Imagine a tool that simplifies the creation of an Independent Data Layer, empowers you to deploy advanced controls effortlessly, and seamlessly connects buildings to various partner applications like fault detection or energy optimization - that's exactly what Normal Software does. What sets Normal apart is its simplicity. Unlike many other solutions, Normal is easily deployable as a standalone docker container, enabling smooth integration into existing products and IT infrastructures or directly embedding into your own product.
Founded in 2020, Normal Software was created by industry pioneers to bring building programming into the modern era of software development. The team at Normal specializes in building controls, data management, and applications for IoT (Internet of Things) analytics. They focus on providing open, transparent, and portable solutions for controlling and analyzing IoT devices.
Key features of the Normal Framework (NF) offering include:
- Open & Transparent Approach: NF emphasizes openness and transparency in the design, with support for native BACnet and an API-first approach.
- Powerful APIs and SDK: NF offers powerful APIs and a JavaScript SDK for developers to easily integrate and extend their solutions. This eliminates the need for proprietary languages or complex tools.
- DataOps Features: NF provides tools for DataOps, including data modeling, normalization, quality monitoring, and rapid identification and resolution of outages and quality issues. They also support an "Edge-Only Model" for data processing.
- No 3rd Party SaaS Subscription: NF does not require a third-party subscription for monitoring and securing data, offering a self-contained solution.
Normal offers a comprehensive platform for managing and controlling IoT devices and data, with a focus on openness, flexibility, and ease of integration. They look forward to working with you to create a world where IoT analytics and controls are open, portable, and sharable.
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Episode 198 is a conversation with Brad Bonavida from Nexus Labs, Gabe Sandoval from UCSF Health, and Patrick Testoni from the University of California, Santa Cruz.
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.
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