Niobia<\/a> is betting big on a critical but often overlooked part of battery and chip making: helping engineers understand how and why things go wrong. Its AI software flags production defects and lets engineers ask a chatbot what caused them, aiming to deliver answers in minutes rather than days.<\/p>\nHow it works (broadly): The company\u2019s customers place AI agents on their production lines to look for defects, then loop in human engineers to work out what might cause a product to fail. The agents run on self-contained GPUs mounted directly on manufacturing equipment, much like a sensor. They can spot pinhole-sized flaws such as scratches. Niobia\u2019s AI also gathers all the data in one place so engineers can ask a chatbot what went wrong and dig deeper into the cause.<\/p>\n
Speed is so critical because chips and batteries are some of the world\u2019s most important products, while also increasingly in short supply. As manufacturers continue to develop and improve their technologies, things inevitably go awry during research and development. Engineers then spend days or longer figuring out a fix before they can restart production. Each flaw or mistake can prove costly \u2014 sometimes running into the millions of dollars \u2014 and set back manufacturing timelines. <\/p>\n
Niobia co-founders Gaurav Jha and Harveen Kaur both have PhDs in chemistry and have worked at chipmakers and battery manufacturers, giving them firsthand experience with the challenges within the industry. In a call ahead of TechCrunch Disrupt 2026, where the company is a Battlefield finalist, Jha explained that while what Niobia does is specialized, it serves a multibillion-dollar market that includes some of the biggest battery and semiconductor makers today.<\/p>\n
\u201cIt\u2019s extremely niche; maybe people will never know about it,\u201d said Jha, speaking of the battery and chip manufacturing market that his company aims to serve. \u201cBut it\u2019s like behind the scenes, this is powering everything\u2026 we just don\u2019t see it in the regular world.\u201d\u00a0<\/p>\n
\u201cEvery product around you, like glasses or shoes, slippers, wallets, and headphones \u2014 these processes touch every single part of it,\u201d added Jha.<\/p>\n
Niobia\u2019s agents run on open source models that the company trains further with battery and chip research and other data. That training is what lets them identify pinhole-sized defects. Jha said that Niobia chose open source models largely because its customers don\u2019t want their highly sensitive company information going into a public AI cloud where it might be used to train the model.<\/p>\n
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