Physical AI in practice
Deploy Physical AI where failure has an economic consequence.
Physical AI Deployment & Production Recovery helps robot companies and industrial customers diagnose costly deployment gaps, execute bounded recovery programs, and stabilize accepted systems.
Best fit: a named owner, one robot workflow, accessible operating evidence, material economic impact, and a decision within 90 days.
The questions I help resolve
Use case
Which task is valuable enough to buy, bounded enough to test, and realistic for the hardware and autonomy available now?
Evidence
Which capabilities are demonstrated, under what conditions, and which assumptions still depend on operators, fixtures, connectivity, or future data?
Deployment
What must happen at the customer site for installation, safety review, acceptance, recovery, service, and repeatable operation?
Working together
Start with a paid deployment diagnostic when the problem or intervention remains uncertain. Teams with equivalent operating evidence can scope implementation directly. A diagnostic can lead to a deployment-recovery program and then a bounded reliability-support block; it also remains useful on its own.
Scope, timing, fees, and delivery arrangements are agreed privately after an initial discussion.
For founders and investors: one evidence method
For a founder, the diagnostic tests whether the selected workflow, system architecture, deployment burden, and customer value can support a production commitment. For an investor or board, the same method provides a decision-ready view of capability, dependencies, delivery economics, and the evidence behind the commercial plan.
Review areas
| Area | Questions tested |
|---|---|
| Capability evidence | What was demonstrated, how often, under which conditions, and with what human or environmental assistance? |
| System readiness | Which hardware, controls, perception, data, connectivity, and supplier dependencies can block the product? |
| Deployment reality | What installation, safety, integration, acceptance, support, and recovery work does each site require? |
| Product and roadmap | Does the first use case fit current capability, and does the roadmap retire the highest-risk assumptions early? |
| Commercial assumptions | Do pilot scope, customer value, gross-margin logic, service load, and scaling claims agree with the delivery model? |
Deliverable
A written memo separates verified evidence, management claims, inferences, open questions, and red flags. It includes a readiness assessment, prioritized requests for further evidence, and the technical/commercial conditions that should precede the next decision. Conclusions are limited by the access, materials, demonstrations, and expert scope agreed for the engagement.
I disclose relevant commercial relationships and scope specialists where deeper safety, legal, financial, or domain certification is required. If diligence concerns a platform or implementation I may later support, that relationship is made explicit before the engagement.
What I focus on
I take on humanoid, intelligent-robot, and industrial robotics work where a real customer workflow is approaching or failing deployment. General AI strategy, fundraising support, standalone product workshops, hardware resale, component sourcing, open-ended engineering staffing, and factory optimization unrelated to a robot deployment are outside this business.
Why this perspective
My work connects three layers that are often reviewed separately:
- Robotics field deployment across commissioning, troubleshooting, production support, safety, uptime, and acceptance.
- Founder experience building and exiting industrial IoT, edge networking, and applied-AI products.
- Humanoid and Physical AI product work spanning roadmaps, data collectors, on-device intelligence, privacy, and bounded demonstrations.
I also declined a $7M robotics project when available platforms could not responsibly meet its terrain, endurance, perception, and support requirements. The useful decision was to identify the gap before a contract turned it into a field failure. Read the decision.
Start with one costly deployment problem.
Describe the robot, customer workflow, operating impact, available evidence, and decision deadline.