AiCadGen

Getting from a verbal design intent to a CAD file has always required either years of software fluency or a consultant willing to bill hourly for sketch constraints. AiCadGen removes that gate: type a description — a planetary sun gear, a drone chassis, a bracket with specific hole patterns — and the system generates real parametric geometry in a sandbox, returning downloadable STEP, STL, DXF, OBJ, or PDF drawing files. The whole loop from description to first export is described by the vendor as taking under a minute.

The differentiating feature is chat-driven refinement on top of generated geometry. Instead of reopening a feature tree, you type ‘make the holes 8 mm’ or ‘move the kitchen wall 600 mm east’ and the model updates. For assemblies, the export includes a STEP assembly file, per-part files, and a mate-intent manifest — which means the output is structured for import into downstream CAD tools rather than a flat mesh that loses assembly context. Floor plan and MEP overlay generation works from a brief or from an uploaded PDF, photo, or scan, and the vendor states standards-aware drawing output targeting AS1100, ISO, and ASME.

AiCadGen fits early-stage design cycles: makers who need a printable part today, hardware founders generating prototype geometry before committing to a CAD seat, or mechanical engineers who want a parametric starting point to refine in their preferred tool. The wall appears when that downstream tool has its own history tree and expects associative geometry that wasn’t built there — imported STEP geometry is dumb solid at that point, and the parametric intent the AI generated does not survive the handoff. Teams doing tolerance stack-ups, FEA, or design-for-manufacturability validation inside an existing PLM workflow will exhaust what AiCadGen can do before the part reaches a drawing review.