EV Operational Intelligence

Turn EV telemetry into faster, better decisions.

Real-time dashboards, anomaly detection, demand-side analytics, fleet utilisation, and executive scorecards — built on a single source of truth across the company.

AnalyticsDashboardsAnomaly DetectionFleet Analyticsdbt

A network is only as good as its decisions

By the time an EV operator is running a hundred stations or a thousand vehicles, the rate-limiting factor is no longer hardware or software. It is decision throughput — the number of correct calls the operations, finance, and product teams make per week, and how quickly those calls translate into actions.

Our operational-intelligence practice exists to raise that throughput. We build the dashboards, alert systems, demand-side analytics, and executive scorecards that sit on top of the telemetry and billing pipelines — so that every team has the same numbers, the same alarms, and the same view of what is actually happening across the network.

What we typically build

  • Real-time operations dashboards — Per-station, per-region, and per-shift views of uptime, throughput, energy delivered, swap-time distribution, and revenue. WebSocket-driven for sub-second freshness; layout configurable per role.
  • Anomaly detection — Statistical and ML-based detectors over operational signals (utilisation drop-off, payment failure rate spikes, MTTR creep, customer-cancel rate). Alerts routed via PagerDuty / Slack / Teams with inline context.
  • Demand-side analytics — Customer cohorts, retention curves, session-level lifetime value, and elasticity analysis on tariff changes. Feeds product, marketing, and finance.
  • Fleet utilisation — For fleet operators: vehicle-level utilisation, idle-time, charging-time, dwell at depot, and TCO per kilometre — sliced by route, driver, and vehicle class.
  • Predictive maintenance — Pack-level RUL feeding into maintenance schedules; charger MTBF and MTTR forecasting; proactive replacement planning instead of reactive break-fix.
  • Executive scorecards — Weekly and monthly board-ready packs with deterministic numbers, drift-detection on key metrics, and explicit narrative on why the numbers moved.

Built on the same data your engineers use

The intelligence layer queries the same lakehouse the data engineering team works in — the same numbers your CFO reads in the board pack are the numbers your engineering manager pulls in a notebook. There is exactly one definition of "uptime", "energy delivered", and "active customer" across the company. That single-source-of-truth discipline is the foundation of every other claim on this page.

Frequently asked

Either. We expose the canonical lakehouse via SQL and via a documented API. Many clients use our operations dashboard for real-time work and a tool like Looker or Superset for ad-hoc analysis — both reading the same numbers.