System 02 · EnergyConcept and prototype
EnergyAI Ops brings equipment, energy and maintenance data into one workspace. It connects to existing meters, sensors, SCADA, BMS, DCIM, Excel/CSV files and other data sources. It helps engineers find deviations faster, understand their likely causes, make well-founded decisions and verify the outcome of maintenance work.
EnergyAI Ops does not replace SCADA or BMS - it adds what they lack: shared context, analytics and a clear engineering process.
At most sites, data is scattered across different sources:
Engineers switch between systems by hand, match readings, hunt for documents and reconstruct equipment history. As a result:
The platform answers the five questions an engineer asks every day:
A deviation is always read in context:
Monitor energy use and equipment modes without constantly switching between meters, SCADA and spreadsheets.
Connects telemetry with repairs, inspections and servicing.
One view of risks, equipment condition and operating priorities.
Manage multiple sites, standardise maintenance and keep engineering knowledge inside the company.
EnergyAI Ops connects to the site's existing infrastructure. No equipment needs to be replaced.
Manuals, procedures, specifications and maintenance history become a structured digital passport.
AI helps extract information from documents, but safety-critical fields are approved by a specialist.
In the first days, the system learns how the equipment behaves and checks data quality. Initial observation usually takes 2 to 5 days. At this stage EnergyAI Ops gives no final diagnosis - it builds an initial baseline, a model of normal equipment behaviour.
Model maturity depends on these factors.
The platform compares current readings with the normal mode and flags unusual changes.
EnergyAI Ops does not treat every signal as a failure. It checks the context - which cuts useless alerts and makes recommendations better grounded.
The engineer remains the decision owner. Feedback is stored and refines models, rules and baselines.
After maintenance, EnergyAI Ops compares readings before and after the work. Maintenance becomes a closed, verifiable loop.
Energy use above baseline at comparable load
…
Engineer's decision
Illustrative example
Monitors the quality of incoming data.
Works with documents and digital equipment passports.
Builds the model of normal operation.
Analyses a deviation together with its context.
Explains the situation in plain engineering language.
The platform does more than report a problem. It keeps the full context of a technical event:
Not tied to one equipment manufacturer or automation system.
Built for operating sites that already have meters, sensors and dispatch systems.
Takes documentation, equipment history, maintenance and staff decisions into account - not just time series.
AI helps analyse data, but critical conclusions and actions are confirmed by an engineer.
Links a deviation to the work done and lets you verify the result against data.
Models and rules improve as history and specialist confirmations accumulate.
The actual economic effect can only be determined by a pilot project and depends on the site, data quality, equipment and evaluation criteria. EnergyAI Ops makes no upfront claims of validated savings or forecasting accuracy.
Current work:
A pilot needs:
During the pilot, we will define together:
We are looking for industrial sites, technology partners and engineering teams to test EnergyAI Ops on real data together.
Discuss a pilot project
A newsroom, a content pipeline, engineering data or an EnergyAI Ops pilot at your site. Tell me the problem - we'll work out where AI delivers and where it doesn't.