The problem you're facing
Hacked-together LLM projects collapse in production: unreliable answers, exploding inference costs, confidentiality discovered too late, and a model provider deprecating your version without warning.
Our answer
We architect your LLM usage with production rigour: per-use-case model selection, grounding in your data (RAG), continuous evaluation on business test sets, cost optimisation and a model-version migration strategy.
What LaMeDuSe AI takes care of
- Model selection: generalist, specialised or open source
- RAG architecture for answers grounded in your data
- Continuous evaluation: test sets, quality metrics
- Inference cost optimisation (caching, routing, small models)
- Confidentiality strategy: hosted APIs or self-hosting
- Version management and model migrations
- Usage monitoring and logging
Our methodology
- 1
Right model for the job
The right model for each use: generalist, specialised or self-hosted open source, arbitrated on cost and confidentiality.
- 2
Quality architecture
RAG, agents and guardrails designed to limit hallucinations and guarantee answer traceability.
- 3
Continuous evaluation
Business test sets, quality metrics and drift monitoring : AI without measurement degrades.
- 4
European operations
Models run on our European infrastructure, with monitoring and inference cost control.
Technologies
Security & compliance
Self-hosted model option for sensitive data, environment segmentation, managed secrets and call logging.
Hosting
Self-hosting on our European cloud (France, Germany, Switzerland) or hosted APIs with data minimisation.
Maintenance
Model watch (new versions, deprecations), quality re-evaluation, inference cost optimisation.
Why LaMeDuSe AI
We run LLMs on our European infrastructure for our own products: the trade-offs we recommend, cost, quality, confidentiality, are the ones we live daily.
Frequently asked questions
It depends on three criteria: required quality, data confidentiality and budget. A generalist model fits most uses; sensitive data points toward open-source self-hosting. We arbitrate in a POC with your real cases.
Rarely as a first resort: RAG (grounding in your documents) solves most needs at lower cost and stays easy to update. Fine-tuning is justified for very specific formats or vocabulary, once RAG has been exhausted.
Four levers: caching frequent answers, routing to smaller models for simple tasks, limiting the context sent, and cost tracking per use case. Costs are managed : not suffered.
Yes, two ways: self-hosting open-source models on our European infrastructure, or using European APIs with data minimisation. The choice depends on your confidentiality requirements and needed quality.