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.
- Step 01
Client specification
- Step 02
Task design
- Step 03
Workforce matching
- Step 04
Environment activation
- Step 05
Capture
- Step 06
Quality assurance
- Step 07
Compliance review
- 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.