AI that controls the physical world — energy grids, vehicles, factories, infrastructure. Not chatbots. Not slides.
70% of AI pilots never reach production. For AI controlling physical infrastructure — factories, energy grids, vehicles — the failure rate is even worse. The problem is not the model. It's the system architecture. I close that gap with 17+ years at the intersection of AI and physical systems: Cisco (network & video infrastructure, 100M+ users), Renault-Nissan-Mitsubishi (connected vehicles, 4M+ users, 39 countries), ABB E-mobility (EV charging infrastructure), and Auralink (400+ microservices, ~20 AI agents, 78% autonomous resolution). I created the Physical AI Stack™ because no framework existed for organizations moving from cloud AI to systems that control real-world assets.
Strategy Theater: You pay $500K. You get a 300-slide deck. Nobody reads it. Nobody implements it. The consultants leave. You're exactly where you started, minus the budget. I've watched this destroy AI programs at Fortune 500 companies. Smart people. Big budgets. Zero production systems.
Consultant Dependency: The engagement never ends. You can't run your own AI because the consulting firm designed it that way. Every change request, every new model, every retrain cycle goes through them. You're not building capability. You're renting it. My goal is the opposite: I build myself out of a job. Every engagement includes capability transfer. You end stronger, not dependent.
Permanent Pilot: The demo works. Leadership is impressed. But it never graduates to production. No clear criteria. No architecture for scale. No plan for real users. Months pass. Budget burns. The pilot becomes a permanent state. At Hyperion, every initiative gets a 90-day checkpoint. It either ships, pivots, or gets killed. 'Permanent pilot' is not in my vocabulary.
Make physical infrastructure AI-native. 70% of AI pilots never reach production. For systems controlling energy grids, vehicles, and factories, failure is not an option. Every engagement is designed around the Physical AI Stack™: from SENSE (data capture) through ORCHESTRATE (autonomous operation). I've done this at Cisco (network & video infrastructure, 100M+ users), Renault-Nissan-Mitsubishi (connected vehicles, €250M program), ABB (EV charging infrastructure), and Auralink (400+ microservices, ~20 AI agents).
Diagnose the real problem--not the symptom the board noticed. Engineer for production, not demos. Pilot with clear graduation criteria and kill switches. Launch to real users with monitoring from day one. Optimize continuously against business metrics. Yield measurable impact and full capability transfer. This method is built on 17+ years of shipping at organizations where failure meant lost revenue, not lost slides.
Every initiative gets a 90-day checkpoint. Ship to production, pivot with purpose, or kill it. I will never let you burn budget on a pilot that has no path to production.
No partnerships. No kickbacks. No bias. When I recommend open source over proprietary, or build over buy, it's because the math says so for your situation. I have zero incentive to push any vendor.
I tell you what you need to hear. Sometimes that's 'don't do this.' Sometimes it's 'you're not ready.' Respectful pushback is part of what you're paying for. If you want a yes-person, I'm not it.
I build myself out of a job. Every engagement includes documentation, training, and knowledge transfer. When I leave, you run your AI yourself. That's success.
Hyperion is not for everyone. I'm the wrong choice if:
I'm for organizations who want an honest partner who ships production systems, not a contractor who presents slides. If that's you, let's talk about your specific challenge.
30 minutes. No pitch. Just an honest conversation about your challenge and whether I can help. If I'm not the right fit, I'll tell you and point you somewhere better.