The problem you're facing
An LLM queried without your data answers confidently and is often wrong: it knows neither your products, nor your procedures, nor your contracts. And a stale knowledge base produces stale answers with confidence.
Our answer
We build the complete pipeline: ingestion and updating of your documents, vector indexing adapted to your content, relevant retrieval and grounded generation with source citation. Data freshness is built into the architecture, not bolted on.
What LaMeDuSe AI takes care of
- Ingestion of your sources: documents, databases, wikis, tickets
- Automatic updating: the RAG follows your data
- Indexing adapted to your content (vector, hybrid)
- Grounded generation with source passage citation
- Relevance evaluation on your real cases
- Access control: everyone sees only what they're allowed to
- Answer quality monitoring
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
Our own internal tools rely on RAG architectures we operate: technical documentation, support histories, procedures. Retrieval reliability is measurable : we measure ours.
Frequently asked questions
RAG gives the model access to your data at answer time, updating as soon as your documents change. Fine-tuning modifies the model itself: costly, slow to update, unsuited to evolving data. For business knowledge, RAG almost always wins.
Access control is applied at retrieval time: each query only retrieves documents the user is allowed to see. A RAG assistant must respect the same confidentiality as your document tools.
Through continuous ingestion: connection to your sources (DMS, wikis, databases) with change synchronisation. A frozen RAG lies with confidence : freshness is part of the architecture.
From a few hundred to several million documents depending on architecture: vector indexing, hybrid search and adapted chunking. Real volumes and response times are validated in the POC on your content.