Generative design that ships starts with constraints.

Generative design has a portfolio problem: the internet is full of organic lattice sculptures that won 3D-printing beauty contests and never touched a production line. The technology isn't the issue. The workflow is. Generation without encoded constraints produces beautiful answers to the wrong question — geometry that ignores the mould, the budget, the assembly line and the service technician.

Constraints first

The real work happens before any geometry is generated: encoding the package envelope, the load cases, the manufacturing process with its actual rules — minimum wall, draft, tool access — the cost model, the interfaces that cannot move. That encoding is engineering judgement made executable, and it's where most of the value lives. Get it right and the search space contains only buildable answers. Get it wrong and you've automated the production of impossibilities. We put the constraint model in front of the client's engineers for sign-off before the first run; it's their knowledge, formalised.

Search wide, verify hard

With constraints encoded, machines do what humans can't: explore thousands of architecture and geometry variants without fatigue or attachment. But breadth is only half the loop — every promising candidate goes through simulation against the physics and a manufacturability check before a human ever sees it. What reaches the review isn't "what the algorithm made"; it's the verified best of a space no manual process could have covered.

Producible or it doesn't count

Our rule for generative work is blunt: if it can't be made by the process you'll actually run, it isn't a result — it's concept art. Optimised components are re-detailed for the real process with our engineering practice, and the measured delta — mass, stiffness, thermal margin — is reported against the baseline honestly, estimates labelled as estimates.

People decide

Nothing ships because an algorithm scored it highest. The final call weighs things no objective function holds — brand, service reality, assembly ergonomics, risk appetite — and belongs to people. The machines widen the search; the judgement stays human. That division of labour is what "AI-augmented design" means when it's a practice rather than a press release.