Flare is a unique approach to home security. It protects user's home all by itself, recognizes and identifies friends from foes and adapts to home specifics - such as noisy environments, random schedules, various visitors. Using embedded machine learning algorithms, Flare can understand what is happening in the world around it, and predict dangerous events occurring at home.

We are analysing both audio (dog barking, glass breaking, steps, speech, alarm, etc.) and video (humans, dogs, cats, faces, fire, etc.) in a parallel manner, to classify events, and learn patterns from them in time. All this intelligence working on a small embedded device, is what makes Flare unique, powerful and challenging to build. Top technical limitations and challenges, together with various solutions, usability concerns in IoT, state-of-the-art of the industry will be presented and discussed.