ai-for-hardware
(4)
Enzzo
Enzzo is a Seattle-based AI platform that helps hardware product teams move from idea to investment-ready in roughly half the time. Spun out of startup studio Pioneer Square Labs and led by serial founder Ford Davidson, Enzzo uses generative AI and customer-supplied data to draft product definitions, requirements, user personas, competitive analysis, risk assessments, concept images, and bill-of-materials pricing - the slow, document-heavy front end of building physical products. It raised a $3M seed round in March 2024 and counts electronics and instrumentation makers among its early paying customers.
JITX
JITX is a Berkeley- and San Jose-based software company building a code-driven, AI-assisted platform for circuit board design. Instead of dragging parts around a canvas by hand, electrical engineers describe what a board must do in code, and JITX selects components, places parts, routes traces, and checks the design against electrical, mechanical, and manufacturing rules. Spun out of UC Berkeley and backed by Y Combinator and Sequoia Capital, the company is used by hardware teams at firms like Honeywell Aerospace, Lockheed Martin, Northrop Grumman, OpenAI, and HP, who report 5-20x productivity gains on complex multi-layer designs.
Duncan Haldane
Duncan Haldane is the co-founder and CEO of JITX, a Berkeley-born startup that lets electrical engineers design circuit boards by writing code instead of dragging lines on a screen. Before software, he built robots: at UC Berkeley he created two Guinness World Record machines - the X2-VelociRoACH, the fastest robot relative to its size, and Salto, the most vertically agile jumping robot ever built. He holds a PhD in mechanical engineering from Berkeley and started JITX in 2017 because he wanted a faster way to design the hardware inside his robots.
Arena Physica
Arena Physica is a New York-based AI company building 'electromagnetic superintelligence' - AI systems grounded in applied physics that accelerate the design, testing, and optimization of advanced hardware. Its Atlas platform and Heaviside foundation model predict electromagnetic behavior from geometry in milliseconds, claiming roughly 800,000x faster results than commercial solvers, and are used by hardware leaders across semiconductors, aerospace, automotive, medical devices, and defense.