Expert-led work
Start with people who understand the equipment and the task.
Physical AI for
data centers.
Robotics data, inspection workflows and specialist expertise for the infrastructure behind AI.
Conceptual data-center imagery. We are developing our first data-center pilot.
CURRENT FOCUS
Workflows where a careful record matters.
- Rack acceptance
- Leak triage
- Test evidence
- Qualified operators
Behind every AI model is a physical world that has to work.
Power, cooling, racks and skilled people. We focus on the work that connects them.
01 — DATA CENTERS
Starting with liquid-cooled racks.
We are developing human-guided inspection and test-evidence workflows for rack acceptance and leak triage.
- Capture the workRecord the procedure, observations and instrument results.
- Resolve the exceptionsMake issues and retests traceable for the technical team.
- Build useful dataWith permission, turn reviewed examples into robotics training and evaluation data.
Initial pilot scope. Approved procedures and qualified operators remain in control.
02 — PILOT BRIEF
Start with a clearer conversation.
Choose a few details and create a planning draft to bring into an email. This is a conversation aid, not technical or safety advice.
YOUR PLANNING DRAFT
Select your context and create a planning draft.
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03 — OPERATIONS IN CONTEXT
Inside the infrastructure.
Every model depends on physical systems that need to be installed, inspected and maintained.
AI-generated concept film · Data-center operations
04 — OUR APPROACH
From human know-how to physical intelligence.
A careful path from the work itself to useful learning.
Purposeful data
Capture demonstrations, observations and exceptions with context.
Evaluation in the real world
Define the task, review the evidence and learn from what fails.
Conceptual imagery used throughout this section.
05 — APPLICATIONS
From rack readiness to the exceptions that need a second look.
Rack readiness
Bring the equipment, task and handoff into one review.
Cooling inspection
Keep observations connected to the approved procedure.
Exception review
Make questions, retests and next steps legible to the technical team.
Conceptual imagery used throughout this section.
06 — LEARNING WITH CARE
Observe. Review. Learn.
A qualitative progression for building useful understanding around real operations.
- 01
Observe
Capture the work and its context with the people doing it.
- 02
Review
Bring evidence and exceptions to the people responsible for the work.
- 03
Learn
Use reviewed examples to inform evaluation before autonomy.
Operator controlApproved proceduresTraceable evidencePermissioned dataException reviewEvaluation before autonomy
07 — FOR AI LABS & ROBOTICS TEAMS
The right expertise. Where it matters.
We also help AI labs, LLM teams and robotics companies find experts for data, evaluation and technical work.
Data-center operations
Work shaped by equipment, procedure and site context.
Robotics & embodied AI
People who can connect task design with physical-world evaluation.
Model training & evaluation
Support for reviewing examples, criteria and failure cases.
Domain expertise
Relevant practitioners for data, technical work and expert review.
EXTERNAL OPPORTUNITIES
Help build what comes next.
Find remote and freelance opportunities across AI, data and expert work.
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