Enterprise capability, redesigned for smaller companies

The corporate AI function, compressed into one accountable lead. And sized for a smaller business.

Large enterprises separate strategy, domain expertise, AI/ML, emerging technology and delivery across departments. I bring those disciplines together inside your real workflow, start with one focused product, and involve specialist support only where the build needs it.

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How the model enters a business

Business context, technical depth and delivery stay in the same room.

01
Technical Breadth

The full technology landscape — not just the latest model.

From legacy ML and data engineering to applied generative AI, agents and on-prem deployment. We pick the right solution for the problem, not the trendy one.

  • Industrial ML & predictive maintenance
  • Generative AI application development
  • Multi-agent architectures
  • On-premise and cloud deployment
02
Embedded Delivery

Built side by side with the people who use it.

We learn the workflow from the inside — the real one, not the org-chart version. Solutions built close to the people who use them get adopted instead of abandoned.

  • Workflow observation from inside operations
  • Built with frontline teams
  • Integrates with existing tools
  • Measured adoption in the live workflow
03
Continuity Architecture

The solution outlives the engagement.

We transfer capability, governance and operating rhythm so your team can run, evolve and own the system independently. No permanent dependency, no vendor lock-in.

  • Role-based capability transfer
  • Internal champions & owner training
  • Governance & escalation structure
  • Independence roadmap
What large companies assemble across departments

Five enterprise disciplines. One accountable lead inside your business.

In a large enterprise, these capabilities often live in separate teams. I combine them in one forward-deployed role, then bring in focused support only where needed. The result is corporate-grade judgment without corporate-scale distance or overhead. Hover a strand to trace the model.

PhilippeForward-DeployedAI Lead
Accountable leadPhilippe · Forward-Deployed AI Lead
01
AI & DataML / Data / Eng
Domain FluencyValue Chains
Emerging TechAgents / LLMs
Business StrategyExecutive / Commercial
Entrepreneurial ExecutionLean / Owner Mindset

Technical Foundation. Grounding in data, AI and engineering deep enough to write the production code — not just a slide about it.

Domain Fluency. Fluency across real value chains — energy, finance, manufacturing, hospitality — so the build fits how the business runs.

Emerging Tech & LLMs. A polymathic read on the SOTA frontier: agentic workflows, RAG, sovereign LLMOps shipping this quarter.

Business Strategy. Executive and commercial experience to connect the product to margin, risk, capacity and ownership.

Entrepreneurial Execution. Ship the smallest useful thing, work within real constraints and improve it from evidence.

The five disciplines let one accountable partner see the business problem and the system behind it.

How an engagement runs

Step 01

Immersion

Working side-by-side, on the floor, in the field.

Step 02

Technical Spec

Translating ambiguous priorities into rigorous blueprints.

Step 03

Production

Iterative deployment, measured and owned by your team.

Why this model

You do not need to buy a transformation. You need the first useful result.

Large companies can hide expensive experiments inside large programmes. Owner-led businesses need a smaller path: solve one costly problem, prove the gain, and keep the wider value chain visible without committing to it all at once.

Commission one product against one specific pain point
Measure whether it changes the workflow that matters
Connect more products when the first result earns it
Embed Philippe with your team when you need strategic and technical ownership together
The common failure pattern

A technically impressive prototype has no workflow owner, no operating rhythm and no reason for the team to trust it.

Tool fragmentation

Separate tools create separate context. The owner sees another subscription; the team sees another place to update.

No workflow fit

A product that ignores approvals, exceptions and human judgment saves time in a demo and creates work in production.

Ways to work together

One product. A connected system. An embedded partner.

Single product

Quoted

One useful product, scoped to your operation

  • A useful outcome agreed before build
  • Integrated with your existing tools
  • Launch support and measurement included
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Connected next step

Connected suite

Quoted

Products connected across your value chain

  • Products share context and operating rules
  • Priorities follow proven business value
  • Ongoing support and optimisation
Scope a suite

Embedded Philippe

Quoted

Strategic and technical ownership alongside your team

  • Embedded on the floor, in the field
  • Scoped to a statement of work
  • Capability transferred, then owned by you
Talk to Philippe
The difference

Traditional consulting vs. Clinic of AI

Traditional AI consulting
Clinic of AI
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Bring one bottleneck. We will decide together whether it is worth solving.

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