The problem you're facing
Your teams spend hours on tasks classic robots can't handle: sorting variable-format mail, answering natural-language requests, pre-filling documents from heterogeneous sources. Every exception goes to manual handling.
Our answer
We automate these grey zones with AI: document understanding, request interpretation, treatment preparation. Simple cases go automatic, ambiguous ones escalate to your teams with context already gathered.
What LaMeDuSe AI takes care of
- Process mapping and manual-handling rates
- AI automation of complex repetitive tasks
- Human validation circuit for ambiguous cases
- Integration with your tools (mail, ERP, DMS)
- Continuous measurement: automation rate, quality, time saved
- Progressive extension to neighbouring processes
- Gain reporting to decision makers
Our methodology
- 1
Use-case scoping
Identifying processes where AI brings measurable value: volumes, current error rates, value of time saved.
- 2
Proof of concept
Prototype on a real sample of your data, with quality metrics measured before any commitment.
- 3
Production rollout
Integration with your tools, human oversight at decision points and continuous quality measurement.
- 4
Continuous improvement
Drift monitoring, retraining when needed and extension to neighbouring use cases.
Technologies
Security & compliance
Your data stays under your control: European hosting, per-tenant segmentation, data minimisation before sending to models, and access logging. No training on your data.
Hosting
AI solutions are hosted on the LaMeDuSe group's European infrastructure (France, Germany, Switzerland) : or integrated into your existing cloud. Sensitive models can be self-hosted.
Maintenance
Maintenance contracts with quality tracking (model drift, LLM provider changes), fixes and support by the team that built the solution.
Why LaMeDuSe AI
We automate our own internal processes with the same techniques: proof by daily usage, on European infrastructure where data stays under control.
Frequently asked questions
RPA follows rules on structured screens; AI handles what isn't structured: free text, variable documents, natural-language requests. The two often combine : AI prepares, RPA executes.
Depending on process variability: from 40-60% (very heterogeneous documents) to 80-90% (standardised flows with edge cases). The exact figure comes out of the proof of concept : that's precisely its role.
They escalate to your teams with context already gathered: extracted documents, pre-filled data, request summary. Human work focuses on the decision, not the collection.
A first automated process: 2 to 4 months including proof of concept. Extension to neighbouring processes is faster afterwards, the platform being in place.