The problem you're facing
Used raw, generative AI invents facts, ignores your business context, and produces unverifiable content. Teams abandon it : or worse, publish without checking.
Our answer
We connect models to your real data (RAG), govern outputs with business guardrails, and keep traceability of every generated piece: sources used, human revisions, versions.
What LaMeDuSe AI takes care of
- Generative use cases: writing, summarisation, rephrasing
- Connection to your data (RAG) for grounded content
- Guardrails: quality filters, enforced formats, business rules
- Source citation for every piece of content
- Human validation circuit proportionate to stakes
- Quality and time-saving measurement
- Team training on proper usage
Our methodology
- 1
Existing process analysis
Measuring the current state: time spent, error rates, friction points. The baseline serves as a measurable reference.
- 2
Targeted prototyping
A prototype on your real data, evaluated with your business teams : not on a generic benchmark.
- 3
Supervised rollout
Progressive go-live, human oversight on ambiguous cases, continuous gain measurement.
- 4
Industrialisation
Scope extension once value is proven, with documentation and team training.
Technologies
Security & compliance
Data processed within the European perimeter, minimisation before sending to models, complete logging and no reuse for training.
Hosting
Hosted on the group's European infrastructure : datacenters in France, Germany and Switzerland.
Maintenance
Production quality tracking, model and pipeline tuning, support by the build team.
Why LaMeDuSe AI
Our own teams daily use generative tools we built : on our data, with the same guardrails we deploy for clients.
Frequently asked questions
Three levers: grounding answers in your real data (RAG) rather than model memory, enforcing source citation, and matching the validation circuit to the content's stakes. Generative AI without guardrails is a risk generator.
No: the commercial models we use are configured without training on your data, and our European hosting guarantees confidentiality. For the most sensitive needs, self-hosting open-source models eliminates any external sending.
One complements the other: generative writes and summarises, specialised (classification, extraction, prediction) processes with measurable metrics. We recommend per use case, often in combination.
For writing and document summarisation, typical gains range from 30 to 60% of time spent : measured on the POC, not promised. The gain depends on your data quality and validation circuit.