AI systems for the built environment
Same method.
Different medium.
A puzzle-solver who maps known vs unknown, finds leverage points, tests with real variables, and wires AI into the gaps.
See the workBackground
Twenty years building things that have to work.
Industrial designer by trade. Twenty years in joinery fabrication and commercial fit-out - reception desks for Okta, lobby joinery for BNP Paribas at 60 Castlereagh, bespoke furniture for Koskela. Work that goes from drawing to CNC to site, with tolerances that don't forgive mistakes.
The method was always the same: map what is known, identify what is not, find where the leverage is, test with real constraints, and build the thing. Repeat until it works.
That method transfers. The problems in built environment firms - tribal knowledge locked in people's heads, onboarding that takes six months, repetitive comms overhead that nobody has time to fix - are the same class of problem. AI is the medium. The thinking is the same.
Work
Three things that show the method.
The feature ceiling at St Paul's University - geometric timber panels, triangulated facets, perforated sections installed across a double-height atrium. Then the BNP Paribas Centre lobby at 60 Castlereagh Street - timber ring ceiling, wall cladding, all joinery throughout. Both came from an architect's design. The job was to figure out how to build them.
That means taking a set of drawings and working out how every panel gets made, how it gets supported, how it goes up in sequence, and how it meets the tolerances that the finished space demands. Structure, fabrication constraints, site conditions - all at once.
That gap between a design that looks right and a building that holds together is where the real work happens. Twenty years of commercial projects across hospitality, healthcare, and corporate fit-out. Same problem every time: take what exists on paper and make it real.
ASOR (A State of Ride) is a performance indoor cycling studio in Sydney. The production rig - DMX lighting, show file control, live stream output - was built from scratch and has run reliably across every live session since.
It works because every decision got made once, tested against real constraints, and locked in. The lighting cues, the sequencing logic, the show file structure - all of it lives in the person who built it.
That is the next problem. A system that only runs when the right person is in the room is not really a system. Extracting that embedded knowledge - the decisions, the sequencing, the cue logic - into something another operator can pick up and run is exactly the class of problem AI is well suited to solve.
The production rig is the proof the method works. The packaging of it is what comes next.
A tool for the built environment currently in development. Details to follow.
Writing
Thinking out loud.
AI doesn't close the gap. It widens it.
Everyone walks into the AI conversation with the same assumption: this is the great equaliser. That is not what I am seeing.
89 commits for a chili plant.
All I wanted was a reminder not to let my chilis die over winter. 89 commits later I have a full stack web app with shared gardens, seasonal planting guides, and a cohort who uses it more than I do.
I don't know what I know.
Someone told the Mrs and I that we are not normal. That we do not use AI like normal people do. I have been turning that over ever since.