A multinational scientific instruments company. The client needed intelligent fault diagnosis for electronic products, together with recommended solutions.
Artificial intelligence and machine learning convert raw source material — product user manuals, historical repair cases, past service work orders and documented expert experience — into knowledge tags. Those tags are then linked back to the original cases to build a graph knowledge base. A large language model performs the diagnostic analysis: when a user submits a query describing a problem, the AI engine searches and diagnoses against the graph database and runs a quantitative analysis. The result is a fault probability analysis, together with spare-parts recommendations and forecasts.