As factories and manufacturing facilities have gotten \u201csmarter<\/a>\u201d through sensors, robotics<\/a> 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.<\/p>\n 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\u2019t fully made its way into the manufacturing realm. This has created an untapped goldmine of insights, and more recently a nascent market for technologies designed both to capture and make sense of a vast array of manufacturing data.<\/p>\n Last month, U.K.-founded Oden Technologies<\/a>, now based in New York, raised a $28.5 million Series B round<\/a> to spur growth for its data analytics platform for manufacturers. Germany\u2019s Daedalus raised $21 million<\/a> to apply AI to precision-manufacturing factories. And Belgium\u2019s Robovision secured $42 million to bring computer vision<\/a> intelligence to industrial machinery.<\/p>\n Now it\u2019s EthonAI\u2019s<\/a> turn, as the Swiss startup announced<\/a> Thursday that it has raised CHF 15 million ($16.5 million) in a Series A round of funding led by Index Ventures<\/a>, with participation from General Catalyst, Earlybird and Founderful.<\/p>\n 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\u2019s Inspector<\/a> software can then identify surface defects in the products during the manufacturing and assembly process. Apple recently acquired a company called DarwinAI<\/a> that serves a similar purpose, in terms of automating the visual quality management process in component manufacturing.<\/p>\n More broadly, though, EthonAI can combine data from across a company\u2019s manufacturing setup, from sensors to line stops<\/a>, and build a picture of where things are and aren\u2019t working well \u2014 and even compare performance across multiple facilities to see where there might be room for improvement.<\/p>\n In its three-year history, EthonAI has amassed some fairly high-profile customers including Siemens and chocolate-maker Lindt.<\/p>\n Digging down into EthonAI\u2019s target markets reveals that semiconductor manufacturing is one particular area of focus, though the company hasn\u2019t divulged any specific customers in this space. However, low yield<\/a> is a known concern in the chip sector, where defects in the silicon wafers<\/a> 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<\/a> (just 55% at the time), with Apple striking a deal<\/a> to pay only for known good wafers \u2014 saving billions<\/a> of dollars in the process.<\/p>\n EthonAI, for its part, says it works with a \u201cleading semiconductor producer\u201d that uses its platform to merge multiple datasets to conduct analysis and spot previously unknown relationships between processes, equipment and yield rates.<\/p>\n \u201cManufacturing is at a critical juncture, and companies that fail to adapt with AI risk falling behind,\u201d Senoner said in a press release. \u201cFactories are producing mountains of data and AI is the key to unlocking insights to drive operational excellence.\u201d <\/p>\n<\/div>\n![\"EthonAI](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2024\/05\/Ethon_AI_-_Julian_Senoner_left_.width-1600.format-webp-e1717080997982.webp?w=680\")
EthonAI finds the defects in products<\/h2>\n