The problem
An elevator is a simple machine with a surprisingly complex social life. Four different groups interact with it daily — the building buyer who needs a business case, the rider who just wants to get to their floor, the maintenance technician tracking wear, and the operations team managing the whole building. Each group sees a different system. None sees the whole.
The result is predictable: buyer decisions based on incomplete cost models, riders left without useful information, maintenance that reacts rather than prevents, operations teams flying blind on energy and usage patterns.
What it does
Elevator Intelligence is a real-time digital twin that maps a single elevator shaft into four simultaneous views — one per stakeholder. Each view pulls from the same live data stream but renders it through a different lens.
The buyer view shows total cost of ownership, energy spend, and projected maintenance load against lifecycle benchmarks. The rider view shows current floor, expected wait, and building occupancy patterns. The maintenance view surfaces wear indicators, service history, and predicted failure windows. The operations view aggregates usage density, peak hours, and energy consumption across the building's vertical circulation.
Why four views from one twin
A single simulation guarantees consistency. All four groups are looking at the same physical reality — not four separate datasets that drift apart and contradict each other in a meeting.
The buyer can hand the rider view directly to building residents. The maintenance prediction feeds the operations scheduling calendar. The system is one, even when the interfaces diverge.
Context
Built as a proof of concept for the Kontai method — demonstrating that operational intelligence can be made legible across roles without building four separate systems. The live demo runs in the browser with simulated real-time data.