Purpose-built. Field-proven. Ready to deploy.
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GridEdge-AI turns raw operational data into intelligence at the edge — before it ever leaves the site.
Reduced downtime. Faster resolution. Fewer false alarms. Every event classified. Every pattern remembered.
See how we work for youMonitoring systems collect everything and struggle to make it useful. You're storing terabytes of data that nobody has time to interpret.
By the time data reaches the cloud, the event already happened. Centralized architectures are structurally late. Decisions need to form at the edge and the gateway — not after the round trip.
Teams drown in dashboards but starve for answers. More screens don't create better decisions.
GridEdge-AI deploys intelligence directly to the device and unifies collection across every sensor, system, and protocol in the environment. It classifies, correlates, and reports conclusions — not raw streams.
Awareness compounds at every layer — device, gateway, center — building a persistent knowledge base that only gets sharper. No cloud dependency. No latency penalty. Decisions at the speed of the event.
Purpose-built. Field-proven. Ready to deploy.
Three stages. One continuous loop. Intelligence sharpens with every cycle.
Intelligence lives at the device — and compounds across devices. Edge nodes correlate with each other at the gateway, enabling campus-wide and geographic analysis before data ever reaches the center. Feeder-to-feeder, building-to-building, zone-to-zone — distributed insight that centralized architectures simply can't deliver.
Clarity on what happened, why, and what to do next. A tiered inference engine correlates patterns, anomalies, and operational history — rules-based intelligence at the edge, learned intelligence at the node.
Operators receive conclusions with confidence scores, incident and problem history, and recommended actions. The knowledge base remembers everything it has learned.
Revenue-grade instrumentation, power quality analysis, and disturbance detection — in a field-hardened enclosure that deploys anywhere from a utility pole to an exascale facility.
AI at the edge. Onboard classification, self-labeling, and gateway correlation in a sealed NEMA enclosure.
Why exascalers need this
Voltage stability. AI training clusters create millisecond-speed load transients. A single sag event took 1.5 GW of data center load offline in Virginia in 2024. Detection has to happen at the rack, not after the incident report.
Harmonic integrity. Thousands of non-linear power supplies generate harmonic currents that compound at scale — degrading UPS systems, tripping breakers, and shortening component life. Without continuous monitoring, the damage is invisible until something fails.
Revenue-grade verification. When you're buying megawatts and billing tenants per kilowatt-hour, certified measurement isn't optional. Billing disputes, compliance mandates, and multi-tenant cost allocation all demand ANSI C12.20 accuracy — not estimates.
Disturbance classification. Knowing something happened isn't enough. Was it capacitor switching, a VFD ramp, a utility sag, or a training checkpoint? Real-time classification at the point of measurement turns a mystery outage into an actionable event.
Exascale-ready. Panel-mounted power quality verification where AI loads demand precision monitoring — voltage stability, harmonic integrity, and disturbance detection at the rack level.
Every site, every system, every domain — unified into a single operational surface. Not dashboards. Operational decisions — classified, contextualized, and ready for action.
Every site, every system, every data source converges into a single operational surface. No more cycling between SCADA, BMS, power monitoring, and security consoles. One view. One truth.
Classify at the source. Ship conclusions, not data. The device knows what happened before the network does — and acts on it locally.
A novel approach to operational knowledge ingestion. Diagrams, spec sheets, wiring tables, and charts are decomposed into individually addressable, graph-linked knowledge artifacts — not text chunks dropped into a vector store. Structured operational intelligence that machines can reason over.
The platform doesn't reset between shifts, between sites, or between incidents. Every event, every classification, every operator correction compounds into institutional knowledge that only gets sharper.
Two decades of operational intelligence. From the first smart meter platform to the edge.
Whether you're running a data center, a substation, or a forward operating base — if downtime costs you money, time, or trust, we built this for you.
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