IIoT Startup, Petasense uses machine learning to predict equipment health
Industrial IoT startup, Petasense was selected by leading venture capital firms, Data Collective and Emergence Capital, as one of the ten most disruptive machine learning startups out of over 350 contenders. The process was highly competitive with a selection rate of under 3%. This puts Petasense amongst the most promising startups implementing machine learning to solve an important real world problem.
The investors present at the event included Matt Ocko from Data Collective, Santi Subotovsky from Emergence Capital, Frank Chen from Andreessen Horowitz, Jerry Chen from Greylock Partners, Dave Munichiello from GV, Mike Abbott from Kleiner Perkins, Vanessa Larco from NEA, and Bill Coughran from Sequoia Capital.
"We are thrilled to have been selected as a Top-10 company to participate in Google's Cloud Machine Learning event," said Abhinav Khushraj, co-founder, and CEO of Petasense. "It's a testament to our core machine learning technology and the huge market opportunity before us. Petasense employs advanced machine learning algorithms to monitor, analyze and predict the health of critical industrial assets in the cloud."
Petasense recently emerged from stealth with one million sensor measurements comprising of 18 billion wireless vibration readings, and an investment led by True Ventures, with participation from Felicis Ventures and top angel investors.
Founded with a vision of making industrial machines smarter, Petasense is a venture-backed Industrial IoT startup based in Silicon Valley. The company offers an end-to-end solution - comprised of a patent-pending wireless vibration sensor, cloud software and machine learning analytics - that helps with asset reliability and predictive maintenance. Customers benefit by reducing unplanned downtime and eliminating unnecessary repair costs. Petasense is backed by True Ventures, Felicis Ventures, and top angel investors. Its customers include industry leaders like JLL, C&W Services, Silicon Valley Power and Stanford University. To learn more about Petasense, visit http://www.petasense.com.