A New Paradigm in Fire Safety
Digital Twins and
Breakthrough Fire Prevention Solution
Detectium's digital twin for risk & anomaly detection, leverages vision-based sensors, 3D model, cloud, and machine learning for fire prevention and rapid detection.
Key features include fire localization, intensity detection, real-time incident feeds for fire brigades/rescue teams, and real-time fire propagation simulation.
Speed & Accuracy
Measured 80% faster detection and significantly reduced false alarms
Conventional fire detectors have limited coverage and are prone to false detections due to legacy technologies.
Detectium enables faster fire and anomaly detection without compromising accuracy.
Cover distances of more than 60 meters with
minimal hardware installation
Visual sensor technology provided by Detectium allows lower CAPEX by being able to cover larger area per sensor installed.
Furthermore, scaling fire safety has never been easier with plug-and-play design, allowing sensors to be integrated with ease.
View past incidents and
simulate fire propagation
Information is critical before and after fire incidents. Avoid potential damages by pinpointing hazard areas by simulation.
Use the gathered data after incidents to perform root-cause analysis and avoid fire incidents in the future.
Agriculture & Animal farms
Detectium solution is currently used in animal farms in Finland and Sweden
Industrial site solution has been deployed the GCC market
Siavash Khajavi, CEO
Siavash is the CEO and co-founder of Detectium holds a PhD and has conducted extensive research on digital twins. His vision is creating a world where the digital representation of all objects can be efficiently and accurately created to enhance the safety in the physical world.
Adriaan Knapen, CINO
Adriaan is a CINO at Detectium. His wide range of expertise includes embedded software development, release and test automation, cloud infrastructure, and software development culture.
Zixuan Liu, CTO
Zixuan is the CTO and a computer vision and embedded system expert at Detectium, enabling seamless integration of image data and AI models. She has deep knowledge of AI research and state-of-the art AI models.
Aarni Huuskonen, CFO
Aarni is the CFO and key account manager at Detectium. Having experience from Nordea and as a business analyst in a scaleup, he ensures Detectium’s path to fast and profitable growth.
Mehdi Moshtaghi, AI developer
Mehdi is an experienced AI developer, responsible for ML Operations, with broad expertise in Computer Vision, Large Language Models, and Diffusion Models, committed to advancing AI innovation.
Yu Dikai, 3D model developer
Yu creates 3D models from environments to enable data fusion in digital twins.
He holds a position at Umeå and Aalto University and knowledge in building information management and digital twinning technologies.
He is the Chief Specialist of business development and innovation with LähiTapiola Insurance Company.
He is a Professor of operations management with Aalto University, Finland. He is an expert in supply chain management and design science research.