For AI labs

Human Data Infrastructure for Physical AI

Access real-world human demonstrations, operational environments and managed workforce across Brazil and Latin America.

What we provide

An operating layer between AI teams and the physical world

You define the data. We build the workforce, activate the environments and deliver reviewed, consented human demonstrations.

Egocentric human demonstrations

First-person capture of people performing real tasks in the environments where those tasks actually happen.

Real-world task execution

Full task sequences with natural variation, interruptions and recovery behaviour — not staged re-enactments.

Operational environments

Warehouses, fulfilment centres, production floors, service areas and other authorised working sites.

Skilled workforce

Contributors recruited by profession, trained on the capture protocol and qualified before production.

Multimodal collection readiness

Video-first today, with the operational structure to extend to audio, pose, depth and sensor streams.

Structured quality assurance

Every submission reviewed against explicit criteria, with issue classification and approved-duration accounting.

Consent and compliance

Informed contributor consent, company authorisation, privacy controls and a full audit trail.

Regional scalability

Brazil-first operations designed to extend across Latin America without rebuilding the model.

How it works

From specification to approved delivery

Every project runs through the same operational sequence, with measurable checkpoints at each stage.

  1. Step 01

    Client specification

  2. Step 02

    Task design

  3. Step 03

    Workforce matching

  4. Step 04

    Environment activation

  5. Step 05

    Capture

  6. Step 06

    Quality assurance

  7. Step 07

    Compliance review

  8. Step 08

    Approved delivery

Use cases

Training data for embodied and multimodal systems

Vision-language-action training

Paired visual context and task intent for action-grounded models.

Human-object interaction

Grasping, handling, tool use and object manipulation in cluttered real settings.

Task planning

Long-horizon sequences with sub-steps, ordering and real-world constraints.

Manipulation learning

Repeated fine-grained manipulation across operators, objects and sites.

Imitation learning

Consistent demonstrations of the same task by many qualified contributors.

Robotics evaluation

Reference human execution for benchmarking robot performance on the same task.

World-model data

Environment dynamics, layouts and human movement through operational spaces.

Multimodal AI

Structured episodes ready for enrichment with additional modalities.

Launch a Pilot in Brazil

Bounded scope, defined acceptance criteria and transparent reporting before any volume commitment.

Start a Pilot