The electrical grid is a complex system that requires real-time data analysis to optimize performance and predict potential issues. The author, David Swank, was working on a microgrid project when he realized the need for a common platform to connect various systems and technologies. This led him to create the ENX Energy Platform, which uses graph technology to model the grid from generation to consumption, enabling utilities to gather intelligence on their operations and make data-driven decisions. The platform is designed to be interoperable, allowing different devices and systems to communicate with each other seamlessly. It also includes features such as predictive analytics, machine learning, and real-time insights, which enable utilities to anticipate and mitigate potential issues, ultimately improving the efficiency and reliability of the grid. By visualizing and analyzing data across all levels of the grid, ENX aims to unlock new use cases for AI/ML, such as optimizing energy consumption in buildings and predicting power outages. The platform has already been successfully deployed with utilities like Oregon Trails Electric Cooperative, enabling them to better manage their grids and prevent potential issues.