Feasibility intelligence
Compare yield, height envelope, setbacks, access, and mixed-use program assumptions before a project direction hardens.
AI for early architectural decisions
Design evidence before detailed modeling.
Architecture AI helps design teams compare site strategy, massing, feasibility, program test-fit, and climate-aware concept options before committing weeks of modeling time.
A research platform for the uncertain first half of design.
The system is built around architectural evidence: areas, setbacks, adjacencies, exposure, context, and reviewable assumptions.
Concept option reasoning
Generate and review option families with traceable design logic, not isolated images without context.
Climate-aware review
Surface early signals for daylight, shadow, envelope exposure, wind-sensitive edges, and outdoor comfort risks.
Program test-fit
Reason through cores, circulation, amenity ratios, unit mixes, workplace density, and lobby placement at concept scale.
Design narrative
Convert selected options into review notes, comparison tables, diagram prompts, and client-facing design rationale.
Human-in-the-loop review
Keep authorship, judgment, code interpretation, and consultant validation with the professional design team.
Example study outputs that a real design team can review.
These schematic diagrams represent the type of early evidence the platform is designed to organize: massing, climate, and test-fit.
Massing comparison
Early form studies track area yield, view corridors, height distribution, and street-wall pressure.
Climate-aware review
Concept checks flag overshadowing, exposure, wind corridors, and outdoor comfort before detailed simulation.
Program test-fit
Layout reasoning keeps adjacency, vertical circulation, net area, and service logic visible during early planning.
Workflow
From rough brief to reviewable option set.
Architecture AI is designed for the messy stage where constraints are incomplete, teams disagree, and every design move still has consequences for area, cost, performance, and identity.
01
Structure the brief
Collect site facts, planning assumptions, target area, program mix, and design intent.
02
Generate option families
Create comparable massing, layout, and facade directions with traceable assumptions.
03
Evaluate design evidence
Review yield, daylight, access, contextual fit, circulation, and unresolved risk.
04
Prepare decisions
Package diagrams, comparison notes, and next-step questions for architects and stakeholders.
Experimental track
Advanced prototypes are tested in Architecture AI X Lab.
The X Lab environment is used for private alpha features such as multi-option reasoning, geometry-aware prompting, and experimental review workflows before they become stable platform capabilities.