LeanFM Technologies
LeanFM Technologies makes OnPoint, software that reads existing building-automation data to surface ranked, dollar-valued HVAC faults.
Founded
2015LeanFM Technologies makes OnPoint, software that reads existing building-automation data to surface ranked, dollar-valued HVAC faults.
OnPoint is the platform facilities teams use to see their buildings clearly. It runs on the Prescriptiv fault-detection engine, born from Carnegie Mellon research, which reads the trend data a building automation system already records and surfaces the faults that alarms never catch — simultaneous heating and cooling, stuck economizers, drifting sensors, and schedules that no longer match how a building is used.
Each finding is scored by AIR into one explainable, comparable rating that opens to the underlying readings, and Maple lets facilities teams ask the same data questions in plain language. The output is a ranked, dollar-valued list of what to fix and why, with documented first-year savings at The Andy Warhol Museum and across K-12 and commercial portfolios.
Up to 30% of a building's HVAC energy spend is lost to hidden faults, and LeanFM targets a 3 to 5 times typical return on the work it surfaces. That economics lands in buildings from 80,000 to over 1 million square feet — universities, healthcare, museums, K-12, and commercial portfolios — where centralized HVAC systems already generate the trend data the platform reads.
Because there is no hardware to sell, the model is recurring software that grows by land-and-expand: a sample analysis proves the return on one building, then the same read-only connection extends across a portfolio. The addressable spend is the energy waste already inside systems a customer owns, not new devices to deploy.
LeanFM connects read-only to the building automation system a customer already runs, selling no hardware and installing no sensors. That keeps its economics those of software rather than devices, and gives it a fast, low-friction path from a sample analysis to a proven return.
Its Prescriptiv engine grew from Carnegie Mellon research, and every AIR rating opens to the underlying findings and the trend data behind them, so facilities teams can interrogate a result rather than trust a black box. A Maple layer lets non-specialists ask the same data questions in plain language.