Chamfer

The problem

Text-to-CAD demos often stop at visual plausibility. A model can produce convincing code, declare success, and still deliver the wrong dimensions, missing features, or invalid geometry. That gap matters when the output is supposed to become a physical object.

Chamfer treats verification as part of generation, not a final cosmetic check. The agent converts a request into a persistent plan, writes build123d CAD code or drives Autodesk Fusion, executes the work against a real geometry engine, and reconciles measured evidence with every active requirement before delivery.

Engineering the agent loop

The harness gives the model a focused tool surface instead of a general coding environment. Complex tasks begin with an evidence-backed plan. Focused documentation retrieval and progressive CAD skills provide domain knowledge only when the task needs it, while long-session compaction preserves requirements, the plan, and verification state.

For image-driven work, Chamfer maintains explicit reference coverage. A build cannot pass the final gate until the latest inspection views have been compared with every active reference. Deterministic checks handle facts the geometry kernel can prove—body count, dimensions, holes, and feature presence—while visual inspection handles intent that is genuinely visual.

Measured, not asserted

Agent changes are gated by an evaluation corpus and a geometry-kernel oracle. The comparison harness runs the same parts through different agents and records correctness, tool calls, context, output, cost, and wall time. This makes prompt or tool changes an engineering decision with a baseline, not a matter of taste.

The product remains local-first when run through npx chamfer: conversations, CAD execution, and geometry stay on the user’s machine, while the user chooses the model provider. Finished work can be inspected interactively and exported for downstream CAD or fabrication workflows.