Using a multi-tool pipeline worked great to connect tech data with my design concept. Claude gave me the initial climate breakdown, and Leonardo successfully generated the exact architectural features I needed—like the elevated floor and vertical fins—without losing the bold Alexander Calder art style I wanted.
Relying on just one AI model failed to catch local weather realities. Claude's initial design focused entirely on the sun but completely missed how intense the monsoon rain is in Selangor. If I hadn't double-checked it, the building would have ended up with flooded roof vents and a rusted red steel frame.
Perplexity AI was the most useful because it acted like a real local juror. It cross-checked Claude's ideas against real Malaysian data and warned me about the 26.6° angle of driving rain and the flash-flood risks in the Kajang valley. It completely saved my technical details.
Human judgment was needed when the AI got stuck on conflicting priorities. The software couldn't figure out how to block the west afternoon sun without completely blocking the panoramic valley views. I had to step in and make the executive design decision to use oblique, vertical red steel fins with a 40% perforation ratio to get the best of both worlds.
It sped up the entire process by compressing weeks of climate research and 3D modeling into quick, organized steps. I went from raw data to technical details—like the 20mm fin standoffs—and final presentation renders in a fraction of the time. Canva AI then helped me instantly build a slide layout using colors straight from my images.
The biggest risk is blind trust. AI is great at making things look beautiful and conceptually cool, but it doesn't actually know if a building will leak or rust in real life. If we don't question the outputs and double-check them against local constraints, we'll end up with unbuildable designs.