High-Level fusion systems based on the JDL model are relatively immature. Current solutions lack a comprehensive ability to manage multi-source data in a multi-dimensional vector space, and generally do not integrate collection to action models in a cohesive thread. Recombinant Cognition Synthesis (RCS) leverages best-of-breed techniques with a geospatial, temporal and semantic data model to provide a unified methodology that recombines multi-source data with analytic and predictive algorithms to synthesize actionable intelligence. This architecture framework enables the traversal of entity relationships at different level of granularities and the discovery of latent knowledge, thereby facilitating the domain problem analysis and the development of a Course-of-Action to mitigate adversarial threats. RCS also includes process refinement techniques to achieve superior information dominance, by incorporating specialized metadata. This comprehensive and unified methodology delivers enhanced utility to the intelligence analyst, and addresses key issues of relevancy, timeliness, accuracy, and uncertainty by providing metrics via feedback loops within the RCS infrastructure that augment the efficiency and effectiveness of the end-to-end fusion processing chain.