Decision engineering for Physical AIIndustrial & Physical AI product leadership for founders and product leaders building robotics, autonomous systems and AI-enabled industrial products.

Product leadership for AI that has to work in the physical world.

Mohammed Cherifi leads product and architecture decisions for robotics, autonomy and industrial-AI teams—across solution, system, software, data and AI, from strategy and discovery through productization, launch and scale.

Discuss a product decision Explore the Product System

17+ years across connected products, mobility and industrial systems · Ambassadeur IA, Osez l’IA · Forbes Technology Council member

The product ownership gap

A working system is not yet a repeatable product.

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.

The missing layer is product ownership: one accountable leader connecting customer evidence, system behaviour, field reality, safety and economics before the next commitment.

From proof to operated product

Production is a different system—not a bigger pilot.

Demo conditionOne configured device

Operational obligationA versioned fleet across sites

Field conditions: Demo condition: Chosen conditions; Operational obligation: People, materials and environments keep changing
Failure response: Demo condition: An engineer intervenes; Operational obligation: Degraded modes, rollback and recovery are designed in
Decision authority: Demo condition: Model output can act; Operational obligation: Deterministic controls enforce the safe boundary

Hyperion Physical AI Product System

One product system. Every decision connected.

Five gates govern when a product may advance. Six lenses test whether the evidence is complete: value, economics, intelligence, architecture, trust and operations.

Cyclical, not a waterfall: evidence, incidents or economics can send a product back to an earlier stage.

StrategyIs this the right product opportunity, for the right customer, with a credible path to value?
DiscoveryIs there enough customer, field, technical and economic evidence to justify product investment?
ProductizationCan this become a complete, dependable, supportable and commercially viable product?
LaunchCan the product be sold, installed, accepted, supported and operated responsibly?
ScaleCan customer value, deployment and economics be repeated across sites, machines, customers and product variants?

The evidence field at every gate

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

Applied AI systems

A model is one component. The product is the whole system.

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.

The objective is not more AI. It is the least complex system that can meet the product's acceptance bar under real operating constraints.

Applied AI product architecture

Edge intelligence
Governed knowledge systems
Data, evaluation & learning systems
Mission-critical architecture

Task-specific SLMs and compact multimodal intelligence
Edge intelligence
Bounded intelligence for products where latency, privacy, power, connectivity or offline operation shape what is viable.
Industrial RAG, retrieval and authoritative operational knowledge
Governed knowledge systems
Knowledge products that preserve source authority, permissions, provenance and abstention instead of hiding uncertainty behind fluent answers.
The evidence infrastructure behind every intelligent behaviour
Data, evaluation & learning systems
Datasets, simulations, evaluation suites and feedback loops treated as governed product assets rather than one-off inputs to a model build.
Product, solution, system and software architecture
Mission-critical architecture
An accountable architecture across hardware, software, AI, data, controls, safety, security, people, edge and cloud — designed around failure as well as nominal operation.

Capabilities Run the model-strategy diagnostic

Industrial & Physical AI Product Leadership

Three primary markets. Five research contexts. One product system.

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

Manufacturing & Industrial Automation
From a capable cell or model to a repeatable product for real lines and operators
Open the decision dossier
Automotive & Connected Mobility
One product boundary across vehicle, platform, market and post-sale operation
Open the decision dossier
Energy, EV Charging & Infrastructure
Whole-fleet reliability, field operations and economics—not model performance alone
Open the decision dossier

Research context

Research contexts

Logistics, agrifood, maritime, smart infrastructure and defence remain visible as noindex research dossiers. They show how the product-system method might apply without presenting researched relevance as client, career, clearance or assurance evidence.

Intelligent machines are a system context—not an industry.

Robotics and autonomy cross every primary market.

Explore intelligent machines

How to engage

Senior product ownership at the level the decision requires.

Choose an embedded fractional mandate, one bounded decision or one named product milestone. Each stands alone.

You need senior product leadership now, but not yet a permanent executive.
Product Operating Partner
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.
Explore the Product Operating Partner
You need to decide what happens next.
Product Decision Review
Make one consequential product decision with evidence.
Duration
Two to three weeks.
Explore the Product Decision Review
You need a senior owner for the next product milestone.
Product Leadership Program
Lead one product to a named evidence gate.
Duration
Milestone-based, with scope and acceptance criteria agreed in writing.
Explore the Product Leadership Program

Start with the smallest useful mandate.

Every engagement has a written decision, evidence gate, authority boundary, stop condition and handover owner. None requires a follow-on engagement.

Check fit and availability

Runnable · inspectable · publicly checkable

Evidence, labelled for what it is.

Run the demonstration. Inspect the reference implementation. Read the paper. Each item states what it proves, its maturity and its limitations.

Measured · simulated · internal R&D
Reachy Mini + SO-101 imitation-learning bench
Inspect the measured feasibility work behind the bench — physical sensing, teleoperation, evaluation and learned behaviour. Every figure is labelled measured, simulation or dry-run.
- Inspect the measurements
Pre-production · owned reference implementation · internal R&D
Auralink
Auralink is Hyperion's owned pre-production reference implementation. Inspect the architecture, control boundaries, operational decisions and evaluation approach behind the method.
- Inspect Auralink
Public paper · publicly checkable
arXiv:2603.08736
Read Mohammed Cherifi's public paper and inspect its argument, architecture and cited basis directly.
- Read the paper

These are Hyperion-owned research and engineering artefacts. They are not customer deployments or commercial outcome evidence.

Open the evidence register

Ambassadeurs IA programme — AI Ambassador appointment ceremony — Osez l’IA — Ambassadeurs IA programme.

Mohammed Cherifi

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

NDS/Cisco video platforms deployed at 100M+ scale · product leadership on Renault-Nissan-Mitsubishi Alliance connected-services programs (Renault OpenR Link, NissanConnect)
17+ years in industry

See Mohammed's record

Founder career — not Hyperion clients

These marks represent founder career, advisory work, memberships and affiliations. They do not represent Hyperion clients.

Advisory work, not Hyperion clients

Memberships and affiliations.

Appointments and bodies Mohammed belongs to — not client relationships.

Fit

A fit when the product, decision and authority are real.

A good fit when you have

A robotics, autonomy, industrial-vision or intelligent-equipment product
A consequential decision or milestone within 3–12 months
An executive sponsor and named internal counterpart
Access to customers, operators, maintainers and realistic field evidence
A willingness to make decision rights explicit
Acceptance criteria that can be made explicit
A team that will retain ownership

Not a fit for

Generic AI ideation
Pure research with no product or field intent
One-off integration without a repeatable-product ambition
Undirected staff augmentation
Implementation without product authority
Legal advice, certification, conformity assessment or a guaranteed outcome

What I build, and what remains outside the mandate

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.

Why not hire?

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.

Bring the decision you cannot leave unowned.

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.

Discuss your product Product Decision Review

30 minutes · no obligation