Technical Deep Dive: AI-Powered Customer Service Automation Architecture (Published)
The rapid evolution of customer service automation through artificial intelligence has transformed the landscape of customer interactions and support operations. Advanced implementations of natural language understanding, coupled with sophisticated distributed architectures, have revolutionized how organizations handle customer inquiries and resolve issues. The integration of machine learning models, knowledge graphs, and multi-modal processing capabilities has enabled unprecedented levels of personalization and context awareness in automated customer interactions. Through the implementation of robust technical architectures, including lambda processing frameworks, comprehensive security protocols, and advanced monitoring systems, modern customer service platforms demonstrate remarkable improvements in resolution times, accuracy, and customer satisfaction. The incorporation of best practices in scalability, performance optimization, and system monitoring has established new standards for automated customer service delivery, while emerging technologies continue to push the boundaries of what automated systems can achieve in terms of understanding, personalization, and efficient issue resolution.
Keywords: Artificial Intelligence, CRM, Real-time personalization, automated customer service, distributed architecture, multi-modal processing, natural language understanding
Metric Genetic-Fuzzy Based Customer Relationship Management Health Model (Published)
Customer Relationship Management (CRM) is a model for managing a company’s interactions with current and future customers. Most of the implemented CRM approaches are subjective in nature, in addition to the serious need to separate feelings of satisfaction or dissatisfaction with the services delivery. Metric Genetic-Fuzzy Based Customer Relationship Management Health Model (MGFBCRMHM) was initiated for these reasons. Unified Modeling Language was utilized for modeling the software system, depicting clearly the interaction between various components and the dynamic aspect of the system. The simulation results utilizing Matrix Laboratory (MATLAB) was satisfactory. This paper demonstrates the practical application of metric based soft computing techniques in the health sector in determining patient’s satisfaction.
Keywords: CRM, Fuzzy Logic, Genetic Algorithm