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.
Many buildings still rely on veteran operators who “just know” how systems work. As those experts retire, building owners are realizing the same work required to preserve that knowledge is also what makes AI usable in buildings.
CannonDesign watched a BAS access quote climb from $66K to $100K, then brought in an MSI to review the architecture and cut it to $29K.
Northern Arizona University’s CIO realized that aggregating IoT data wasn’t the hard part; relational context was. After choosing buy over build, the team moved from raw BACnet feeds to ontology-driven HVAC control, achieving 30% energy savings.
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