← Practical AI for the built environment
Computational Design
Rules, parameters, constraints — repeatable intent.
Not form-finding. Not style. A way to make intent legible and testable so you can iterate without losing control.
Mythologies → mechanisms
Turn fuzzy design beliefs into testable systems: constraints, scripts, and repeatable outputs.
Inputs
Sliders, datasets, tolerances, target metrics.
- Named parameters
- Units + ranges
- Defaults
Logic
Grasshopper graphs + Python nodes that compute deterministically.
- Readable functions
- Small composable parts
- Versioned assumptions
Outputs
Geometry, drawings, schedules — not just images.
- Layered linework
- Quantities
- Exportable formats
Checks
Constraint validation so the model stays honest under iteration.
- Edge cases
- Clashes
- “Does it still satisfy intent?”
Headed workflow (non‑negotiable)
AI can assist drafting, debugging, and documentation — but production stays verifiable inside Rhino/Revit.
Where AI helps
- Structure intent into parameters + constraints
- Generate/debug code
- Write documentation and checklists
Where verification happens
- Rhino/Grasshopper or Revit/Dynamo
- Deterministic scripts
- Human review pass + standards
Projects that prove the method
Selected work where rules, parameters, and constraints became repeatable systems — with evidence in geometry, drawings, and scripts.