One team.
Every critical layer.
HomeMind brings together product leadership, AI research, embedded engineering and scalable software architecture – the disciplines required to turn autonomous building control into a safe, deployable product.
Turning advanced AI into a building product.
HomeMind is built by a team that can move from strategy and customer deployment all the way down to sensors, software architecture and learning algorithms.
Maciej Puchara
Product-driven founder combining enterprise software leadership, hands-on engineering and deep understanding of smart building automation. At HomeMind, Maciej leads strategy, product vision, pilots, partnerships and fundraising – turning advanced AI for building energy control into a scalable commercial product.
Marcin Pietroń
Associate Professor at AGH, AI/ML expert and author of 70+ scientific publications. Marcin brings deep expertise in machine learning, continual learning and neural network optimization, with prior industry experience at Cadence Design Systems, Samsung and Motorola.
Szymon Czerwiński
Embedded systems engineer with 6 years of experience in firmware, hardware optimization and sensor-based devices, including NCBR-funded R&D projects. Szymon leads hardware selection, sensor strategy and embedded development.
Adam Wójcicki
Software architect with 17 years of development experience and an Engineering Manager background at SmartRecruiters. Adam created the architecture enabling local edge deployment and cloud operation – making HomeMind scalable, secure and adaptable to different building environments.
Szymon Piórkowski
Szymon created the AI foundation that learns from user interactions and improves performance over time, enabling better comfort control and measurable energy savings.
From physical signals to autonomous decisions.
Safe building intelligence depends on more than an algorithm. HomeMind connects the full chain: customer need, building physics, reliable sensing, secure edge software and adaptive control.
Customer problem
Pilots, partnerships and product direction grounded in real buildings.
Reliable signals
Practical sensing and edge devices suited to retrofit environments.
Secure operation
Architecture that supports local deployment and controlled scalability.
Continual learning
Models that adapt as the building and operating conditions change.
Measured outcomes
Reinforcement learning connected to comfort, energy and safety limits.
Research depth. Product discipline.
Our work is shaped by one practical rule: autonomous control must prove value safely in real buildings, not only in a model or presentation.
Build the next pilot with us.
Start with a free assessment of your building, data and implementation path.