Every large building already collects the data that reveals its waste. LeanFM is the software that reads it — no hardware to sell, no sensors to install. A thin, fast wedge onto an enormous installed base, built on research from Carnegie Mellon.
of a building's HVAC energy spend is lost to hidden faults.
typical return on finding and fixing them.
sq ft per building — universities, healthcare, museums, K-12, commercial.
Figures from independent building retro-commissioning research. Individual results vary by building.
We connect read-only to the building automation system a customer already owns. Nothing to manufacture, ship, install, or maintain — the economics of software, not devices.
A sample analysis turns one building's existing data into ranked, dollar-valued findings — a short, low-friction path from first contact to proven ROI.
One building becomes a campus or portfolio. Recurring analysis plus AIR scoring across buildings creates durable, expanding accounts.
identified in a single school, from existing BAS data.
first-year savings, growing to $101,383 in year two.
where the Prescriptiv engine was developed — the research foundation behind the platform.
The Prescriptiv fault-detection engine grew out of Carnegie Mellon research; AIR turns its findings into one explainable, comparable score; Maple makes it conversational. Together they form OnPoint — depth that's hard to replicate and gets better with every building analyzed. That research lineage stays inside the company: co-founder Burcu Akinci, Ph.D., is now Dean of Carnegie Mellon's College of Engineering.
Customers don't buy a one-time audit; they keep running it, watching AIR move as fixes land. Findings they can verify build the trust that expands accounts across a portfolio — and every building analyzed sharpens the product.
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