AI Based Vision To Brings New Era of Edge Intelligence To Manufacturing
Elementary, a fast-growing startup that has built an AI machine vision platform for quality and inspection in manufacturing, has closed $30M in Series B funding to support customer demand. Following the new round of capital, the company is now poised to aggressively scale its solution worldwide with existing and new customers.
Given the current manufacturing market dynamics, the demand for Elementary’s platform has been overwhelming. As the world has shifted to requiring automated solutions to address labor constraints, Elementary’s customers have needed automated inspection solutions to keep pace with their quality goals. Unlike manual inspection, Elementary’s platform enables customers to inspect 100% of their produced goods and is capable of inspecting parts and assemblies that were previously impossible to inspect, in a highly repeatable and scalable way.
The $30M Series B investment was led by Tiger Global and joined by existing investors including Threshold Ventures, Fika Ventures, Fathom Capital, Riot VC, and Toyota Ventures.
“There are few trillion-dollar markets like manufacturing. Watching the execution level of the Elementary team to land and expand with global, Fortune 200 companies has been extremely exciting to be a part of. I’m thrilled for the company to add a partner like Tiger Global as the company scales to the next level.” said Mo Islam, an Elementary Board Director and Partner at Threshold Ventures.
“We are proud to partner with Elementary on the modernization of our quality inspection program. It is very exciting to see all of the actionable data that the system produces as it gives us great insight into the efficiency of our operations,” said Chris Rolenc, principal engineer at Milwaukee Tool.
Expanding Global Operations
The company plans to use the funding to expand operations internationally and continue to recruit top talent. During the process, Elementary has continued to add experienced executives to the team, such as Greg McEntyre, VP of Customer Implementation, and Krishna Gopalakrishnan, Sr. Director of Platform and Vision.
Greg McEntyre joins Elementary with more than 20 years of industry experience building, programming, and installing machine vision systems for quality. He started with Keyence before launching his own system integration organization, HNJ Solutions, which became known as the default brand for quality systems in Southern California.
Krishna Gopalakrishnan joins Elementary after a successful track record in the industry, including 10 years as the Principal Software Architect at Cognex, a traditional machine vision provider. After Cognex, Krishna continued to lead teams at Amazon Robotics in logistics and automation, and Realtime Robotics a fellow Toyota AI ventures portfolio company.
“The approach to machine vision being pioneered by Elementary is revolutionary,” said Krishna, “The confluence of cloud compute, highly secure IOT, and AI based vision brings about a new era of edge intelligence to manufacturing.”
No Code AI
Elementary’s no code approach allows customers to create inspection routines and train models by simply labeling data through an intuitive, user-friendly interface rather than requiring expensive programming and integration.
“Our no-code AI, data driven approach is resonating with the market and our customers are asking for more” said Arye Barnehama Founder and CEO of Elementary. “With over 250M inspections performed, we are confident that we can continue to deliver insights that not only help customers ship quality products, but also leverage the data to truly close the loop on quality.”
QA Labor Shortage
Manufacturing plants worldwide are struggling to keep up with labor shortages and quality assurance positions are among the most difficult to fill. More than 10% of all open roles in manufacturing are quality or inspection related, making it the 3rd hardest position to fill in the industry. Elementary believes it is well positioned to alleviate these challenges for their customers.
For more information: www.elementaryml.com