I build AI that controls the physical world.
AI that controls the physical world — energy grids, vehicles, factories, infrastructure. Not chatbots. Not cloud dashboards. AI that controls physical infrastructure where latency and reliability are non-negotiable.
Most industrial AI pilots stall before 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 of software engineering, product leadership, and management at companies where software meets the physical world: Cisco (network & video platforms, 100M+ users), Renault-Nissan-Mitsubishi (connected vehicle platform powering 4M+ users across 39 countries), and ABB E-mobility (EV charging infrastructure). Then I built Auralink: 1.7M lines of code co-developed with AI, ~20 autonomous agents, 78% incident resolution — arXiv preprint. I created the Physical AI Stack because no framework existed for organisations moving from cloud AI to systems that control real-world assets.
The Three Villains of AI Consulting
Strategy Theatre: 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.
The Mission
Make physical infrastructure AI-native. Most industrial AI pilots stall before 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, major programs), ABB (EV charging infrastructure), and Auralink (400+ microservices, ~20 AI agents).
How I Engage
Three engagements, one path from a promising pilot to a dependable production system. Diagnose and Review: find the real problem--not the symptom the board noticed--and decide what deserves to reach production. Build and deploy: build for production, not demos, with security, evaluation, and safety designed in, then ship to real users with kill switches, graduation criteria, and monitoring from day one. Operate and scale: run under real regulation, with the audit trail to prove it, until your team owns the capability and the ROI is measurable. This method is built on 17+ years of shipping at organisations where failure meant lost revenue, not lost slides.
Principles
Ship or Stop
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.
Sovereign-First, Always
Sovereign by default — EU-hosted, European models (Mistral first), chosen for European clients. No resale kickbacks. No referral fees. No vendor 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.
Honest Over Comfortable
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.
Independence as the Goal
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.
Not the Right Fit If...
Hyperion is not for everyone. I'm the wrong choice if:
- You're building a cloud-only analytics dashboard — I specialize in AI that controls physical assets, not dashboards that summarise them
- You need a chatbot, not a production system controlling physical infrastructure — different problem, different expertise
- You need someone to validate decisions already made--I'll challenge the ones that don't hold up
- Your organisation isn't ready to change processes, only add technology
- You're optimising for lowest cost per hour, not highest impact per euro
I'm for organisations whose AI must work in the physical world — where failure means stopped production lines, not a wrong chat response. If that's you, let's talk about your specific challenge.
Mohammed Cherifi
17+ years of software engineering, product, and leadership at Cisco, Renault-Nissan-Mitsubishi, and ABB. Creator of the Physical AI Stack. Forbes Technology Council member.
Meet the Founder
Local Delivery in Greece
Stavros Stavrides
Partner in Greece
In Greece, I work with Stavros as an independent partner, supporting delivery and client relationships in the Greek market. Stavros extends local reach for Greek engagements specifically.
Stavros is not a Hyperion employee — Hyperion remains a single-operator practice. See how I deliver
Ready to Ship Your AI?
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.