Verdigris integrates IoT energy meters, cloud software and AI to deliver real-time, circuit-level energy monitoring and data analysis for enterprises with substantial carbon emissions. Their happiest customers care about three things: Who makes it the easiest to ensure data quality? Who can deploy at scale the fastest? And whose data is ready for the AI applications to come?
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Verdigris was founded in 2011 to help solve the global problem of energy waste. Their vision is to bring AI-powered energy intelligence to every building in the world.
Today, Verdigris’ focus is on providing enterprises with substantial carbon emissions, such as data centers and 24/7 facilities, with real-time, circuit-level energy monitoring and data analysis. Customers use Verdigris to improve capital efficiency and reduce risk when making capacity planning decisions; to track, monitor, and reduce Scope 2 emissions; and to maintain compliance with emerging sustainability requirements.
Verdigris differentiates by being purpose-built for fast, cost-effective rollouts; building foolproof data resilience into its products; and providing highly-granular data that is future-proof for AI-enabled targeted analysis and predictive analytics.
Despite unusable BMS data at one pilot site and slower-than-expected operational cost savings, Amazon's FDD pilot still delivered enough value to trigger a broader rollout across its portfolio.
Hannah Baker, engineer at Willow, walks through how DFW Airport built a CBM program that actually stuck, from training a non-technical QA team to triage thousands of faults, to graduating recurring issues into automated work orders, to tracking a single KPI called 'unsuccessfully actioned' that finally gave leadership visibility into whether closed work orders were actually fixing the problem.
Jose de Castro, CTO of Mapped, shows how one of the world's largest retailers moved restroom operations from schedule-based janitorial rounds to condition-based workflows by combining foot traffic sensors, flush counts, soap levels, and occupancy predictions into AI-summarized work orders that land directly in the existing CMMS, with no new dashboards or tools for technicians to learn.
Brad Dameron from the University of Iowa's Asset Optimization Team and Katie Rossman from Clockworks Analytics walk through how Iowa handles 3,500 faults per day without burying their maintenance shops, showing the exact triage, routing, and closeout workflow they built to turn fault detection into planned work orders that look and feel identical to every other work order in the system.
Tearle Whitson, VP of OT at Metronational and a 26-year facilities veteran, digs into the infrastructure layer that makes or breaks CBM programs—explaining why bad sensor data, uncalibrated instruments, and communication failures will undermine your fault detection before you ever get to triage, and how to build the 'building DNA' foundation that everything else depends on.
Travis Criner, Executive Director of FM Programs at CBRE, makes the case that the hardest part of condition-based maintenance isn't the technology—it's redesigning your maintenance workflows, from validating which PM tasks actually need to exist, to updating CMMS job plans, renegotiating third-party contracts, and deciding what to do with the technician capacity you free up.
James Dice introduces the Nexus Labs Condition-Based Maintenance Playbook, built from 50+ case studies, walking through why CBM is best understood as a layer on top of existing maintenance programs—not a replacement—and outlining the eight-step framework for setup, piloting, and rollout that the industry's leading building owners are using to reduce reactive work, extend asset life, and prove value to leadership.
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