Abstract: State of art techniques hardly provide technical solution to address complex dependencies that exist between different areas of SaaSification. Embodiments of the present disclosure provide a method and system for a contextual advisory for a platform, a process, a technology, and technical components to build Everything as a service (XaaS) for a domain of interest. The method dynamically generates contextual advisory and recommendations to build the XaaS on users input from assessment and decision navigation. Decision navigation captures complex interdependencies of business, design, technology etc. User inclination/inferences are captured from assessment. In accordance with initial inclination of the user, the user is navigated through decision making process involved in building the XaaS model for the domain of interest of the user. The system recommends the decision and generates the dynamic information based on user inputs which is used to generate the advisory documents.

Abstract: This disclosure relates generally to data integration, and more specifically to artificial intelligence based data integration of entities post market consolidation. The method includes extracting, using one or more text mining models, metadata associated with at least one category of each of the participating entities of the deal, from data sources associated with the entities. The disclosed system dynamically configures an assessment for the at least one category based on the metadata by using a set of Natural Language Processing (NLP) rules. The assessment includes parameters associated with the data integration of the entities. Response to the assessment is obtained from users belonging to the entities. An artificial intelligence (AI) based processing model assigns a similarity score to the responses, where the similarity score is indicative of extent of match between distinct responses obtained from the entities. A recommendation engine recommends a data integration model based on the similarity score.

Abstract: This disclosure relates generally to data integration, and more specifically to artificial intelligence based data integration of entities post market consolidation. The method includes extracting, using one or more text mining models, metadata associated with at least one category of each of the participating entities of the deal, from data sources associated with the entities. The disclosed system dynamically configures an assessment for the at least one category based on the metadata by using a set of Natural Language Processing (NLP) rules. The assessment includes parameters associated with the data integration of the entities. Response to the assessment is obtained from users belonging to the entities. An artificial intelligence (AI) based processing model assigns a similarity score to the responses, where the similarity score is indicative of extent of match between distinct responses obtained from the entities. A recommendation engine recommends a data integration model based on the similarity score.