Energy & Utilities AI Engineering

No, and it should not. Grid control stays in certified, secured systems under NERC CIP. AI forecasts demand, predicts failures, and flags anomalies for operators to act on, but it does not actuate control. That separation is a security requirement, because anything that can control the grid is also something an attacker would want to reach.

It depends on your network and how volatile demand is, especially with renewables, so we measure on your data rather than quoting a figure. Better forecasts reduce costly imbalance, and we compare against your current method with a proper holdout. Honest accuracy on your grid is the only number worth acting on.

Enough to schedule work, when the data supports it, which is the whole point. We measure useful lead time on your assets before promising outage reduction, because a warning that arrives too late prevents nothing. A model that predicts failures you cannot act on in time is not worth deploying.

Only with the right isolation, which we design for. AI observes operational data through secured, one-way paths where needed, under IEC 62443 and NERC CIP, without opening a route into control systems. Connecting AI to OT is a security project as much as a modeling one, and we treat it that way.

You do. Your SCADA, meter, and asset data, the trained models, and the integration code remain yours, in your environment. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single load forecasting build is a modest, weeks-long project, while a wider rollout across SCADA, meter, and asset systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from fewer outages, better load balancing, and longer asset life, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as load forecasting usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are load and demand forecasting, asset predictive maintenance, grid anomaly detection, and outage documentation. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.