As technology continues to advance, services and capabilities become computerized, and an increasing amount of business is conducted electronically, there is an interesting need for real-time decision-making systems with many capabilities in various domains. In this paper we introduce INFERD (INformation Fusion Engine for Real-time Decision-making), an adaptable information fusion engine which performs fusion at levels zero, one, and two to provide real-time situational assessment. The advantages to our approach are threefold: (1) The level of abstraction in which the analyst interacts with the engine, (2) the speed at which the information fusion is presented and performed, and (3) our ability to give the user the choice to disregard ad-hoc rules or a priori parameters, which have both advantages and disadvantages. We present both a parameterized approach founded in statistical mechanics theory and a non-parameterized approach using concepts in entropy as understood in the context of information theory.