Energy companies have plenty of data — SCADA historians, PI OSIsoft archives, smart meters, event logs. The headache is that almost none of it arrives ready for machine learning. We supply the specialist engineers who turn raw operational tech into production-ready data assets.
Energy operators are not short of data. They are short of data that a machine-learning model can actually ingest — aligned, cleaned, and continuously flowing from the systems that run the grid.
We bridge that gap. We extract from live PI OSIsoft systems without disrupting operations, map complex DNP3 and IEC structures, and align everything to standard Common Information Models.
71% of energy AI projects stall at the pilot phase. In most cases, the data pipeline is why.
Six delivery streams across pipeline engineering, protocol-level extraction, and the quality and governance frameworks your teams can maintain independently.
“Clean data pipelines aren't an IT upgrade. They are the precondition for every AI outcome that follows.”
Once your data is AI-ready, here is what you can actually build on top of it.
Build 01
With clean, continuous high-frequency sensor streams, your AI can predict failures, optimise maintenance and extend the life of critical infrastructure.
Build 02
Structured historical and real-time telemetry lets your models deliver production-grade, hyper-accurate load and generation forecasts.
Build 03
Unifying fragmented event logs and telemetry gives your AI the clean inputs needed to flag cyber threats and hardware faults instantly.
Our engineering process is designed specifically for mission-critical energy environments — you never risk grid stability to pull data.
Phase 1 · Deep map
We map your legacy data silos and protocols — exactly where the operational truth lives and how it moves.
Phase 2 · Safe extraction
We extract from live systems without touching core operations or risking a microsecond of grid stability.
Phase 3 · Standardise & scale
We format it into clean, standardised data products that feed forecasting, anomaly detection and optimisation models.
We map, normalise and connect across the operational, commercial and asset systems you already run — no rip-and-replace required.
PI OSIsoft
Historian extraction
DNP3 · Modbus
Industrial protocols
IEC 60870-5-104
Telemetry
IEC 61850
Substation automation
GB CIM
Interoperability
Kafka · Spark
Streaming
AWS / Azure IoT
Cloud ingestion
GE Historian
OT archives
71% of energy AI projects stall before production, and the cause is almost never the model — it is the data pipeline. Fixing data foundations first is faster and cheaper than building a model around bad inputs and only discovering the problem at pilot review.
Tell us where your data lives and what you want to build on top of it. We'll come back with the right energy data specialist and the right engagement model.