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Bucket Robotics is making defect detection faster, easier, and more deployable, starting with the 250M lbs of plastic wasted annually in U.S. manufacturing. Our platform turns CAD files into defect detectors. We generate synthetic, photorealistic training data to help factories catch flaws before they ship. No manual labeling, no real defects required. Our models deploy to edge hardware and integrate easily into existing automation stacks. We come from the self-driving world (Argo AI, Uber ATG, Stack AV), where we built reliable perception in high-noise, real-world environments. We're applying that experience to manufacturing: robust sensing, user-friendly interfaces, and fast iteration cycles. Manufacturers hesitate to adopt new sensing due to data concerns, integration risk, and poor UX. We’ve handled petabytes of regulated autonomy data, and built systems that earn trust. Legacy vendors like Keyence and FLIR offer hardware-centric tools with bloated pricing and outdated software. We’re building the opposite: flexible, modern tools that engineers want to use. Manufacturing is in the middle of a $700B automation wave across North America. Bucket Robotics is building the quality control infrastructure to match.

Poka Labs is developing an AI platform to automate operations tasks in chemical manufacturing, beginning with production scheduling. Traditionally, these tasks are performed manually using spreadsheets. Our software seamlessly integrates with existing data sources such as data historians, emails, and PDFs to automate analytics, scheduling, changes, and communication within a single platform. Malay and Andrew met while pursuing their MBAs at Harvard Business School. Malay previously worked as an engineer in the specialty chemical industry, saving his employers over $100 million across US, China, and German plants. Andrew worked as a software engineer on data infrastructure at Meta and optimization problems at a seed-stage startup. He also completed a Masters degree at Harvard specializing in Machine Learning.