Our Approach
Structural design is a search problem
The bottleneck
Engineers spend most of a project not deciding what to build, but iterating toward a member sizing that is both code-compliant and efficient. Each iteration means re-running analysis, re-checking capacities, and adjusting by hand. The drawing is fast; the search is slow.
Why reinforcement learning
Sizing a lateral system is a sequential decision problem over a vast discrete space of sections. Reinforcement learning is built for exactly this: an agent that learns a policy for proposing sections, improving with every model it sees, and converging on efficient designs in minutes rather than weeks.
Why verification
A proposal is only useful if it holds up. Every design DatumBlocks produces is checked against NZS 3404 and validated with nonlinear analysis before it reaches you. The AI searches; the physics decides. Verified, not guessed.