AI for Hardware Development

About this session

Hardware development across every industry — robotics, automotive, silicon, datacenters, and aerospace — remains constrained by multi-week supplier cycles required to obtain manufacturability feedback and cost estimates, and these cycles represent the single largest lever on program timelines. This talk examines what becomes possible when a multimodal AI agent is given direct access to a 3D CAD file — its feature tree, drawings, tolerances, and rendered views — and tasked with reasoning about how a part should be manufactured and what it should cost. I will present a working system built on Claude Code CLI, custom agents, and MCP servers that bridges directly into CAD, fusing symbolic geometric data with rendered visual context to reason about geometry, tolerances, and manufacturability in a manner comparable to a seasoned engineer. The talk will cover the underlying architecture, the evaluation framework used to benchmark model outputs against real engineering ground truth, and results from production programs in which multi-week feedback loops have been reduced to minutes. Attendees will leave with a clear technical view of how multimodal agents can operate on real engineering artifacts and a practical framework for applying these methods within their own hardware domain.