Every deployed asset under continuous performance monitoring. Where they're underperforming, where they're over-utilized, where the next service truck should roll — and what part it should bring.
Every asset tagged at install. Predictive maintenance schedule. Live telemetry from every connected device.
Most asset programs are reactive. Truck rolls when something breaks, monthly preventive maintenance whether or not it's needed. Asset Intelligence flips it.
The six capabilities that make this module work end-to-end. Pick any one as your starting point — they compound.
Native ingestion from chargers, inverters, radios, smart meters, gateways. Vendor-neutral, normalized into a single asset record.
Per-asset baselines and cohort comparisons. Drift, intermittent fault, and slow-burn degradation surface before they become outages.
Service tickets fire from telemetry, not from the calendar. The right truck rolls to the right asset with the right part.
Mean time between failure by vendor, model, install crew, climate zone, and age. Procurement and design feedback loops at the portfolio level.
Each fault tagged to warranty status, vendor responsibility, RMA workflow. Vendor cost recovery automated end-to-end.
When asset behavior signals end-of-life, Customer Health flags the renewal as a hardware refresh opportunity — captured before the customer churns.
Telemetry flows in, baselines build automatically, anomalies fire when behavior drifts, condition-based dispatch sends the truck. Calendar-based PM disappears.
Native + cloud + gateway. Vendor-neutral, one normalized asset record per device.
1s—15min30-day baseline per device. Cohort baselines per model, region, climate.
AutoDrift, intermittent fault, slow-burn — scored against per-asset and cohort baselines.
< 5minService ticket fires with diagnosis, suggested part, optimal crew. Schedule-based PM retires.
Per cycleEach asset class — chargers, inverters, radios, RTUs — gets its own anomaly model and dispatch policy. Hard thresholds for safety-critical signals, learned thresholds for operational drift.
Every anomaly explains itself: which signal drifted, which baseline it's compared against, what cohort behavior was. Crews show up with diagnosis, not guesswork.
# Anomaly Policy · L2 + DCFC chargers asset_class "ev-charger": # hard thresholds — safety when ground_fault_current > 5mA: severity = "critical" dispatch → immediate notify → safety_lead # learned thresholds — drift when contactor.cycle_time.zscore > 2.5: severity = "degraded" eta_to_fail = model.predict() if eta_to_fail < 45d: dispatch → cbm part = "AC-CONTACTOR-200A" # vendor pattern when cohort(vendor="B").comms_drop_rate > 2%: flag_for → vendor_review rma_eligible = true
Native to ChargePoint, EVgo, Tritium, Enphase, Generac — and 50+ more.
ServiceTitan, FieldEdge, Salesforce FSL — tickets fire with diagnosis attached.
First-party vendor APIs — RMA submission, tracking, cost recovery automated.
MTBF, fault patterns, vendor scorecards — exported to your warehouse.
Pick a single depot or region. We'll connect Asset Intelligence to its telemetry, build the baselines, and walk you through the first month of anomalies it surfaces.