Operations AI that respects OT reality
Plants, grids, and field assets cannot tolerate always-on cloud assumptions. We design for offline modes, safety interlocks, and maintenance windows.
IT/OT separation
Keep control loops on prem or in qualified environments; use AI to advise, schedule, and prioritize - not to bypass safety systems. Document network paths and patch processes explicitly.
Data gravity
High-frequency telemetry often belongs near the asset. Plan ingestion, feature stores, and model delivery with bandwidth and latency truth, not lab assumptions.
Frequently asked questions
Can we use cloud LLMs for industrial copilots?
Often yes for non-real-time tasks with strict data handling. For shop-floor or grid operations, expect hybrid patterns with on-device or private inference for time-critical paths.
What KPIs matter first?
Unplanned downtime avoided, mean time to detect/resolve, energy efficiency gains, and safety incident rate - each tied to model and sensor inputs you control.
How do we test without risking production?
Shadow deployments, digital twins, and replayed historical alarms let you validate recommendations before operators act on them.
Can an agent change a set point on a plant or grid asset?
Not as a default. Keep control loops on qualified OT. Use AI to advise, schedule, and prioritize. Any write-back needs an interlock, a named approver, and a revoke path that does not depend on the agent.
What does energy AI governance add beyond generic responsible AI?
HSE and reliability evidence: safety interlocks, patch windows, OT/IT network paths, and an inventory that distinguishes advisory copilots from anything that can affect generation, transmission, or field operations.
Next step: a fixed-fee diagnostic.
Three weeks. Board-ready brief. Ranked opportunities. No discovery theatre.
Book a diagnostic →