Designing an AI use case: where the ones that do not fail start
The seven steps that separate an anecdotal proof of concept from a system the shop-floor team uses every day.
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Expert in designing and implementing AI use cases in industrial environments. Founder of PLAIfield, he has trained more than 4,000 professionals in applied AI. He leads the technical and validation phases of the Caliber Method™.
Carles designs the AI use cases that industrial SMEs can actually implement — not on paper, but on the shop floor. His job is to translate an operational diagnosis into a working prototype, and a prototype into a system a non-technical team can operate.
Founder of PLAIfield and associate lecturer at the University of Girona, he has trained more than 4,000 professionals in AI applied to business environments. He combines academic experience with hands-on projects in manufacturing, logistics and computer-vision-assisted quality control.
Translating an operational diagnosis into AI architectures deployable in shop-floor environments.
Quality control, anomaly detection and automatic counting systems for manufacturing.
Enabling non-technical professionals to operate and maintain AI solutions autonomously.
Protocols to assess whether an AI solution is viable, robust and maintainable in the long term.
The seven steps that separate an anecdotal proof of concept from a system the shop-floor team uses every day.
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A review of integration patterns across 12 implementations: where ROI comes fast and where everything stalls.
The 9-hour module we have run at six industrial companies. Format, contents and real results.