The problem you're facing
Your application works, but competitors are adding AI features and your customers are asking for yours. Integrating AI without breaking the existing, without exploding inference costs and without violating GDPR: three classic pitfalls.
Our answer
We integrate AI as a component of your product: model choice per use case, grounding in your data, cost control and compliance : by the team that already knows your code.
What LaMeDuSe Technology takes care of
- Analysis of AI use cases in your product
- LLM integration (search, generation, assistance)
- Grounding in your data (RAG) where applicable
- Inference cost control (caching, routing)
- GDPR compliance and European hosting
- Quality evaluation before and after rollout
- Model maintenance and tracking
Our methodology
- 1
End-to-end scoping
Business need, technical constraints and hosting scoped together from the start.
- 2
Architecture
Argued, documented and reversible technical choices : no proprietary lock-in.
- 3
Delivery
Iterative development with demos, testing and continuous integration.
- 4
Operations
Deployment on European infrastructure, monitoring and maintenance by the same team.
Technologies
Security
Application security built into the cycle: code review, dependency analysis, secrets management and testing.
Hosting
Hosting on the group's European infrastructure : or deployment on your existing infrastructure.
Maintenance
A single contract covers the software and its operations: no more ping-pong between vendor and host.
Why LaMeDuSe Technology
We've integrated AI into our own platforms : search, assistance, generation: the cost, quality and confidentiality patterns we apply are our own.
Frequently asked questions
No: AI integrates via API into the existing, without a rewrite. The scope (which feature, which screen) is chosen to maximise customer value at constant effort.
Caching frequent answers, routing to smaller models where possible, limiting the context sent and cost tracking per feature. Costs are managed from design.
Yes: minimisation before sending to models, no-training configuration on your data, and European hosting. Open-source model self-hosting is possible for maximum requirements.
Through metrics defined before integration: feature adoption rate, satisfaction, time saved. An unmeasured AI feature is one you can neither improve nor justify.
Ready to turn your ideas into reality?
Contact our experts today to design and develop your custom technological solution.