AI integrator for manufacturing
AI
cuts your scrap. predicts your failures. tunes your parameters. accelerates your digital transformation. secures your inventory. stabilizes your data. streamlines your operations.
We deploy AI directly into your operations — on your data, in your IT system, with full handover to your teams. No vendor lock-in. No black box.
What clients say
« They refused to lock us into a tech stack. Rare. »
— General management, Auvergne-based manufacturer, 30 employees
« 14 quantified, prioritized use cases — honest about what NOT to do. »
— Mid-cap CEO, premium equipment goods
« The code is ours, the docs are current, the team can run it. Rare. »
— Engineering office lead, plastics
« We swapped the model six months later without breaking production. It was designed for that. »
— CIO, multi-site manufacturer
« They refused to lock us into a tech stack. Rare. »
— General management, Auvergne-based manufacturer, 30 employees
« 14 quantified, prioritized use cases — honest about what NOT to do. »
— Mid-cap CEO, premium equipment goods
« The code is ours, the docs are current, the team can run it. Rare. »
— Engineering office lead, plastics
« We swapped the model six months later without breaking production. It was designed for that. »
— CIO, multi-site manufacturer
Three pillars, one discipline
What we do
01
Industrial operations
Production, quality, predictive maintenance, supply chain, R&D. We integrate AI where value is built. Defect detection, machine parameter optimization, failure prediction, scheduling, materials exploration. Each use case plugs into the existing IT system, not bolted on the side.
→02
Cross-functional teams
Finance, HR, IT, legal, procurement. Useful AI for the people who keep the business running outside the shop floor. Accounting automation, candidate sourcing, modern data architecture, contract review, supplier analysis. Same rigor, same agnosticism, same skills transfer.
→03
Technical agnosticism
We don't sell a model, a cloud, or a platform. We pick what fits your use case, justify it in writing, and keep it replaceable. If a better model lands in eighteen months, you don't pay for the project twice.
→Open knowledge
115 interconnected technical concepts
SPC, DOE, Machine Learning, LLM, industrial protocols — explore the links. The glossary is a commons: reusable, citable, contributable on GitHub.
Latest articles
What we write
Dense, quantified technical writing. Six Sigma, industrial Machine Learning, generative AI, Industry 4.0.
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The real cost of a RAG stack in production — SMB numbers
Honest breakdown of a production RAG stack: Anthropic/OpenAI tokens, Qdrant cloud vs self-host, compute infra, dev maintenance.
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When one agent isn't enough — orchestrator pattern for SMBs
Limits of a single LLM agent (context, bias, rate limit), concrete thresholds for switching to multi-agent, patterns.
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Can an agentic system replace an ERP?
3-year TCO of a classic ERP vs an agentic stack (LLM + RAG + LangGraph + DB) for a 15-50 employee SMB.
AI in industrial context
Five use cases in action
Five concrete situations. No slides, no promises: code, data, measurable results.
A four-step method
How we work
- 1
Field framing — 1 to 2 weeks
We come to your site. Operator interviews, flow mapping, IT system inventory, identification of the data actually available. No Miro workshops floating above the ground.
- 2
Quantified diagnostic — 2 to 4 weeks
Inventory of candidate use cases, data maturity assessment, value-feasibility matrix, prioritization. We also tell you what NOT to do. The roadmap is dated and budgeted.
- 3
End-to-end integration — 2 to 6 months
Short-cycle development, production releases, monitoring under supervision, continuous evaluation. Operators test during the build, not after. Stack assembled per use case.
- 4
Handover and clean exit — 2 to 4 weeks
Full documentation, team training, code and models handed over, autonomous maintenance plan. You can call us back or not — your choice, not ours.
Our agnostic stack
Fifteen interchangeable bricks
None imposed. Each brick can be replaced without breaking the whole.
See full stackFAQ
Questions we get asked
Why do you say "agnostic" when everyone says it?
Because we have a technical definition. Our architectures have independent layers: swapping the LLM takes a few days, swapping the cloud doesn't break production. Ask us for a technical demo — we'll show you.
How big is your team?
A small senior team. We don't artificially inflate headcount to bill juniors. If a project exceeds our capacity, we say so and co-deliver with vetted partners.
Do you work with SMBs or only mid-caps?
Our core target is industrial mid-caps between 50M and 500M euros revenue. We accept SMBs when the use case is clear and data maturity is sufficient. Sometimes we say no.
Will my data go to a US hyperscaler?
Only if you decide so, after a documented choice. Otherwise we deploy locally, with open-weight models, on your servers or with a sovereign French host.
How much does it cost?
The Data and AI Diagnostic is six thousand euros flat-fee, delivered in two to four weeks. Integration of priority use cases is then billed time-and-materials at a one thousand euros per day rate (senior consultant). We also offer in-company AI awareness sessions and role-based training (production, quality, maintenance, supply chain, procurement, finance/IT/HR): from one thousand five hundred euros per day depending on group size.
Discuss a concrete use case
A 45-minute call, free, no fluff slides.
You describe an operational pain point. We tell you whether we can help, how, and at what cost.
Book 45 minutes