01Business problem
When a machine broke down, the clock started ticking. The first minute went to finding the technician, the next several to diagnosing the fault and finding the fix. The answer could be anywhere: in databases, manuals, inspection reports or in a technician's head. And when knowledge stayed in one plant, the same failure elsewhere meant starting from zero. Every minute of line stop can cost around €1M.
02Intervention
Asset data, work orders and repair history from every site brought together in one system, with a conversational agent on top. Technicians ask what went wrong, get to the root cause and find the next step, while the system points to where better preventive maintenance could stop the failure from happening again.
03Role of data & AI
Every recommendation traces back to real past cases, not a guess, so a technician can see why it was made. Records in different languages are reconciled automatically, so a fix logged in one plant surfaces just as easily for another. And the set-up is built to plug into a new site with nothing to rebuild.
04Adoption
Every intervention adds to the shared knowledge base, so the system gets smarter with every repair. As adoption grows, the tool can evolve into a multi-agent system, extending from diagnosis to a broader maintenance intelligence layer.
05Measurable impact
Under 30 seconds from alert to root-cause diagnosis and next step; preventive maintenance backed by evidence.