The home page covers what we build. Here we cover the people behind it, and why we chose a modular middleware and Agentic AI — instead of another heavy EMS.
Origin story — the industry gap we saw, how the first site came to be, and why we chose SME energy management as our entry point. Could include a founder's note, a timeline, or a key turning point.
Name · title · background. Ideally one person each across tech / energy / business, with a line on their expertise.
Name · title · background. Ideally one person each across tech / energy / business, with a line on their expertise.
Name · title · background. Ideally one person each across tech / energy / business, with a line on their expertise.
Energy device generations are mixed and protocols disagree. A heavy EMS tries to unify by buying hardware, signing long contracts, and replacing existing investments. We build an asset-light middleware between devices and decisions — bridging new and legacy equipment and unifying data formats without replacing what you already own. Let data flow reliably first, then talk decisions.
Traditional rule engines and MPC already handle multi-objective optimization across tariff, load, battery life, and contracted capacity — we don't claim credit for that layer. The LLM Agent adds value where the rule table stops: strategy adaptation when tariffs change, anomaly response to device alerts, cross-source reasoning outside the rules, and explaining decisions in natural language. Energy management moves from “monitoring” to “decides and explains.”
SMEs have long been shut out of energy management — heavy assets, hard integration, no AI capability. We believe Energy AI shouldn't be a large-enterprise privilege. An asset-light middleware plus on-demand Agents lets equipped SME sites afford AI decisions too.
We're not building another EMS. We're building the middleware that lets equipped SME sites connect to AI decisions — forecast, dispatch, diagnosis, O&M, and reporting forming a closed loop: energy data in, AI decisions out. Numerical optimization at the core, with LLMs handling strategy adaptation, anomaly response, and explainable decisions.