The platform doesn't just detect patterns. It acts on them.
Because we own the full stack — from the CAN bus to the command back to the vehicle — our intelligence layer doesn't just answer questions. It closes the action. Every time.
Every CAN message, GPS fix, charge event, and diagnostic code — normalised and ready in milliseconds.
Fleet-scale pattern models find what happened before — in other vehicles, at scale — and reason from it.
Command. OTA. Service ticket. Notification. The response that closes the loop, automatically.
The intelligence is only as good as what it sees.
Every signal. In real time.
CAN bus signals, battery charge cycles, GPS tracks, diagnostic trouble codes, OTA acknowledgments, command responses. All normalised against the vehicle's signal catalog within milliseconds of emission. Nothing batched. Nothing stale.
Fleet-scale pattern models.
Each signal becomes a vector in a model trained on vehicle telemetry — not text, not generic data. When the intelligence finds a similar vehicle, it means physically similar: same degradation curve, same thermal signature, same failure precursor. And it searches across the entire fleet to find it.
Grounded decisions. Specific actions.
Not a dashboard alert. Not a generic recommendation. A specific answer grounded in this vehicle's data compared against the fleet — followed immediately by an action the platform can take: a command, a campaign, a notification, a service appointment.
The model improves as the fleet grows. A pattern seen in one vehicle becomes context for a thousand. A failure signature seen across a batch becomes a predictive rule for the next hundred thousand. The intelligence compounds — that is not a feature, it is a structural advantage.
Six questions. Six loops closed.
Each scenario shows the same three steps: what the intelligence retrieves, what it concludes, and — critically — what action it takes. That last step is what most platforms cannot do.
“Why is this battery degrading 30% faster than fleet average?”
The intelligence retrieves this vehicle's full charge history, cell-level voltage curves, and thermal patterns — then searches the fleet for physically similar degradation signatures. It finds 43 vehicles from the same production batch with matching cell-group variance.
Charge rate restricted automatically. Service appointment created. Dealer notified. RCA template pre-filled.
“Is it safe to push this firmware to 50,000 vehicles right now?”
For every vehicle in the cohort, the intelligence checks current battery state, connectivity quality, and anomaly history against fleet-learned failure signatures from previous campaigns. 8,231 vehicles are healthy. 1,769 show patterns that preceded failures in the last three campaigns.
Cohort re-ranked. High-risk vehicles excluded from first wave. Rollback triggers calibrated to fleet history. Campaign runs itself.
“What maintenance does my fleet of 2,000 vehicles need in the next 30 days?”
The intelligence crosses degradation trajectory models against maintenance history, parts lead times, and workshop capacity. It outputs not a list sorted by mileage, but a risk-weighted schedule — accounting for failure probability, replacement cost, and operational impact.
Work orders created. Drivers notified. Parts pre-ordered. Workshop calendar blocked.
“Battery thermal anomaly detected. No engineer awake.”
The intelligence matches the anomaly's signature against historical incident patterns. It finds the same precursor in 12 prior events — each of which preceded thermal runaway within 72 hours. Confidence: 84%. The platform does not wait for human review.
Charging disabled via command layer. Driver alerted. Severity-1 incident created. RCA template drafted. Dealer appointment scheduled.
“Why is my range getting shorter every week?”
The intelligence retrieves this vehicle's energy consumption history, charge pattern behaviour, ambient temperature context, and similar vehicles' trajectories. It finds usable capacity down 4% in 60 days — faster than expected — with frequent DC fast charging above 80% as the primary contributor.
Charge limit adjusted. Pre-conditioning schedule optimised. Confirmed by driver in one tap.
“Which of my 500 EVs should I replace this year?”
The intelligence crosses per-vehicle Remaining Useful Life projections against maintenance cost history, energy consumption trajectories, and total cost of ownership models. 34 vehicles exceed the replacement threshold. 89 require monitoring. 377 are healthy through the year.
Procurement report exported. Replacements flagged to fleet management system. Health inspections scheduled.
Three reasons the loop can only close here.
Domain-specific pattern models
The pattern models are trained on CAN bus signals, charge cycles, and thermal gradients — not text, not generic data. When the intelligence finds a similar vehicle, it means physically similar: the same electrochemical degradation signature, not a semantic match. That specificity is the difference between a useful prediction and a generic alert.
Full stack ownership closes the action
An analytics platform can show you the anomaly. We can respond to it — in seconds, automatically — because the command layer, the OTA layer, and the notification layer are all part of the same platform. The intelligence doesn't stop at the answer. It closes the action. That loop is only possible when you own the whole chain.
Fleet scale creates compounding intelligence
A single vehicle's telemetry is a time series. Ten thousand vehicles is a pattern library. A hundred thousand vehicles is a model that predicts the future. The accuracy improves as the fleet grows. Each new vehicle adds signal that benefits every vehicle already on the platform. That is a structural advantage that cannot be bought — only built over time.
See how the loop closes in your programme.
How closed-loop intelligence integrates with your vehicle programme
Architecture walkthrough, integration patterns, and a discussion of how the intelligence layer connects to your existing TCU, data infrastructure, and service network.
Talk to engineeringAn intelligence brief for your fleet segment
A segment-specific view of what the closed-loop intelligence layer sees, concludes, and acts on — with outcomes mapped to your operational questions.
Request an intelligence brief