LeanFM exists to give facilities teams something they've never had: a clear, defensible view of the waste, faults, and risks already hiding in their buildings — from the data those buildings already produce.
At Carnegie Mellon, researchers asked a deceptively simple question: buildings already record thousands of data points every day — could that data, read closely enough, reveal the faults that alarms never catch? The simultaneous heating and cooling. The stuck economizer. The schedule that quietly stopped matching how a building is used.
The answer was yes — and the harder part was making it trustworthy. A fault you can't explain is a fault no engineer will act on. So the work focused not just on finding problems, but on showing the evidence behind every one.
That research became Prescriptiv, our fault-detection engine. Today it runs inside OnPoint, the platform facilities teams use to see their buildings clearly — scored by AIR, explained by Maple, and reported in dollars.
You don't need more sensors. The trend data your BAS already collects holds the answer — it just needs to be read closely, and ranked by what it costs.
Every AIR rating opens to its findings, and every finding opens to the trend behind it. No black box, because no facilities director should act on one.
LeanFM is read-only by default. We surface what's wrong and what it's worth; your team decides what to change. Nothing happens to your building without you.
Another screen of charts isn't help. A ranked list of what to fix, why, and what it returns — reviewed by engineers — is.
Where the Prescriptiv engine was developed — the research foundation behind every analysis we run.
$56,386 in first-year savings, growing to $101,383 in year two — in one of the most demanding climate environments there is.
$105,000/yr identified in a single school, and active work across schools and commercial portfolios.
LeanFM was founded by Carnegie Mellon researchers who studied how buildings actually operate — and it's still led by them.
Nick leads LeanFM with a focus on commercializing practical building-system intelligence for facilities, energy, and operations teams.
A former facilities manager, software architect, researcher, and entrepreneur, Pine connects building operations, data systems, and applied facilities research.
IFMA Forty Under 40
Dean of Carnegie Mellon's College of Engineering and a CMU University Professor, whose research advanced how the data buildings already produce reveals their true performance.
Dean of Engineering, Carnegie MellonBehind them is a team of controls and commissioning engineers, data scientists, and building scientists — which is why findings are reviewed by people who have actually stood in a mechanical room before they reach you. Interested in joining us?
Send us one building's data and we'll return a sample analysis — real findings with dollar values, and your first AIR score.