Maintenance copilot
Most maintenance planning reacts to what already broke. Predictive models can flag it months out instead, and that's the AI layer I'm adding on top of the MJOP work I already do.
The problem
Most housing corporations and property teams still plan maintenance from a fixed multi-year schedule, updated by hand and revised once a year if at all. By the time a roof, an installation, or a facade actually needs attention, the plan is already out of date, and the person managing it usually hears about it from a complaint, not a forecast.
How it works
The starting point is the same MJOP work I already do as a day job: structured, verified condition data on every building element. A predictive layer sits on top of that, trained to flag which elements are drifting toward failure months before they actually fail, instead of waiting for the next scheduled inspection.
It doesn't replace an inspector's judgment. It tells the inspector where to look first, and gives a portfolio manager a plan that updates itself instead of aging quietly in a drawer until the next budget cycle.
Where this is proven
This is the same shift I helped Dutch housing corporations make at Bryder: moving MJOP planning off static spreadsheets and onto a live, digital-twin-backed platform.
Read the case study →Sound close to your actual problem?
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