The problem
Repair technicians across appliances, HVAC, electronics and industrial equipment were diagnosing faults from scattered manuals and tribal knowledge, so the same failure got solved from scratch on every service call.
The approach
- Built FieldSense for root cause analysis, RemoteSense for remote diagnostics and DocsSense for intelligent document parsing.
- Backed it with Node and MongoDB for real-time tracking of repair activity and equipment status.
- Pulled third-party and internal sources into one live view of diagnostics and parts compatibility.
- Trained predictive maintenance models to flag failures before they happen.
- Ran document storage on S3 and inference workloads on Lambda to keep costs elastic.
The outcome
Technicians diagnose from one interface instead of five, and predictive maintenance moves work from emergency callouts to scheduled service.