The problem you're facing
Visual quality checks tire teams and let defects through; maintenance histories stay unexploited documents; production reports get filled by hand. Industrial data lies dormant.
Our answer
We address the proven-value uses: vision quality control trained on your lines, maintenance diagnosis support, technical data extraction. On-site deployment possible, with no cloud dependency.
What LaMeDuSe AI takes care of
- Computer vision quality control
- On-site deployment: local servers, appliance
- Technical document extraction and structuring
- Maintenance diagnosis support (RAG over your histories)
- MES / CMMS integration via API
- Monitoring: detection rates, false positives
- Continuous improvement from operator corrections
Our methodology
- 1
Understanding sector constraints
Compliance, confidentiality, regulation: constraints dictate the technical architecture from scoping.
- 2
Prioritised use case
Start with the highest-gain process, with before/after measurement.
- 3
Integration with the existing
Connection to sector tools (clinical systems, case managers, ERP) rather than a parallel system.
- 4
Documented compliance
Traceability, minimisation and evidence for your audits and processing records.
Technologies
Security & compliance
European hosting by default, encryption, data minimisation and access traceability : GDPR and sector-aligned.
Hosting
Group datacenters in France, Germany and Switzerland; full self-hosting option for regulated data.
Maintenance
Compliance tracking through changes, periodic security reviews and support by the team that knows your sector.
Why LaMeDuSe AI
Our vision systems are evaluated on detection and false-positive rates measured in real conditions, variable lighting, damaged products, difficult angles, not on demo datasets.
Frequently asked questions
Yes: vision and analysis models run on local servers or an on-site appliance. Only remote monitoring and continuous improvement need a connection : and even that is optional.
A few hundred well-annotated examples per defect type often suffice, complemented by data augmentation. The POC on your real images measures feasibility and achievable performance.
Via API: results (detections, scores, annotated images) feed your existing systems rather than creating a parallel tool. Connectors specific to your stack are scoped at the start.
Your operators, through a simple validation interface : and every correction feeds model improvement. The system gets more precise with usage, at your teams' pace.