Transforming Unstructured Data into Context-Aware Conversational Intelligence using Advanced GPT And LLAMA

This project aims to transform vast amounts of unstructured data such as text, speech, and multimedia into meaningful conversational intelligence using advanced Large Language Models (LLMs), specifically GPT and LLaMA. In today's digital environment, a large portion of data exists in unstructured formats, making it difficult for traditional AI systems to extract contextual meaning and accurately interpret human intent. To address this limitation, the project employs a structured pipeline involving data integration and preprocessing, contextual embedding generation, and knowledge graph construction, enabling the conversion of complex unstructured inputs into structured and actionable representations. The developed system integrates conversational AI orchestration to generate context-aware and adaptive dialogue responses, supporting scalable applications in customer service automation, medical information retrieval, and enterprise systems. By combining the generative strength of GPT with the computational efficiency and domain adaptability of LLaMA, the proposed approach enhances decision-making, improves communication, and delivers personalized user experiences. This study highlights the potential of large language models to unlock the hidden value of unstructured data and advance intelligent, human-centric conversational systems.