PHYSICAL AI PRODUCT MANAGEMENT
Fractional CPO and Interim Head of Product leadership for robotics, autonomous systems and industrial AI—connecting customer value, product economics, AI, hardware, software, safety and field operations.
17+ years across Cisco, Renault–Nissan–Mitsubishi and ABB · Based in Delft · Working across Europe
Buyer situations
Each one is a product decision. Engineering progress alone will not resolve any of them.
The system demonstrates well. What customers buy, at what price, and with what obligations is still open.
One site went well. Nobody can yet state what has to be true for the next ten to go the same way.
Each decision is defensible on its own. Together they no longer describe one product.
Product direction is settled in engineering reviews because nobody else owns it.
The date is fixed. The evidence behind go, redirect, sequence or stop is not yet assembled.
The mandate is real, the hire is months away, and the decisions cannot wait for it.
How to engage
Choose one bounded decision, one named product milestone or embedded ongoing product leadership. Each engagement stands alone.
You need to decide what happens next.
Make one consequential product decision with evidence.
Duration
Two to three weeks.
You need a senior owner for the next product milestone.
Lead one product to a named evidence gate.
Duration
Milestone-based, with scope and acceptance criteria agreed in writing.
You need senior product leadership now, but not yet a permanent executive.
Fractional CPO for Physical AI
Embedded product and portfolio leadership across strategy, discovery, productization, launch and scale.
Duration
Recurring part-time mandate; authority, cadence and capacity agreed in writing.
Every engagement has a written decision, evidence gate, authority boundary, stop condition and handover owner. None requires a follow-on engagement.
Runnable · inspectable · publicly checkable
Run the demonstration. Inspect the reference implementation. Read the preprint. Each item states what it proves, its maturity and its limitations.
Client work · paid mandate
One bounded mandate on Software-Defined Platform work, January–April 2024, as Tech Leader within Innovation & Technology, Energy Management. The named client disclosure is published in one canonical place.
Founder career · roles at former employers
A 64-person Product Management / Product Owner function, up to 40 direct reports, a 96-person product organisation and a €25M programme budget at Renault-Nissan-Mitsubishi. Career evidence, not Hyperion client outcomes.
Public technical preprint · publicly checkable
Read Mohammed Cherifi's public technical preprint and inspect its argument, architecture and cited basis directly. It has not been through journal peer review; its results are a controlled-environment evaluation.
Pre-production · owned reference implementation · internal R&D
Auralink is Hyperion's owned pre-production reference implementation. Inspect the architecture, control boundaries, operational decisions and evaluation approach behind the method.
Measured · simulated · internal R&D
Inspect the measured feasibility work behind the bench — physical sensing, teleoperation, evaluation and learned behaviour. Every figure is labelled measured, simulation or dry-run.
These are Hyperion-owned research and engineering artefacts. They are not customer deployments or commercial outcome evidence.
Berkeley SkyDeck Key Advisor (October 2022 – Present)
Appointments and bodies Mohammed belongs to — not client relationships.
The product ownership gap
Physical AI crosses customer workflows, hardware, software, models and field operations. Without one owner for the trade-offs across them, engineering can keep progressing while the product decision remains unresolved.
No shared acceptance bar. Model or cell performance is measured, but the complete product customers must buy, operate, support and trust is not.
Hidden work props up the pilot. Expert tuning, operator intervention, bespoke integration and unpriced support disappear from the business case.
Decisions arrive too late. Intended use, field boundaries, service, safety and unit economics surface after expensive architecture commitments.
Industrial & Physical AI Product Leadership
Industry fluency is not a list of standards or use cases. It is the ability to draw the physical system, expose where product ownership fractures, and connect operator reality to evidence and economics.
All industries
From a capable cell or model to a repeatable product for real lines and operators
Open the decision dossier
One product boundary across vehicle, platform, market and post-sale operation
Open the decision dossier
Whole-fleet reliability, field operations and economics—not model performance alone
Open the decision dossierAdjacent sector
Logistics, agrifood, maritime, smart infrastructure and defence remain visible as noindex sector dossiers. They show how the product-system method might apply without presenting researched relevance as client, career, clearance or assurance evidence.
Robotics and autonomy cross every primary market.
Explore intelligent machinesHyperion Physical AI Product System
Five gates govern when a product may advance. Six lenses test whether the evidence meets the threshold for the current decision: value, economics, intelligence, architecture, trust and operations.
Cyclical, not a waterfall: evidence, incidents or economics can send a product back to an earlier stage.
Customer and operator value
Business model and product economics
AI, data, evaluation and human-machine interaction
Hardware, software and system architecture
Safety, cybersecurity, governance, compliance and human oversight
Deployment, operations, product family, partners and ecosystem
Inside the system, DecisionOps keeps each decision current: it names the decision, the evidence threshold, the owner and the next gate. It is a supporting discipline within an engagement, not a separate offer or platform.
Applied AI systems
Hyperion connects product intent, system architecture, datasets and evaluation before choosing RAG, fine-tuning, a task-specific small language model — or no model at all.
Applied AI product architecture
Selected Insights
You work directly with Mohammed — a product leader across connected platforms, mobility, industrial systems and AI, now focused on Physical AI products.
Cisco → RNM Alliance → ABB → Hyperion
Fit
Hyperion owns the product leadership mandate and only the hands-on work explicitly named in scope. Your team retains enduring ownership of the product and broader delivery. Legal advice, conformity assessment, penetration testing and full functional-safety work remain with qualified specialists.
Hire when the mandate is stable, the role is permanent and you can wait for the right person. Use Hyperion when the immediate decision cannot wait, the permanent role is not yet clear, or you need evidence before adding long-term capacity. The engagement can also define the decision rights and hiring brief your team will retain.
In a 30-minute fit call, we will identify the decision, the missing evidence and whether Hyperion is the right mandate — or say plainly that it is not.
Mohammed leads every engagement personally. Scope and capacity are confirmed before work is accepted.
30 minutes · no obligation
