Investors
Reducing Energy Waste in Buildings Through Better System Intelligence
LeanFM helps building owners and operators uncover hidden inefficiencies in their building systems—turning existing data into actionable insight that reduces waste, improves performance, and supports long-term operational efficiency.
LeanFM operates as a building system intelligence layer—helping facilities teams understand what their systems are actually doing.
Building system intelligence layer
Existing data
Buildings already generate operational signals
System behavior
Patterns reveal hidden inefficiencies
Portfolio action
Findings become operational priorities
Scalable starting point
The Wedge: Sample Analysis
LeanFM does not need a long enterprise implementation to begin showing value. The first commercial step is a focused Sample Analysis using existing BAS trend data.
Data review, findings walkthrough, then paid rollout where the value is clear.
Existing BAS Data
Runtime · Setpoints · Schedules · Sensors
Hidden Patterns
Waste · Drift · Conflicts · Logic Faults
Clear Actions
Prioritize · Assign · Resolve
A Large, Persistent Problem in the Built Environment
Buildings rely on complex systems to manage heating, cooling, and ventilation. While these systems generate large amounts of data, many inefficiencies remain hidden.
A significant portion of building energy waste is driven by operational inefficiencies rather than equipment failure.
Most buildings already have the data needed to improve performance—the problem is that it is not being interpreted effectively.
Building systems are complex and difficult to monitor manually
Many inefficiencies do not trigger alarms
Facilities teams lack time to analyze large volumes of data
Energy waste, comfort issues, and equipment strain persist unnoticed
Business Model
Sample Analysis as the first commercial offer
Findings walkthrough to validate operational value
Paid rollout for buildings or portfolios where the value is clear
Every engagement is backed by a money-back ROI guarantee.
If our analysis does not identify HVAC issues with combined estimated first-year operational impact of at least 3x the engagement fee, we refund the fee. The guarantee is conditional on the customer implementing the recommended corrective actions. Full terms at /terms.
The guarantee is operationally underwritten by Sample Analysis as the entry product: we know what we will find before quoting an engagement, and the deliverable carries its own proof. This significantly lowers procurement friction in our institutional segments.
Making Hidden Operational Issues Visible
LeanFM analyzes existing building system data to identify patterns that indicate inefficiencies, faults, and operational issues.
LeanFM focuses on how systems behave over time, not just whether something has failed.
Works with existing building system data
Identifies hidden faults and inefficiencies
Produces clear, prioritized findings
Supports facilities teams with actionable insights
Timing and Market Drivers
Rising energy costs
Increasing pressure on operating budgets
Growing focus on sustainability and emissions
Aging building infrastructure
Increasing complexity of building systems
Applied in Complex, Real-World Buildings
LeanFM has been applied in real-world environments where system performance directly impacts cost, comfort, and operational reliability.
LeanFM has identified hidden system issues, including sensor drift, control logic faults, and operational inefficiencies that impact performance and cost.
A Pittsburgh-area cultural institution
Historical healthcare facility analyses
Large commercial and mixed-use buildings
Representative Outcomes From Case Studies
Identification of hidden system faults affecting energy use and comfort
Identification of faults that were not previously visible to facilities teams
Reduction in unnecessary runtime and system strain
Improved prioritization of maintenance tasks
Reported savings in specific deployments
Increased visibility into building system performance
Large Addressable Market
LeanFM targets buildings with complex systems and significant operational scale.
These buildings represent a large portion of global energy consumption and ongoing operational spend.
K-12 school districts
Universities
Commercial real estate
Museums and cultural institutions
Built on Advanced Data Analysis
LeanFM applies advanced data analysis to building system behavior, focusing on identifying patterns and relationships that are difficult to detect through traditional monitoring.
Experienced Leadership and Research Foundation
Founded by Carnegie Mellon University researchers
Co-founder Burcu Akinci is a current CMU professor in facilities information technology
Co-founder Pine Liu was recognized as an IFMA Forty Under 40 honoree
Methodology developed from CMU facilities research, productized as OnPoint
Why This Scales
LeanFM does not require new hardware to begin delivering value. It works with data that buildings are already generating, making it applicable across a wide range of facilities.
Uses existing BAS trend data
No new hardware required to start
Repeatable diagnostic workflow
Vertical-specific outbound motion
Expandable from one building to portfolios
Reducing Energy Waste at Scale
Buildings consume a significant portion of global energy. Much of that energy is lost due to inefficiencies that are difficult to detect.
LeanFM’s goal is to make those inefficiencies visible, helping buildings operate more efficiently and reducing unnecessary energy use over time.
Connect With LeanFM
For investor conversations or to learn more about LeanFM’s approach and growth strategy, reach out directly.
Contact LeanFM