EV-Charging Network AI: From One Depot to an Operated Fleet

About the Client

This case study illustrates the pilot-to-production methodology in the EV-charging domain — the domain of Auralink, Hyperion's owned pre-production reference platform. The scenario shows how a representative engagement would run — no client outcome is claimed. Illustrative scenario, not a specific client engagement.

Size: Representative: a European charge-point operator, 500–5,000 charge points

The Challenge

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

My Solution

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.

Implementation Phases

• Weeks 1–2Production readiness reviewThe depot pilot assessed against the network's real hardware and telemetry diversity — the blocker map and the scale decision.
• Weeks 3–6Edge-first architectureSite-level inference tolerant of backhaul loss; OCPP-version-agnostic telemetry normalisation; offline-tolerant sync.
• Weeks 7–10Autonomous incident workflowsBounded automated remediation for defined incident classes, deterministic guards around every action, human escalation paths — the pattern class proven in Auralink's simulated operations.
• Weeks 11–12Operate & transferNetwork dashboards, per-site health scoring, and operator handover to the CPO's own team.

Site-level edge inference · OCPP 1.6J/2.0.1 telemetry normalisation · Autonomous incident workflows · Deterministic safety guards · Fleet observability · Offline-tolerant sync · Per-site health scoring · Load-management awareness · Demand-response windows · Escalation routing · Charger-vendor abstraction layer · Incident-class taxonomy

Results & Impact

Illustrative outcome: incident handling would move from dashboard-and-hope to bounded autonomous workflows with human escalation, operated by the CPO's own team. No client outcome is claimed.

Services in a Representative Engagement

Production Readiness Review · Pilot-to-Production Program · Physical AI Operating Partner · Edge AI Architecture · Autonomous Incident Workflow Design

Facing a Situation Like This?

Every engagement starts with a 30-minute diagnosis. Describe your situation, and I will tell you — honestly — whether I can help and how fast.