The problem you're facing
"Autonomous" AI agents sold in demos fail in production: they act out of scope, hallucinate data, and nobody knows what they did. Autonomy without guardrails is a risk, not a feature.
Our answer
We build agents with an explicit action perimeter: tools they can access, allowed actions, human escalation points and complete logging. The agent does what it's designed for : and asks when in doubt.
What LaMeDuSe AI takes care of
- Process analysis and agent perimeter definition
- Agent connected to your tools (APIs, databases, files)
- Guardrails: allowed actions, human validation, escalation
- Complete logging of actions and decisions
- Quality monitoring and drift detection
- Tracking interface for your teams
- Evolution at the pace of earned trust
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
Our agents run on our European infrastructure with integral logging: every action is traceable, every decision auditable. This requirement isn't an add-on we sell : it's how we run our own systems.
Frequently asked questions
A chatbot answers; an agent acts: it queries your systems, executes multi-step tasks and triggers real actions (create a ticket, process an order, draft a document). The difference is the action perimeter : and therefore the guardrails needed.
Three mechanisms: an explicitly allowed action perimeter (whitelist), human validation thresholds on sensitive decisions, and complete logging making every action verifiable. Autonomy is granted in steps, as trust is measured.
Anything exposing an API or structured access: CRM, ERP, ticketing, databases, file systems, messaging. Systems without APIs can often be integrated through connectors or dedicated interfaces.
They absorb repetitive and preparatory tasks, and hand decisions to humans. In practice, agents increase team capacity rather than replace : tasks with expert value stay human.