> For the complete documentation index, see [llms.txt](https://docs.net2grid.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.net2grid.com/net2grid-energy-ai-edge-on-ami2/overview/how-energyai-edge-works.md). # How NET2GRID EnergyAI® Edge on AMI 2.0 works NET2GRID EnergyAI® Edge performs advanced energy analytics directly on supported smart meter platforms through embedded firmware. Instead of transmitting high-frequency measurement data to a cloud service for analysis, the solution executes optimized machine learning models and algorithms locally on the device, transforming raw meter measurements into actionable insights. Unlike traditional cloud-based analytics solutions, NET2GRID EnergyAI® Edge operates entirely on the smart meter and is designed to work out of the box. The solution does not require historical data from Meter Data Management (MDM) systems, cloud-based model training, prior knowledge of customer energy assets, or additional in-home communication infrastructure such as Wi-Fi. As a result, NET2GRID EnergyAI® Edge can be deployed rapidly across large meter populations without customer-specific configuration or historical data collection, enabling utilities to obtain actionable insights within one week of deployment. By performing inference locally, the platform significantly reduces communication requirements, as only compact application outputs and events are transmitted through the smart meter's existing communication infrastructure rather than continuous high-frequency measurement data. This edge-first approach reduces latency, enhances customer data privacy, minimizes dependency on cloud infrastructure, and enables timely delivery of actionable insights. Although each NET2GRID EnergyAI® Edge application delivers different analytics, they all follow the same execution model. The embedded firmware continuously processes available meter measurements, applies application-specific machine learning models and algorithms, and generates lightweight messages containing the resulting insights. This common processing framework enables multiple applications to operate efficiently on resource-constrained smart meter platforms while maintaining low computational and communication overhead. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.net2grid.com/net2grid-energy-ai-edge-on-ami2/overview/how-energyai-edge-works.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.