How an incident-prediction pilot can move from one charging depot to an operated, network-wide product
A European charge-point operator context spanning mixed OCPP fleets, unreliable site connectivity and incident workflows that still depend on manual intervention.
Size: Representative: a European charge-point operator, 500–5,000 charge points
An availability and incident-prediction model works at one depot; scaling to the network means intermittent connectivity, OCPP fleet heterogeneity, and operations that cannot depend on a data scientist being awake.
An indicative starting point: one depot's chargers feeding a model that flags failures before they strand drivers — accurate locally, unproven across hardware generations
The charger fleet spans OCPP 1.6J and 2.0.1 hardware from multiple vendors; the pilot assumed one vendor's telemetry
Sites with poor backhaul lose cloud connectivity for hours; the pilot assumes always-on links
No incident-response loop: predictions land in a dashboard nobody owns at 3 a.m.
Grid-side constraints — load management, demand-response windows — are not represented in the pilot's data
No definition of which incident classes may be remediated automatically and which must page a human
A representative engagement would design the edge-first production architecture — site-level inference tolerant of backhaul loss, bounded autonomous incident workflows with human escalation, fleet-wide observability — drawing on the patterns proven in the simulated operations of Auralink, Hyperion's pre-production reference platform (78% autonomous incident resolution, arXiv 2603.08736).
Edge-first by design: the network keeps working when the cloud is unreachable, and automation stays inside deterministic guards.
The depot pilot assessed against the network's real hardware and telemetry diversity — the blocker map and the scale decision.
Weeks 1–2Site-level inference tolerant of backhaul loss; OCPP-version-agnostic telemetry normalisation; offline-tolerant sync.
Weeks 3–6Bounded automated remediation for defined incident classes, deterministic guards around every action, human escalation paths — the pattern class proven in Auralink's simulated operations.
Weeks 7–10Network dashboards, per-site health scoring, and operator handover to the CPO's own team.
Weeks 11–12The intended outcome is bounded autonomous incident handling with explicit human escalation, fleet-wide observability and operational ownership transferred to the CPO's team.
Every engagement starts with a 30-minute diagnosis. Describe your situation, and I will tell you — honestly — whether I can help and how fast.