Capturing field expertise with speech-to-knowledge AI

How might we preserve the practical knowledge of experienced mechanics and make it accessible to the next generation?

Inform

Experience lives in people, not systems

The company depended on senior mechanics whose knowledge had been built through years of hands-on experience. They often knew how to approach a problem based on patterns they had learned over time, but much of that expertise was never documented. It was shared in conversations at the worksite and often stayed there. Recruiting junior mechanics was already difficult, while many experienced employees were approaching retirement. Existing systems could record hours and replacement parts, but not the reasoning behind a diagnosis or repair. The company needed a practical way to preserve that expertise and make it available to others.

Analyse

From individual experience to shared knowledge

The challenge was to capture the reasoning behind mechanics’ decisions without interrupting the way they worked. We designed a mobile app that mechanics could use directly at the worksite, as part of their existing workflow. They could describe what they observed, what they believed caused the issue and how they solved it. The app then turned that unstructured explanation into a clear sequence: observation → cause → solution. What would normally remain a brief conversation at the worksite could now become structured knowledge that was ready to be stored, searched and reused. Over time, those entries could form a growing knowledge base for less experienced mechanics to learn from.

Activate

Keeping expertise in the organisation

The result was a speech-to-knowledge app that captured practical expertise without asking mechanics to write extensive reports or manuals. By making the reasoning behind previous solutions searchable, knowledge that had previously lived in individual heads could become a shared resource across the organisation and continue to grow.