UtiliSmart
AI-Augmented Smart Grid Operational Intelligence Platform
UtiliSmart provides utility data analytics, meter data management and software solutions supporting electric utilities and other energy-sector organizations. Its SmartMAP platform provides engineering and operational teams with geographic analysis and near-real-time visibility into electrical distribution networks.
UtiliSmart's SmartMAP platform aggregates operational information from electrical distribution environments to help utility teams understand conditions across their networks. The system combines information such as voltage, load, outages, device health, infrastructure states and other operational signals that technicians use when investigating anomalies and determining potential causes of distribution-system issues.
Cylix was engaged to begin transforming SmartMAP from a highly capable visualization and analytics platform into an AI-augmented operational intelligence environment capable of helping utility professionals understand not only what is occurring on the network, but why it may be occurring.
The solution introduces contextual AI reasoning directly into SmartMAP, enabling operators to investigate selected graphs and operational indicators and receive prioritized root-cause hypotheses based on underlying telemetry, historical events, operational documentation, environmental conditions and recognized electrical-distribution patterns. The initial deployment establishes the AI and data foundation for a broader roadmap that can ultimately support more advanced capabilities such as semantic investigation, predictive analytics, anomaly forecasting and agent-based operational intelligence.
Solution Implemented
Problem Statement
Modern electrical distribution networks generate significant volumes of operational data from smart meters, sensors, substations and other grid infrastructure. SmartMAP already helps utilities convert this information into a geographic operational view of their distribution systems, including measured voltage and load conditions and other network information. However, identifying an abnormal condition is only one part of the diagnostic process. When an unusual voltage condition, load change, oscillation, transformer condition or other anomaly occurs, an OT technician must still interpret multiple data sources, compare historical patterns, consider environmental influences and apply utility-domain experience to determine the most probable cause.
The opportunity was therefore to introduce AI directly into this investigative process — helping technicians move from: "What is happening?" to: "Why is this happening, and what should I investigate first?" without allowing AI to autonomously modify or control power-distribution infrastructure.
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