Proptech firm BrainBox AI, which focuses on decreasing energy costs and reducing carbon footprints in commercial properties, announced that it had raised $20 million, with $10 million coming from the Government of Québec as lead investor and also money from ABB, a company focused on electrification and automation in a number of different industries.
Up until mid-February 2023, the total the company received, including a seed round, two Series A rounds, and a grant, was $45.1 million, according to Crunchbase. The new investments bring the total to $65.1 million.
BrainBox expects to use the new funds to further develop its decarbonization technology and global commercialization plans.
“The Québec Government’s $10 million USD commitment, granted by Investissement Québec, signals the need for low CAPEX, high impact, decarbonization and energy efficiency solutions like BrainBox AI’s,” the press release said. “This investment further emphasizes the Ministry of Economy, Innovation and Energy’s unwavering support for technologies that contribute to the fight against climate change.”
In addition, BrainBox is acquiring the Multi-Site Retail Energy Management System business from ABB and will integrate it into existing operations, including the incoming New Hampshire-based team and 12,000 retail locations. “Coupled with BrainBox AI’s industry-leading AI optimization technology, the combined offering will help retailers reduce their greenhouse gas (GHG) emissions and utility costs without replacing their existing environmental management system (EMS),” the company said.
Last year, BrainBox announced that it had its first installation in New York City. In information it sent to GlobeSt.com at the time, the company claimed that a building operator can gain up to 25% in total energy cost savings within three months, a 20% to 40% reduction in carbon footprint, and a 50% improvement in HVAC-related operations and maintenance costs. The company also said then that it had gained installations in 100 million square feet of commercial real estate across nearly 20 countries.
The approach uses Internet-connected control equipment, machine learning (where software uses data feedback to improve its operations), and third-party data like weather and occupancy.
The software doesn’t replace maintenance and HVAC staff in buildings. Instead, it uses data to make multiple changes in buildings per hour and anticipates what conditions need to be in the immediate future.
It takes about three months for the company to create an initial model for a building. Then machine learning algorithms adjust in response to building-specific data, with the system ultimately providing control of space temperature two to three hours in advance of use.
