The Physical AI Stack maps the architecture. The Hyperion Lifecycle drives the delivery. Every engagement is anchored in both — built from 17+ years of production experience.
Most AI engagements fail because they lack both a technology framework and a delivery discipline. The Physical AI Stack maps the six layers from physical sensor to autonomous operation. The Hyperion Lifecycle ensures every layer reaches production in 90 days. Both frameworks were built from 17+ years at Cisco, Renault-Nissan-Mitsubishi, ABB, and Auralink — organizations where theory and production are two different departments.
Two frameworks anchor every engagement — the Physical AI Stack (what we architect) and the Hyperion Lifecycle (how we deliver). Nothing else to learn.
HOW to architect Physical AI systems
A six-layer model for making physical infrastructure AI-native. From sensor data capture to autonomous fleet orchestration. Used to architect Auralink: 400+ microservices, ~20 AI agents, 78% autonomous incident resolution.
HOW to deliver AI projects to production
A five-stage methodology ensuring every AI initiative has a production path. Built at organizations where pilot has an expiration date. Every stage has clear graduation criteria: ship, pivot, or kill in 90 days. For European SMEs entering AI for the first time, the same five stages run on compressed, budget-conscious timelines.
Focused engagements that answer a specific question. AI strategy, EU AI Act compliance, production audit — with clear deliverables and a fixed price you know before we start.
Best for:
Hands-on projects with milestone-based payment. I work alongside your engineering team to build and ship production AI systems. Payment tied to deliverables, not hours.
Best for:
Ongoing AI leadership at the level you need — from 2 days/month strategic advisory to embedded full-time transformation leadership. Senior AI thinking without the full-time executive salary.
Best for:
Hands-on training built around your real challenges — not generic case studies. Your team practices with your data, your systems, your constraints. They leave able to do it themselves.
Best for:
An honest conversation about your situation. I diagnose the real problem — not just the stated one. If I can help, I'll tell you exactly how. If I can't, I'll tell you that too. No pitch, no pressure.
I dig into the context your team doesn't have time to explain on a call. Stakeholder interviews, technical review, constraint mapping. The goal: understand the problem you actually have, which is rarely the problem you think you have.
A 2-3 page document — not a 60-page deck. Fixed scope, specific deliverables, exact price, clear timeline. You'll know precisely what you're getting and what it costs before you commit a cent.
Direct access to me throughout. Weekly touchpoints, async communication in between. No junior delegation — the person who wrote the proposal is the person doing the work. You'll never have to 're-explain' your business to a new face.
I deliver things that work in production — not 200-page strategy documents that collect dust. Code, architecture, runbooks, trained models, compliance documentation. Artifacts your team can use the day I leave.
Capability transfer is not an add-on — it's woven into the work. Your team learns as we build. When the engagement ends, you're more capable than when it started. 30 days of email support after delivery, included at no extra cost.
One operator. A fleet of AI agents. A team's throughput with one accountable mind.
There's no bench of juniors — there's a fleet of AI agents. The agents do the leverage work; the senior does ALL the judgment. The person on your discovery call is the person doing the work.
The Operating Loop
Intent
You and I set the outcome
Fan-out
Agents generate options and work in parallel
Judge
I select and adversarially verify every output
Integrate
I assemble and reconcile
Ship
Production, not slideware