We place engineers and data scientists who can turn SCADA telemetry and sensor data into early warning systems — before failure becomes a callout, a curtailment, or a safety event.
Most energy asset failures do not happen without warning. The warning is in the data — vibration signatures, temperature drift, partial discharge patterns, current imbalance. The problem is that most organisations either don't have the pipelines to surface it in real time, or don't have the models to interpret it correctly.
We place the engineers who build both: the data infrastructure that gets the right signals out of SCADA and sensor systems, and the ML models that turn those signals into maintenance decisions a team can act on.
The warning is in the data — the gap is the pipeline to surface it and the model to read it.
01
Wind turbine drivetrain monitoring, blade health from vibration and thermal imaging, gearbox and bearing fault detection.
02
Transformer health monitoring, substation equipment degradation, protection relay performance tracking.
03
BESS cell health scoring, degradation curve modelling, cycle life optimisation against dispatch schedules.
04
Cable fault prediction, pole and tower structural monitoring, smart meter anomaly detection.
Eight capabilities spanning the full path from raw telemetry to a maintenance decision — health scoring, anomaly detection, physics-informed prediction, computer vision, prioritisation, CMMS integration, and operational dashboards.
“Energy asset engineering knowledge — not just ML applied to generic time-series data. Physics-informed models that respect engineering constraints, on live UK energy infrastructure.”
Step 01
We assess what SCADA, sensor, and historian data you have, its quality, its gaps, and what it can realistically support.
Step 02
We extract, structure, and clean the telemetry data into a form that ML models can train and run on.
Step 03
We build, validate, and deploy anomaly detection and degradation models against your live asset data.
Step 04
We connect model outputs to your maintenance workflows, CMMS, and dashboards so the intelligence reaches the right person in time to act.
01
Drivetrain, blade, and gearbox health monitoring for wind turbines at scale.
02
Transformer and substation equipment health monitoring on high-voltage networks.
03
Cable fault prediction and smart meter anomaly detection across distribution infrastructure.
04
BESS cell health scoring, degradation modelling, and cycle life management.
Condition monitoring tracks asset health metrics in real time — vibration, temperature, current, discharge. Predictive maintenance uses those signals to forecast when a fault will occur and trigger a maintenance action before it does. Condition monitoring is the data layer. Predictive maintenance is the intelligence layer built on top of it.
Tell us about your assets and the SCADA, sensor, and historian data you already stream. We'll place the engineers and data scientists to turn it into early warning systems.