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AI manufacturing startup funding is on a tear as Switzerland’s EthonAI raises $16.5M

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EthonAI co-founders Julian Senoner (CEO, right) and Bernhard Kratzwald (CTO, left) at a Siemens factory in Zug, Switzerland
Image Credits: EthonAI / co-founders Julian Senoner (CEO, right) and Bernhard Kratzwald (CTO, left) at a Siemens factory in Zug, Switzerland

As factories and manufacturing facilities have gotten “smarter” through sensors, robotics and other connected technologies, this has created a potential treasure trove of data that can be mined for insights on bottlenecks and other areas for improvement. Or maybe even to just expedite processes that would otherwise require significant manual spadework.

But much of this generated data is unstructured and not easy to harness off the bat. While big data analytics has for years been a mainstay of industries such as finance and logistics, it hasn’t fully made its way into the manufacturing realm. This has created an untapped gold mine of insights, and more recently a nascent market for technologies designed both to capture and make sense of a vast array of manufacturing data.

Last month, U.K.-founded Oden Technologies, now based in New York, raised a $28.5 million Series B round to spur growth for its data analytics platform for manufacturers. Germany’s Daedalus raised $21 million to apply AI to precision-manufacturing factories. And Belgium’s Robovision secured $42 million to bring computer vision intelligence to industrial machinery.

Now it’s EthonAI’s turn, as the Swiss startup announced Thursday that it has raised CHF 15 million ($16.5 million) in a Series A round of funding led by Index Ventures, with participation from General Catalyst, Earlybird and Founderful.

EthonAI co-founders Julian Senoner (CEO, left) and Bernhard Kratzwald (CTO, right) at a Siemens factory in Zug, Switzerland
EthonAI co-founders Julian Senoner (CEO, left) and Bernhard Kratzwald (CTO) at a Siemens factory in Zug, Switzerland. Image Credits: EthonAI
Image Credits: EthonAI

EthonAI finds the defects in products

Founded out of Zurich in 2021 by CEO Julian Senoner and CTO Bernhard Kratzwald, EthonAI can train AI models for specific use cases, for instance in electronics manufacturing where the customer supplies imagery of defect-free products and EthonAI’s Inspector software can then identify surface defects in the products during the manufacturing and assembly process. Apple recently acquired a company called DarwinAI that serves a similar purpose, in terms of automating the visual quality management process in component manufacturing.

More broadly, though, EthonAI can combine data from across a company’s manufacturing setup, from sensors to line stops, and build a picture of where things are and aren’t working well — and even compare performance across multiple facilities to see where there might be room for improvement.

In its three-year history, EthonAI has amassed some fairly high-profile customers including Siemens and chocolate-maker Lindt.

Digging down into EthonAI’s target markets reveals that semiconductor manufacturing is one particular area of focus, though the company hasn’t divulged any specific customers in this space. However, low yield is a known concern in the chip sector, where defects in the silicon wafers can affect the number of actual usable chips post-production. Notably, Apple reportedly reached an agreement last year with chipmaker TSMC that apparently had particularly low yield rates (just 55% at the time), with Apple striking a deal to pay only for known good wafers — saving billions of dollars in the process.

EthonAI, for its part, says it works with a “leading semiconductor producer” that uses its platform to merge multiple datasets to conduct analysis and spot previously unknown relationships between processes, equipment and yield rates.

“Manufacturing is at a critical juncture, and companies that fail to adapt with AI risk falling behind,” Senoner said in a press release. “Factories are producing mountains of data and AI is the key to unlocking insights to drive operational excellence.”

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