Most teardowns end in a landfill. ML Systems models your home, runs a reverse takeoff of what is actually in it, and lets lenders bid to fund the rebuild. Deconstruct instead of demolish, then rebuild larger from the recovered material.
I believed AI and Machine Learning was moving too fast for the human consciousness so I LLC'ed ML Systems.
I now am attempting to rebuild the American Dream of homeownership by rebuilding the existing homes.
I run a construction company in Rhode Island, and I built this because the industry has a hole in it.
There is software for buying a home, selling it, renovating it, insuring it and financing it. There is almost nothing for the moment a house comes down, which is exactly when most of its material value gets destroyed. A machine flattens the structure, and old-growth framing, brick, fixtures and hardware go to a landfill. Rhode Island's Central Landfill is projected to hit capacity around 2046.
So the app starts there. You enter an address, it builds a model of the house, and it runs a reverse takeoff: what is actually in this structure, and what is it worth. From there it tracks the whole loop in one place - financing through deconstruction, design and construction - with one auditable record per home.
Two things I want to be straight about, because I would rather you trust the numbers than be impressed by them.
The software is shipped and real. The construction loop is modeled and has not been run yet. I am about to run the first cycle on my own property. Every claim in our public repo is labeled MEASURED, MODELED or ASPIRATIONAL for exactly that reason.
The concepts are all open at github.com/MLSystemsRI/ml-systems-public - the ledger design, the ontology, how we compress a house into a canonical model. Happy to go deep on any of it.