Per-vehicle threshold alerts catch failures one at a time. iMatrix watches your entire fleet for systemic patterns — and flags the bad bearing, the weak alternator batch, or the coolant drift weeks before any check-engine light fires.
"Predictive maintenance" used to mean reading error codes the moment they fire and dispatching a technician. That's still a reactive system — just with faster response time. The next generation looks at the fleet as one organism.
Each truck monitored in isolation. Alerts fire when a sensor crosses a hard limit, an error code is set, or a service interval is hit.
iMatrix AI baselines every model, route, and driver, then watches for drift, clusters, and outliers across the whole fleet. The signal is the population, not the individual.
For decades, automakers focused on what happened during a crash. Crumple zones, side-impact bars, airbags, five-point belts — engineering for the impact you couldn't avoid.
The last decade flipped the question. Lane-keep assist, automatic emergency braking, blind-spot monitors, predictive cruise — the goal isn't surviving the crash anymore. It's making sure the crash never happens.
Fleet maintenance is at the same inflection. Error codes and service intervals are the airbags of operations — they keep you running when something fails. Fleet-wide AI is the AEB: the system that sees the failure forming and prevents it from happening at all.
No single layer is novel — the value is running all four against the full fleet's telemetry, continuously, with feedback from every confirmed root cause.
Build behavioral envelopes for every vehicle/model/route/driver combination. What does normal coolant, voltage, vibration, and fuel economy look like for this truck on this route on a 78° day?
Flag drift on individual vehicles. Then cross-reference: are the same patterns showing up on multiple vehicles? Same model? Same depot? Same parts batch? Same route?
Correlate flagged patterns against build dates, supplier batches, service history, ambient conditions, and driver assignment. Surface the most likely systemic cause — not just "vehicle 7 is acting weird."
Output is an ordered list of jobs ranked by failure probability × downtime cost × parts availability. Maintenance teams stop chasing every yellow light and start working the queue.
Examples of failures that don't trigger any single-vehicle error code, but show clearly when the whole fleet is in view.
A small upward shift in vibration signature appears on 28 of 412 trailers — all built in the same 6-week window. Individually each one is still within spec.
Resting battery voltage on Class-8 tractors trends down 0.05 V/month over 90 days — across 18 vehicles, all servicing the same southwest route during summer.
Average operating coolant temp on a sub-fleet rises 4°F over 60 days. No vehicle hits the alert threshold. No DTCs. Pattern correlates with a supplier change on the coolant additive.
MPG on identical vehicles diverges by route and driver assignment. The variance widens across a quarter — not from any single factor, but from compounding tire pressure, idle time, and route choice.
Fleet-wide AI is software on top of the same telemetry FleetConnect already streams. No additional hardware purchase. No separate sensors to install.
The hardware family that captures CAN bus, GPS, accelerometer, voltage, and BLE sensor data continuously across your fleet.
FleetConnect details →Global cellular connectivity that keeps every vehicle reporting — including cross-border and remote routes where regional carriers fail.
EdgeUplink details →When the AI surfaces a parameter change that fixes the systemic issue, push the new firmware or configuration without a truck roll.
SafeSync details →Talk to our engineering team about deploying fleet-wide predictive maintenance on your existing FleetConnect hardware — or scoping a pilot if you're new to the platform.
Talk to Engineering →