entic Conversational Recommender System (LLM-Based)

Designed and developed a highly advanced Conversational Recommender System (CRS) utilizing Agentic AI architectures. The system employs multiple autonomous agents to understand user intent, maintain contextual memory, and dynamically recommend solutions. Built using Large Language Models (LLMs) and advanced orchestration frameworks to ensure seamless human-like conversational flows and precise data retrieval.