EthonAI secures USD 6.8M in seed financing round

The capital increase enables EthonAI to bring its no-code quality management platform to a broader market to help clients boost performance by detecting, monitoring, and preventing quality losses at scale. The ETH Zurich spinoff already serves some of Switzerland’s leading companies, including Siemens, Roche, Lindt & Sprüngli.

Founded in 2021, EthonAI is dedicated to redefining how manufacturers approach quality management. The startup uses advanced AI methods to analyze and improve the quality of manufacturers' products and processes, thanks to enabling them to aggregate process and quality data into a single platform. Process engineers and quality managers can use the startup’s suite of software tools for vision-based defect detection, process monitoring, and root cause analysis without writing a single line of code. Manufacturers using the EthonAI platform have seen significant increases in productivity and over 50% reduction in quality losses.

One year after the company’s incorporation, EthonAI counts leading manufacturers, like Siemens, Roche, and Lindt & Sprüngli, among its customers. “At Siemens, we are always looking for ways to improve the efficiency of our manufacturing processes,” explains Alexander Dierolf, Head of the IT Value Center Data Intelligence & Automation at Siemens SI. “EthonAI’s tools impressed us because of their ease of use and scalability. We already use them in multiple Siemens factories in Europe and the US—and are currently scaling the EthonAI platform to other sites.”

The fresh funding will enable EthonAI to advance product development and expand its market base. Co-led by Earlybird Venture Capital and La Famiglia, the financing round also attracted existing investors, Wingman and Acequia Capital.

“We are thrilled to have the support of Earlybird and La Famiglia as we continue to grow and expand our product platform,” says EthonAI CEO Julian Senoner. “With this funding, we can bring our tools to more manufacturers globally and invest in developing new features such as simulation based on causal machine learning.”

(Press release/RAN)