Indoor positioning systems have become widely available due to the increased number of wireless technologies available today. A type of wireless device that has become very popular in the past years has been the Bluetooth Low Energy (BLE) beacon. This compact, battery-powered device can enable location-based and proximity services across in-door spaces. Several indoor positioning techniques have been explored to achieve indoor localization using these wireless devices. One of these techniques is the fingerprinting technique, which requires careful collection of training data at known locations. We developed an app to facilitate and expedite the process of collecting training data with iOS devices. The training data is collected by our app and saved in the cloud for future retrieval. We collected training data from different floor maps, performed initial analysis on this data, and tested a fingerprinting algorithm in order to provide indoor localization. We developed several tools to evaluate and visualize the training data and tested our indoor localization algorithm in a real-time scenario.