London-based xWatts has raised £1.6M in a seed funding round led by Cambridge Enterprise Ventures, with participation from Cambridge Angels, Parkwalk, and R42.
The funding will support the expansion of xWatts’ AI-powered energy management platform across energy-intensive, non-domestic real estate assets in Europe.
Turning buildings into actively managed energy systems
xWatts develops machine-learning software that goes beyond energy monitoring, directly connecting to building infrastructure to autonomously manage energy generation and consumption. The platform models facilities in real time and optimises systems such as HVAC, solar installations, and combined heat and power units to reduce both costs and emissions.
The company targets large and complex properties where energy is a major operational expense, including healthcare facilities, universities, and industrial sites.
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Automating decarbonisation at scale
According to CEO and co-founder Yigit Akar, xWatts was built to move beyond static dashboards and manual interventions. The platform actively controls building energy systems, enabling intelligent, automated, and scalable decarbonisation across portfolios rather than individual assets.
The company says its technology is already live in healthcare, education, and manufacturing environments, delivering measurable reductions in energy usage, operating costs, and carbon output while improving visibility over building performance.
Focus on high-impact European sectors
With the new capital, the startup plans to accelerate product development and expand in key European markets. The company is prioritising sectors where energy optimisation delivers the fastest return, particularly healthcare, higher education, and industrial real estate.
About xWatts
xWatts is an AI-powered energy management platform for commercial and non-domestic buildings. By automating the control of heating, cooling, generation, and energy storage systems, the startup helps property owners cut costs, lower emissions, and operate buildings more efficiently with minimal manual input.
