Energy
Serving energy operators and utilities in Spain
Load predictions based on simple historical data without incorporating temperature, holidays, events, or flexible demand signals. Error paid in capacity reserves or real-time imbalances.
Transformers, lines, and substation equipment maintained on schedule or failing before the maintenance cycle arrives. Unplanned downtime cost multiplies the cost of advanced preventive maintenance.
Gap between injected and billed energy living in the "losses" line without diagnosis of cause or zone. Fraud, illegal taps, or metering errors assumed as structural cost.
Control room acting on alarms — reactive by design. The ability to anticipate incipient overloads, voltage anomalies, or risk conditions in assets is in the data but not in the systems.
01
Models combining consumption history, temperature, holidays, weather forecast, and flexible demand signals to predict load by zone or node with hourly and daily horizons. Dispatch adjusts reserves and scheduling before the imbalance occurs.
What changes
Forecasting error reduced 20-35%. Capacity reserves optimized. Fewer real-time imbalances to resolve at high marginal cost.
02
Models on telemetry data from critical transformers, switches, and lines that estimate failure probability within a time window. Maintenance is scheduled before the asset fails — not after.
What changes
Unplanned downtime on monitored assets reduced 25-40%. Total maintenance cost falls by eliminating emergencies and optimizing the preventive calendar.
03
Continuous analysis of injection-versus-billing differences by zone, correlated with historical consumption patterns, meter behavior, and reading variables. The system flags priority zones and customers for inspection with a risk score.
What changes
Revenue recovery from localized non-technical losses. Targeted inspections with 3-5x higher success rate than random inspection.
04
Continuous SCADA measurement monitoring to detect out-of-range behavior: incipient overloads, voltage asymmetry, insulation degradation. Prioritized alerts to the control room before they become an alarm or failure.
What changes
Incident response time reduced. Cascade failures avoided by early action. Control room with less alarm noise and more actionable information.
05
An assistant managing customer communications about outages, readings, and billing: reports outage status in real time, opens incidents, requests data, and transfers to a technician when needed. No queues at a saturated call center during mass outages.
What changes
60-70% reduction in repetitive call center calls during incidents. Average complaint handling time drops. Customer NPS during outages improves from proactive information.
3-4 weeks
Audit of available data (SCADA, meters, maintenance, billing) and critical processes. We identify the 2-3 cases with the best ROI for your asset type and operating area.
10-16 weeks
Implementation of forecasting, predictive maintenance, or loss detection on a defined perimeter (geographic zone, asset fleet, or customer type).
6-10 months
For utilities wanting an AI layer across all operations: forecasting, maintenance, losses, and customer service on a common architecture.
No. We work in read-only mode on data already coming out of the grid (SCADA, meters, maintenance logs). Models generate recommendations — the decision to act on the grid is always made by the human operator. The architecture respects OT/IT segregation by design.
It depends on the use case. Forecasting with annual seasonality: ideally 2-3 years of clean hourly series. Predictive maintenance: failure history plus telemetry (12-18 months is usually enough to start). Non-technical losses: billing and meter readings from the last 24 months. We specify this in the AI Readiness.
Prediction and analysis models are operational support tools, not regulated control systems. Integration with regulatory reporting systems is part of the scope where applicable — and documented for any regulator audit.
AI Readiness: €8k-€15k. Single-process pilot: €40k-€90k depending on perimeter. Operational multi-case platform: €120k-€250k+ over several months. ROI in avoided maintenance or recovered losses has clear, measurable metrics from the first month post-go-live.
Treated as a hard constraint. We work with data architectures that do not directly expose OT systems, on-premise deployments when required, and no write channels toward control systems. The client's industrial cybersecurity team participates in design from the diagnosis phase.