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Services

Consulting

I help engineering teams ship better software, adopt AI effectively, and level up their technical practices.

AI Transformation

Strategic

End-to-end guidance for companies integrating AI into their workflows.

  • Audit & Opportunity Mapping

    Immersion in your workflows to identify where AI delivers real value — not where it sounds impressive.

  • Implementation

    Building production AI systems tailored to your specific domain and data, not off-the-shelf demos.

  • Team Enablement

    Training your engineers to own and evolve the AI systems after the engagement ends.

Trainings

On-site or Remote

Intensive training sessions to help your team mature on programming practices and tools.

  • Advanced Scientific Programming

    Writing robust, testable, and performant numerical code. From prototyping to production-grade scientific software.

  • Programming Workflow

    Software architecture, testing strategies, Git workflows, code review, and DevOps methodology.

  • Scientific Computing & Simulations

    Finite elements, numerical solvers, and computational methods — bridging the gap between theory and implementation.

Reference

DAES — One-week intensive training
Helped the team mature on programming tools and methods: software architecture, testing, and DevOps methodology.

On-site Consultant

Embedded

I join your team directly, on-site, for the duration of the mission.

  • On-demand CTO

    Technical leadership, architecture decisions, and roadmapping for teams that need senior guidance without a full-time hire.

  • Scientific Programming

    Numerical methods, simulations, and computational engineering — turning math into production code.

  • Backend & DevOps

    Infrastructure, CI/CD pipelines, cloud architecture, and backend development.

  • AI Engineering

    Building production systems with LLMs — RAG pipelines, agent infrastructure, prompt engineering. Not model training.

Case Study

Jimmy Energy

Head of Software & Comex member — 2022–2025

Jimmy Energy builds micro nuclear reactors to decarbonise industrial heat. When I joined, the engineering team relied on a legacy PLM and file-based workflows — a big design update took months.

What we built

  • Replaced the legacy PLM with a Python-based system (PyJimmy) where all systems are described in code
  • Synchronized all teams via GitHub, with quality checks through pull requests
  • Automated verification of regulatory requirements with GitHub Actions
  • Built a RAG system on 1 million technical PDFs for knowledge retrieval

Results

  • A big design update now takes days instead of the months expected in the industry
  • Safer outcomes — automated checks and Python-coded relationships prevent copy-pasting errors
  • Engineers spend time engineering instead of managing files
  • Clean, versioned data enables AI workflows across the organisation

"A big update in their design takes them days instead of the many months expected in their industry. And the result is much safer, because the checks are automated and the relationships coded in Python prevent copy-pasting errors."

Read the full LinkedIn post →

Python, Git, AWS, GitHub Actions

Get in touch

Each company has a specific situation. Let's talk about yours.

Let's talk