This demo highlights our efforts in designing a prototype recommender system for use within IBM Switzerland. Goal of the system is to enable the sales and marketing teams in exploring the ever-changing wealth of information about existing and prospective customers, and to facilitate the identification of previously undiscovered sales opportunities. The presented system achieves its goals first by enabling the effective data aggregation from diverse sources (financial data, RSS feeds, hardware and software install-base, etc), and then by allowing the interactive exploration of the customer space. Data exploration is achieved through the use of neighborhood graphs and bi-cluster formation techniques, that offer both proximity and cluster navigation of the customer/product search space. The demo, which is developed as a Web 2.0 platform, offers to the users the capability to interact with state-of-the-art analytics for recommender systems, and also highlights the significance of exploratory data visualization techniques.