Anvaya EnerTech offers the Neev AI Energy Operator, a system that uses clamp‑on Wi‑Fi sensors and machine‑learning to continuously monitor and disaggregate building electrical loads. It autonomously adjusts HVAC, lighting and major equipment in real time, delivering 10–30% electricity savings while providing monthly performance dashboards and carbon‑footprint reports for hotel, commercial, industrial and residential property managers.
Funding
Funding not disclosed
Founders
Product
Problem
Building owners and operators often lack real-time visibility into energy consumption and rely on manual adjustments, leading to significant electricity waste and higher operating costs across hotels, commercial, industrial, and residential properties.
Solution
Anvaya EnerTech’s Neev AI Energy Operator continuously monitors building electrical loads using non‑invasive clamp‑on sensors and applies machine‑learning algorithms to disaggregate demand, detect anomalies, and autonomously adjust HVAC, lighting, and major equipment. The system operates over existing wiring with Wi‑Fi connectivity, requiring no rewiring or downtime. Real‑time optimization actions reduce electricity use by 10–30% while preserving occupant comfort. Monthly performance reports quantify savings and carbon‑emission reductions, enabling owners to track financial and environmental impact without ongoing manual intervention.
Target Audience
Primary customers are hotel operators, commercial property managers, industrial facility owners, and residential building managers seeking automated energy cost reductions.
Features
- Clamp‑on, Wi‑Fi enabled sensors that attach to existing circuits without disrupting operations
- Continuous load disaggregation and pattern recognition to identify wasteful consumption
- Autonomous real‑time control of HVAC, lighting, and major loads based on learned demand profiles
- Anomaly detection and automated corrective actions to prevent energy spikes
- Monthly savings dashboards and carbon‑footprint reports for transparent performance tracking