Real-time operations, asset and sustainability analytics for energy and utilities — built on direct IoT and sensor integration rather than periodic manual readings.
Sensor data is collected but never reaches the people making operational decisions. Maintenance runs on a calendar rather than on condition. Sustainability reporting is assembled manually each quarter, and outages are explained after the fact instead of predicted.
We integrate IoT and SCADA sources into a streaming data layer, build condition-based maintenance analytics on top, and automate the sustainability reporting that currently consumes a quarter of somebody's year.
Grid and asset condition visible continuously. Maintenance prioritised by actual risk. Sustainability metrics produced automatically from operational data rather than reconstructed by hand.
What We Build
Live operational state across assets and regions, with threshold alerting.
Sensor and telemetry streams landed into a governed analytical layer.
Condition-based failure risk scoring so intervention precedes the outage.
Emissions and consumption metrics generated automatically from operational data.
Root cause, duration and recurrence tracked across the asset base.
Demand patterns by region and segment, supporting capacity planning.
Proven Results
Proof
Technical Stack
Context
Energy and utilities operations are among the most heavily instrumented environments in industry, and among the least well served by analytics. Sensors generate continuous telemetry, SCADA systems record it, and the people making operational decisions frequently work from a daily summary. The data exists; the path from sensor to decision does not.
Maintenance is where the cost shows up most clearly. Calendar-based schedules service assets that do not need it and miss the ones that do. Condition-based prioritisation requires the telemetry to reach an analytical layer continuously, which is an integration problem rather than a modelling one.
Sustainability reporting is the other recurring burden. Emissions and consumption metrics are usually reconstructed by hand each quarter from the same operational data that already exists in the estate. Automating that is straightforward once the integration layer is in place, and it removes a recurring drain on a technical team's time.
How It Runs
FAQ
In most cases yes, via historian databases, OPC interfaces or vendor APIs depending on what is exposed. We work with the integration path your system supports rather than requiring a change to it.
It depends on failure history and sensor coverage. Where you have both, condition-based risk scoring is achievable and is where the return concentrates. Where you have telemetry but little failure history, we start with threshold-based alerting and build the dataset that makes prediction possible later.
Yes, and it is usually one of the clearer wins. Emissions and consumption metrics generated automatically from operational data remove a recurring manual exercise and improve consistency between reporting periods.
With an architecture designed for it: streaming ingestion, appropriate aggregation at rest, and a warehouse layer sized for analytical query patterns rather than raw retention of every reading at full granularity.