Novant unifies building data into a single, independent data layer (IDL) that integrates live BAS signals with static asset and space information. Their platform makes buildings machine-readable, scalable, and easier to manage across portfolios.
A radically simple, self-serve platform that puts you back in control. Whether you’re managing a single site or an entire campus, Novant brings all of your building systems and data into one unified, user-friendly platform. No vendor silos. No waiting months to get started. No paying for vendor startup costs again and again.
Our take after reviewing the product demo:
Novant delivers an independent data layer (IDL) purpose-built for the built environment. By combining real-time BAS data with design documentation, static asset records, and space information, Novant creates a comprehensive, machine-readable model of a building. Their distributed edge nodes securely connect to the cloud, enabling rapid scaling and simplified deployment through reusable “source maps.” With CSV import tools, mass manipulation features, REST API integrations, and native plugins, Novant streamlines how system integrators, service providers, and building owners manage complex portfolios. The result is a unified foundation for smarter operations, reduced complexity, and future-ready building applications.
Lendlease rewrote its tech procurement process after two vendors promised "out-of-the-box" APIs and one came back with a ~$200K integration quote.
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.
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