The smart energy startup Verdigris announced today that it has raised 6.7 million to scale production of its Einstein smart sensor and frequency detectors. The sensors are used to predict the failure of machines and improve energy efficiency. Factories, manufacturing facilities, and other large buildings using Verdigris technology reduce energy use 8 to 22 percent, CEO Mark Chung told VentureBeat in a phone interview. The Einstein frequency detector from Verdigris made its debut in August. "Rather than take a big data approach where we study thousands of motors and this is the failure pattern, we instead take a physics based model which is looking at a signal through our sensors," Chung said.

Verdigris, a San Francisco, CA-based provider of an artificial intelligence and IoT platform for smart buildings, raised an additional 6.7m in Series A funding. The round was led by Jabil – which also plans to roll out Verdigris' AI energy sensor platform in a number of its largest manufacturing sites – with participation from Verizon Ventures, Stanford StartX Fund and existing angels. The company, which has raised 16m in total funding to date, plans to use the funds to scale manufacturing and customer operations. Led by co-founder and CEO Mark Chung, Verdigris is an artificial intelligence and IoT platform which combines proprietary hardware sensors, machine learning, and software to make buildings smarter and more connected while reducing energy consumption and costs. The software produces reports including energy forecasts, alerts about faulty equipment, maintenance reminders, and energy usage information for each and every device and appliance.