Hospital sustainability policies and demand for environmentallyconscious healthcare supplies drove Vention to Verdigris. Vention was seeking energy data metrics, particularly validation of energy usage and insights for potential savings. With 13 facilities and a newly formed sustainability council, Vention needed a tool to easily identify and quantify hidden potential cost savings. They also needed a reliable system to track equipment malfunction to maintain sensitive environments which are critical to medical device manufacturing. Vention selected their Sunnyvale office, which has several clean rooms, for the proof-of-concept test. They wanted to learn how to best prevent downtime for important equipment such as HVAC, which could jeopardize FDA-approved clean room conditions, contaminate materials, and ultimately increase production costs. Vention approached Verdigris to get help going above and beyond their customers’ specific requirements for sustainability.

Modern buildings are required to run longer hours, support a variety of end uses, and contribute to higher levels of economic productivity, leaving a thin margin for error. However, even the most advanced building and environmental control systems have failed to adequately support facilities and operations management. Buildings are often inefficient and the people using them are underserved. To meet occupant comfort and maintain cost and energy efficiency, a dynamic, AI-assisted approach is needed.

Juan and the Orchard ownership group had reached the limit of their own capacity. Orchard faced two key challenges: Limited engineering capacity to proactively manage their properties in an effective manner. A rigorous method to measure success and return on investment, especially for new energy efficiency projects. Moreover, from his position overlooking two hotels, Juan wanted a better way to monitor usage and compare results between the two properties. Each 10 stories tall, with a roughly equal number of rooms and located just 500 yards apart, the energy consumption profile should be very similar. In fact, this wasn’t the case.

Bill DeMartini, Chief Engineer of the W Hotel San Francisco, was searching for a way to identify the unseen problems in his building. He wanted to take energy efficiency beyond his hotel’s LEED Silver certification. To tackle this, he knew he must look at TRACE, the award-winning restaurant. Without a cost-effective way to monitor kitchen operations 24/7, Bill lacked the facts required to optimize his kitchen and improve W Hotel’s efficiency. Bill engaged with Verdigris to deliver the granularity he needed to identify ways to improve the kitchen operations.

Hospital sustainability policies and demand for environmentally conscious healthcare supplies drove Vention to Verdigris. Vention was seeking energy data metrics, particularly validation of energy usage and insights for potential savings. With 13 facilities and a newly formed sustainability council, Vention needed a tool to easily identify and quantify hidden potential cost savings. They also needed a reliable system to track equipment malfunction to maintain sensitive environments which are critical to medical device manufacturing. Vention selected their Sunnyvale office, which has several clean rooms, for the proof-of-concept test. They wanted to learn how to best prevent downtime for important equipment such as HVAC, which could jeopardize FDA-approved clean room conditions, contaminate materials, and ultimately increase production costs. Vention approached Verdigris to get help going above and beyond their customers’ specific requirements for sustainability.

Building owners and property managers face significant challenges in verifying the return on investment (ROI) from energy efficiency measures due to the dynamic nature of buildings. Factors such as shifting weather patterns, changes in occupancy, and varying equipment lifespans complicate the isolation of savings from energy efficiency investments. Traditional methods of verifying savings, such as hiring energy engineers to create predictive thermodynamic energy models, are often complicated and expensive. These methods involve intensive data collection and calibration processes, which can cost between $0.10 to $0.50 per square foot, making them cost-prohibitive for many building owners. Additionally, traditional methods have limited capability for tracking ongoing performance, which is crucial for identifying failures or below-average performance of energy efficiency measures.