sample is 4,248 sequences. The platform, Mission Control, is how ingest, curation, labeling, quality control, dataset versions and evaluation run, through the web app, the Python SDK, the TypeScript SDK, the REST API and MCP.

Avala does not publish list prices. Every engagement is a scoped quote. The quote is built from the four inputs on the pricing page: data, workflow, infrastructure and operations. Data covers modalities, scene volume, capture cadence, retention and the dataset release you need. Workflow covers the stages Avala runs. Infrastructure covers compute, storage, deployment and security requirements. Operations covers whether your team runs Mission Control or Avala supplies collection, labeling and quality control capacity. The three starting points are First release, Production loop and Enterprise control plane. Send the request through the contact route, or email hello@avala.ai with modalities, volume, label types, a deadline and where the data lives.

The compliance statements on this page are the ones Avala already publishes: SOC 2 Type I and GDPR. Standard bring-your-own-storage keeps source data in the customer's own bucket, including S3 or GCS, and in the customer's region. Avala-managed infrastructure and API metadata remain in the United States. Coworker capacity is the published network of 15,000+ registered coworkers across five continents, working flexibly and paid for accepted work. Avala does not publish revenue or a valuation on this page. Licensable stock beyond the public samples is not listed here.

The first engagement is a scoped First release. It names one model objective, one modality mix, one bounded dataset release and the acceptance criteria that show the release is useful. A Production loop is the next path when ingest, curation, labeling, quality control, versioning and export need to run as one repeatable system, with model feedback and re-label on the same record. Enterprise control plane is the path when several teams need one way to govern how sensor data is released and connected to training. The customer trains the model in the customer's pipeline. Avala keeps the operating record.

A labeling request should name the sensor modalities, the approximate volume, the label types, the deadline and where the data lives. A collection request should name the task, the setting, the rig if you already have one, and the acceptance bar the delivery has to meet. A dataset request should name the task family and the modalities you want to inspect first, then open the public samples before asking for anything custom. The catalog is public, so a brief can point at a workflow, a family and a setting instead of describing the task from scratch. The contact route has five intents. Use labeling, dataset access or collection program for the work on these pages.

Can a team buy labeling without buying the whole loop?

Yes. Labeling is one stage of the loop Avala runs, not the category. A team can scope labeling and data operations, a collection program, or dataset access as its own engagement. The category remains fleet learning infrastructure for Physical AI.

Does Avala publish list prices?

No. Every engagement is a scoped quote built from data, workflow, infrastructure and operations. The starting points are First release, Production loop and Enterprise control plane.

Where should a first request go?

Open the public samples with no account, or send the contact route with the intent that matches the work: labeling, dataset access, or a collection program. Include modalities, volume, label types, a deadline and where the data lives.