Accurate occupancy information in commercial buildings can enable several useful applications such as energy management and dynamic seat allocation. Most prior efforts in this space depend on deploying an additional network of deeply coupled sensors to gather occupancy details. This paper presents a novel approach for occupancy detection using only context sources that are commonly available in commercial buildings such as area access badges, Wi-Fi access points, Calendar and Instant Messaging clients. We present models to conduct a situation-centric profiling using such sources and evaluate results of those models. Through a pilot study of a building floor with 5 volunteers for 6 weeks, we demonstrate the potential for detecting occupancies with accuracy as high as 90%. © 2012 IEEE.