The problem you're facing
Your support teams spend most of their time on the same repetitive questions while complex requests wait. Classic chatbots, meanwhile, loop on generic answers and frustrate customers.
Our answer
We build an assistant grounded in your real knowledge (documentation, histories, procedures): it answers when it knows, with sources, and escalates to your teams with a conversation summary when it doesn't.
What LaMeDuSe AI takes care of
- Support assistant connected to your knowledge base
- Grounded answers with source citation
- Intelligent escalation: conversation summary handed over
- Integration with your channels (site, email, messaging)
- Dashboard: volumes, resolution, satisfaction
- Continuous improvement from unresolved cases
- GDPR compliance: European hosting of conversations
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
We run our own technical support with tools of this family: we know the balance between useful automation and customer frustration : because we live it.
Frequently asked questions
Typically 40 to 70% of first-level requests depending on your knowledge-base quality. The exact figure comes from the pilot deployment, and the rate improves with continuous improvement.
Answers are grounded in your actual documentation (RAG), with source citation : the assistant doesn't answer when it can't find, it escalates. Every unresolved conversation feeds system improvement.
Only within the perimeter you validate: status checks, ticket creation, and possibly simple actions with validation. Sensitive actions stay human : the perimeter is built in steps.
Yes: European hosting, personal data minimisation, defined retention period and no model training on your conversations. A DPA is signed for every deployment.