Atamate is an Oxford-based smart building software company that started in 2007. We aim to reduce the environmental impact of buildings in the UK and around the world while reducing both capital and running costs of properties. Not only do we continually strive to provide customers with best in class software, but we’re also constantly looking for better, more innovative ways to achieve our goals.
Based on decades of experience in construction, electronics and software engineering, Atamate uses a discrete network of sensors and controls to analyse building systems and usage, revealing and remedying areas of weakness, waste and risk. Atamate goes deeper than ‘smart home’ applications that focus on consumer comforts while providing machine learning insights that costly ‘building management systems’ cannot match.
Atamate can be deployed in new developments at a fraction of the capital cost of traditional building management systems. It provides revolutionary machine learning about building usage, system functioning and environmental factors.
Those insights are fed back into building management decisions, enabling targeted savings on energy and operational costs. At the same time, layers of security and access control can be continually customized. Atamate’s flexible design and structure enable it to interact with and control almost any device or machinery, avoiding problems commonly caused by the interoperability of different proprietary systems and standards.
Atamate’s innovative use of Bluetooth technology allows its unobtrusive network of sensors and controls to be retrofitted throughout a building. A decades-old office block and even a home designed and built centuries ago can benefit from an Atamate installation.
The minimally invasive procedure involves grafting the Atamate network onto the structure, to give efficient and intelligent control of the entire building for the first time. A proprietary portfolio of sensors, normally nested in a discreet ceiling fitting, acts in concert with remote wireless switching. Real-time data, predictive learning and environmental factors come together in one central control hub that can be actively or passively managed.