A fleet that fixes itself (before it breaks)
A fleet operator was tired of roadside failures and surprise repair bills. They had telemetry — they needed predictions.
A fleet operator was tired of roadside failures and surprise repair bills. They had telemetry — they needed predictions.
Maintenance was mileage- or time-based. When something failed on the road, it was a tow, a delay, and an unhappy customer. The fleet had telemetry from vehicles — engine, battery, brakes, usage — but it was used for dashboards, not predictions. They wanted to know which units needed attention before the next failure and to schedule service in a way that didn’t disrupt operations.
We built a vehicle health and prediction layer that ingests their telemetry and maintenance history. It models failure risk for critical components (e.g. battery, brakes, alternator) and produces per-vehicle alerts and recommended service windows. Fleet managers see a prioritized list and can slot work into available downtime. We integrated with their existing fleet and workshop tools so alerts and work orders flow through. The system learns from their fleet mix and driving patterns so predictions stay relevant as they add new vehicle types or regions.
Roadside failures and unplanned downtime dropped. Maintenance cost became more predictable and better aligned with actual risk. The operator is now using the same data to optimize replacement timing and to pilot the approach for their EV segment as it grows.