Mechanical Engineering: Maintenance and Fault Analysis with a Local AI Copilot

A mid-sized machine manufacturer with 200 employees sells and maintains complex production systems. Its technical field service — 18 service engineers — works in a decentralized way: on-site at customer facilities every day, often with poor or no internet connectivity. When confronted with an unfamiliar fault, they used to call the home office or flip through paper manuals for hours. Design data, fault histories, and spare parts catalogues were spread across different systems — none of them usable in the field.

Know-How That Must Not Leave the Company

The manuals and design data are the company’s core knowledge. They contain proprietary developments that are competitively sensitive. Processing them in external cloud AI services would have meant handing that know-how to a third party — unacceptable.

Offline-Capable Technician Copilot with SoverIQ Box

SoverIQ Box — a hardened edge device — is issued to each service engineer. The device runs completely offline and contains:

• All machine manuals as a searchable knowledge base
• The fault history of every system (synchronized from ERP when online)
• The spare parts catalogue with availability status

In the field, the technician asks by voice or text:

“Error code E-447 on system type MX-3 — what’s the most likely cause?”

The model analyses the error message, cross-references it with the machine’s fault history, and suggests the three most probable causes along with remediation steps.

Results

Average fault resolution time dropped by 35%. Escalations to the home office fell by 60%. New service engineers now reach full productivity in 6 instead of 14 weeks — because the experience of senior colleagues is embedded in the knowledge base.