The problem you're facing
Manual visual inspections are slow, tiring and inconsistent between operators. Classic vision systems, meanwhile, demand perfect lighting and collapse at the first unplanned case.
Our answer
We build vision systems adapted to your real conditions: training on your images, edge-case handling, confidence thresholds and human escalation of ambiguous cases. The system learns from your corrections.
What LaMeDuSe AI takes care of
- Visual need analysis: detection, classification, reading
- Training on your real images (including rare cases)
- Camera integration or ingestion of existing images
- Confidence thresholds and human validation circuit
- Monitoring: detection rates, false positives, drifts
- Continuous improvement from corrections
- On-site deployment or on our European infrastructure
Our methodology
- 1
Right model for the job
The right model for each use: generalist, specialised or self-hosted open source, arbitrated on cost and confidentiality.
- 2
Quality architecture
RAG, agents and guardrails designed to limit hallucinations and guarantee answer traceability.
- 3
Continuous evaluation
Business test sets, quality metrics and drift monitoring : AI without measurement degrades.
- 4
European operations
Models run on our European infrastructure, with monitoring and inference cost control.
Technologies
Security & compliance
Self-hosted model option for sensitive data, environment segmentation, managed secrets and call logging.
Hosting
Self-hosting on our European cloud (France, Germany, Switzerland) or hosted APIs with data minimisation.
Maintenance
Model watch (new versions, deprecations), quality re-evaluation, inference cost optimisation.
Why LaMeDuSe AI
We run vision systems on our infrastructure for industrial clients: our models are evaluated on measured detection and false-positive rates, not demonstrated on competition datasets.
Frequently asked questions
Fewer than expected if data is well chosen: a few hundred well-annotated examples per class often suffice, complemented by augmentation techniques. The POC evaluates feasibility with your existing images.
That's precisely what the POC validates: variable lighting, difficult angles, dirty or damaged products. We train on your real conditions, not lab images : and the system keeps learning in production.
Yes: vision models can run on local servers or dedicated machines on the production line. For sites without infrastructure, a small appliance often suffices.
Processing can stay fully local or on our European infrastructure: images don't leave the perimeter you validate, and no data is reused without your agreement.