MISFITS OFMANUFACTURING

Computational Design workflows are powerful and elegant so long as you stay in one self-contained system. But the moment you push a parametric workflow into actual production — CNC, robotics, 3D printing, laser welding — you're on your own. Bridging file formats, fighting legacy CAD, feeding ERP and PDM systems, writing custom interfaces, duct-taping together systems that were never meant to be connected.

And even when everything works, the tools stay locked to whoever built them. Colleagues can't touch your Grasshopper definitions without a steep learning curve, so the expert becomes the bottleneck. Rolling these workflows out via UIs and RhinoCompute sounds great — until you hit GDPR, IP protection, server setup and user management. More glue, more problems.

AI is changing what's possible in this pipeline — generative design for manufacturing constraints, synthetic training data for robotics workflows, automated code generation from design intent. But the moment you involve models, questions surface: how do you maintain design intent across the pipeline? How do you ensure your proprietary geometry, tolerances, and process parameters stay protected and aren't leaking into training sets or competing against you?

If this sounds familiar, you're probably alone or in a very small team. A misfit — an oddball, admired or more likely despised for their strange spaghetti tools. The knowledge stays siloed, the problems stay unsolved, everyone reinvents the same workarounds. Meanwhile, someone in a completely different industry has maybe already solved the problem you're stuck on. Architecture, mechanical engineering, product design — different fields, same tools, same pain.

Cross-industry exchange doesn't happen by itself — so we're making it happen.