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The future of business intelligence (BI) is to integrate intelligence into operational systems that works in real-time analyzing small chunks of data based on requirements on continuous basis. This is moving away from traditional approach of doing analysis on ad-hoc basis or sporadically in passive and off-line mode analyzing huge amount data. Various AI techniques such as expert systems, case-based reasoning, neural-networks play important role in building business intelligent systems. Since BI involves various tasks and models various types of problems, hybrid intelligent techniques can be better choice. Intelligent systems accessible through web services make it easier to integrate them into existing operational systems to add intelligence in every business processes. These can be built to be invoked in modular and distributed way to work in real time. Functionality of such systems can be extended to get external inputs compatible with formats like RSS. In this paper, we describe a framework that use effective combinations of these techniques, accessible through web services and work in real-time. We have successfully developed various prototype systems and done few commercial deployments in the area of personalization and recommendation on mobile and websites., {"references":["R. Ling, and D.C. Yen, \"Customer relationship management: an analysis\nframework and implementation strategies\", Journal of Computer\nInformation Systems, Vol. 41, No. 3, pp. 82-97, 2001.","E. W. T. Ngai, Li Xiu, D. C. K. Chau, \"Review: Application of data\nmining techniques in customer relationship management: A literature\nreview and classification\", Expert Systems with Applications: An\nInternational Journal, Vol. 36, No. 2, 2592-2602, 2009.","E. W. T. Ngai, \"Customer relationship management research (1992-\n2002): An academic literature review and classification\", Marketing\nIntelligence, Planning, Vol. 23. 582-605, 2005.","J. Abbott, M. Stone, F. 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