San Francisco-based startup Niobia is using AI to tackle a critical challenge in battery and semiconductor manufacturing: quickly identifying and understanding production defects that can cause costly delays. By deploying AI agents equipped with self-contained GPUs directly on manufacturing equipment, Niobia’s system detects even tiny flaws like pinholes and scratches on the production line, capturing data in real-time. This data is then accessible through a chatbot interface to help engineers diagnose and address problems within minutes instead of days, dramatically reducing downtime and expenses.

The company was founded by Gaurav Jha and Harveen Kaur, both PhD chemists with industry experience in chip and battery production, who saw the need to streamline defect analysis. They emphasize that while Niobia’s technology serves a niche market, it underpins a wide range of everyday products. The AI models they use are open source but trained on proprietary battery and chip data, allowing for sensitive information to remain secure without reliance on public cloud services. Customers manage their own encryption keys to ensure data privacy, addressing key cybersecurity concerns in this highly competitive sector.

Niobia currently focuses on three manufacturing specialties—film coating, electroplating, and fluidized bed reactions—as it aims to further automate the defect identification process. The startup’s broader vision is to enable fully autonomous factories that can rapidly troubleshoot and fix production issues independently. This capability is vital because chips and batteries are essential components in technology globally, and manufacturing setbacks can cost millions of dollars and hamper supply chains.

At TechCrunch Disrupt 2026, Niobia is competing as a Battlefield finalist, showcasing its AI-driven approach to revolutionizing quality control in high-stakes manufacturing environments. Their innovative system promises to help major battery and semiconductor manufacturers push forward new technologies more efficiently by minimizing costly errors and accelerating root-cause analysis timelines, offering a significant edge in a market where speed and precision are paramount.