Company Positions Itself in the Epicenter of Automotive Innovation
WekaIO, the innovation leader in high-performance, scalable file storage for data intensive applications, announced the opening of a new office in Detroit, Mich., along with the appointments of Mike Guentner as Regional Sales Manager, and Tony Raleigh as Senior Systems Engineer. This new presence and key appointments in the Midwest region are integral to the growth of the company’s automotive industry presence.
“Detroit is the heart of the US automotive industry and therefore a critical territory for us as we execute our strategy to increase our dominance in this key market,” said Richard Dyke, VP of sales at WekaIO. “We are proud to cut the ribbon on our new regional office and to welcome Mike and Tony, two seasoned players in the IT and automotive fields, to the team. Their expertise is a valuable addition to our operations and will help advance our plans for this region and industry.”
As Regional Sales Manager, Guentner brings deep sales and storage expertise to his role, having honed his talents at Compellent, Avere Systems, Qumulo and most recently at Pure Storage. As Senior Systems Engineer, Tony Raleigh in turn brings a strong background in systems engineering gleaned as Systems Engineer at Qumulo, NetApp, and from more than a decade’s experience as a consultant focused on engineering systems applications for automotive suppliers.
In the automotive industry, Artificial Intelligence (AI) is a key enabler in the advancement of not just the (obvious) use case of autonomous vehicles, but also of increasingly advanced driver assistance systems. So much so, that by 2024, the Automotive Artificial Intelligence Market is expected to exceed more than US$ 10.73 Billion at a CAGR of 37.5% in that forecast period. WekaIO’s Matrix parallel file system is a favorite of innovators in the AI space and is lauded as the premier file system for pioneers in the autonomous driving space; the company was recently recognized with an Alconic Award from AI Business for Best Innovation in Deep Learning for precisely this use case.