Fleet Health Intelligence
Predictive maintenance for vehicle fleets — modelling failures from telemetry before they become downtime, and surfacing the right signal to the right operator.
What it does
A vehicle fleet generates a continuous stream of telemetry — motor temperatures, battery voltages, wheel speeds, encoder counts, error codes. Most of that data is noise. A small fraction of it, in the right combinations, predicts a failure that hasn’t happened yet. Fleet Health Intelligence finds those combinations.
The outputs are operator-facing: “Vehicle 14 is showing the pattern that preceded the bearing failure on Vehicle 22 last quarter; schedule it for inspection.” Not a probability score. Not a chart. A specific, actionable recommendation tied to a specific vehicle and a specific failure mode.
Why this approach
Predictive maintenance is one of the most over-promised analytics categories of the last decade. The systems that actually work share two properties: they’re trained on the failure modes of the actual equipment in question, and their outputs are framed as actions, not insights. We build for both.
What’s in the system
- A historical store of fleet telemetry, with labelled failure events.
- A feature pipeline that converts raw telemetry into the rolling windows and lagged signals the models actually use.
- Per-failure-mode models, each producing an action recommendation.
- A delivery surface that puts those recommendations in front of the people who can act — not buried in a dashboard nobody opens.