Mapped is the independent data layer for commercial real estate. By automating the discovery, extraction, and normalization of data from building systems, sensors, devices, and vendor APIs, Mapped simplifies the integration process down to a single standardized API. Mapped breaks enterprise data silos to empower building owners, facility operators, and solution providers to rapidly unlock the value of data and drive transformative business outcomes – allowing them to focus on innovation rather than integration.
Mapped started with the idea “what if every built space had an API?” Getting data out of buildings was a complex, time consuming and often manual process - weeks or even months of work. To solve the problem, they created a knowledge graph of people, places and things using machine learning to automate the process of data extraction and identification. They then built a simple, secure and unified API on top; now anything that generates data - devices, sensors, enterprise applications and more - is accessible quickly, easily and securely.
Check out their website to learn more: https://www.mapped.com/
Lincoln Property Company’s Chris Lelle realized that burdened engineers can’t each manage 300,000 sq ft by diving deep into BAS data—so he used FDD to simplify the troubleshooting his techs need to do.
For years, complaints about comfort at a Microsoft campus were attributed to BAS issues. Packet-level network data told a different story and exposed 118,000 hours of missed runtime.
Goldman Sachs detailed how it scaled a global smart building program across 94 sites by changing where cybersecurity decisions happen—before devices ever reach the field.
Delta Air Lines and JLL made a deliberate call at LaGuardia Terminal C: stop relying on engineers to walk rooms multiple times a day just to confirm conditions were still acceptable—and replace those rounds with standardized, proactive alerting.
Five years ago, Clockworks Analytics made a bet: fault detection would only reach most commercial buildings if it could work without deep owner-side engineering teams.
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