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Heatmiser is UK’s leading smart home heating control manufacturers, offering standalone room thermostat control systems under their Neo Smart line of products. With options such as daily schedules, heating reminders, frost protection to save pipes etc, Heatmiser offers mobile-based advanced temperature control solutions.
The true breakthrough came with the integration of Agentic AI. Instead of relying only on sensor triggers, autonomous AI agents continuously learned household behavior, predicted heating needs, and dynamically coordinated across devices. This agentic orchestration allowed the system to proactively balance comfort, efficiency, and energy cost optimization in real-time.
Heatmiser’s existing mobile application was complicated to use, with layers of unnecessary webs of information overloading. The user often found themselves lost, this decreased their overall user-engagement, which meant less and less people were using their heating control app.
We understood the information architecture of the app and found out how it will help the users of this app. We deployed a human-centric approach to design and used powerful IoT technologies to provide tech-enabled heating control solutions for the entire house, segregated by user profiles. This resulted in an immersive user experience, more engagement, and more control.
Using one single mobile application for heating control, we gave the control back to the users. Not only can they now manage temperate settings with minimal clicks/taps, but they can also control temperature at each nook and corner of the house, create profiles, and deploy time-based temperature control. A new paradigm in IoT-powered smart home mobile app.
Using a combination of IoT technologies, human-centric user interface and automation rules, we turbocharged Heatmiser’s heating control solutions with advanced features. This helped both Android and iOS users to save upto 50% off their energy bills.
We conceptualized & designed user journeys based on the information architecture of the app, and enabled upto 15-times more user-engagement. Our minimalistic approach to user-interface proved to be a big hit with the overall user experience.
Using profile settings, users of Heatmiser can now set their temperature control based on various factors such as location, time of the day and more. This gives more control and flexibility to the end-users, and heating control becomes easy.
We facilitated upto 32 zones for smart heating control, that covered every corner and section of the house. These detailed geolocations, combined with smart profiles translates to more flexibility, and more control for temperature settings.
Using one single mobile interface, the user can control the temperature of the entire home, based on geolocation settings and profile. We call it IoT based magic.
With Agentic AI, the dashboard was not just informative but proactive—AI agents synthesized data from multiple devices, weather forecasts, and usage history to recommend optimal heating schedules.
AI agents autonomously orchestrated multi-device routines—such as lowering heating when windows were open or pre-heating rooms before occupancy—ensuring efficiency without manual intervention.
We developed a visually striking user interface, that inspired the users to engage and interact with the platform, seamlessly.
Behind every thermostat adjustment, a network of AI agents was working in the background—predicting usage, negotiating between energy-saving and comfort goals, and orchestrating multiple IoT devices seamlessly. This approach ensured customers always experienced optimal results without needing to constantly adjust settings.
Heatmiser wanted a robust, powerful and comprehensive mobile application that enabled their users to control and monitor temperature, across every part of their house. We not only conceptualized a delightful, and minimalistic user-design, but also leveraged the power of IoT and machine learning to develop a smart app, for a smart home.
By embedding Agentic AI, Heatmiser achieved more than IoT automation—it unlocked an adaptive, self-learning ecosystem. Customers saw up to 50% reductions in energy bills, while the system intelligently balanced user comfort, weather conditions, and grid efficiency. This agent-driven orchestration elevated the product from a connected device platform to a truly autonomous smart home ecosystem.
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