The problem you're facing
AI projects rarely fail for lack of technology: they fail for lack of scoping. A poorly chosen use case, a proof of concept never measured, and a project that dies in the demo : after burning the budget.
Our answer
We start by identifying processes where AI brings measurable value, then prove it on your real data with metrics accepted by your business teams. Production only starts once value is demonstrated.
What LaMeDuSe AI takes care of
- Mapping of priority AI use cases
- Feasibility study: data, models, inference costs
- Measured proof of concept on a real sample
- Target architecture and production plan
- Integration with your existing tools
- Team training and documentation
- Post-deployment quality and gain tracking
Our methodology
- 1
Use-case scoping
Identifying processes where AI brings measurable value: volumes, current error rates, value of time saved.
- 2
Proof of concept
Prototype on a real sample of your data, with quality metrics measured before any commitment.
- 3
Production rollout
Integration with your tools, human oversight at decision points and continuous quality measurement.
- 4
Continuous improvement
Drift monitoring, retraining when needed and extension to neighbouring use cases.
Technologies
Security & compliance
Your data stays under your control: European hosting, per-tenant segmentation, data minimisation before sending to models, and access logging. No training on your data.
Hosting
AI solutions are hosted on the LaMeDuSe group's European infrastructure (France, Germany, Switzerland) : or integrated into your existing cloud. Sensitive models can be self-hosted.
Maintenance
Maintenance contracts with quality tracking (model drift, LLM provider changes), fixes and support by the team that built the solution.
Why LaMeDuSe AI
LaMeDuSe AI is part of a group that operates its own European infrastructure: we don't advise on AI from an ivory tower : we run AI systems in production, with the real constraints of cost, quality and compliance.
Frequently asked questions
With process analysis: we identify where volume, repetition or complexity creates a value pool. Only then comes the technology choice : arbitrated on cost, confidentiality and quality criteria, not fashion.
A POC focused on one use case typically starts around €15,000 to €30,000. It includes the prototype on your data, quality metrics and an argued recommendation : including, sometimes, not to proceed.
Yes, and that's our default setup: hosting in our French, German or Swiss datacenters, data minimisation before sending to models, and a full self-hosting option for open-source models with sensitive data.
That's a result, not a failure: the POC exists to find out before committing the full budget. You leave with the feasibility measurement, cause analysis and alternatives : sometimes classic automation is the better answer.