Our SME aims to develop an AI-based solution to enhance energy efficiency in commercial buildings. Leveraging computer vision and predictive analytics, the project will create a system to analyze energy consumption patterns and suggest optimizations. By integrating with existing building management systems, this solution seeks to reduce energy waste, lower costs, and support sustainability goals.
Commercial building owners and facility managers seeking to reduce energy costs and improve sustainability.
Energy inefficiency in commercial buildings leads to high costs and increased carbon footprints. The critical need is for a scalable system that can provide real-time insights and automation to optimize energy usage.
Regulatory pressures and the competitive advantage of sustainability drive the market's readiness to invest in efficient, compliant energy solutions.
Failure to address energy inefficiencies will result in continued high operational costs, potential non-compliance with regulations, and a weakened brand image regarding sustainability.
Current alternatives include manual audits and generic energy management software, but these lack real-time analytics and integration with AI-driven insights.
Our solution's unique selling proposition is its integration of AI-driven real-time insights with existing BMS, providing actionable and automated energy optimizations unlike traditional static solutions.
We will employ a B2B go-to-market strategy, focusing on partnerships with BMS providers and energy consultants, as well as targeted digital campaigns to reach facility managers and commercial property owners.