Abstract
<jats:p>This paper presents the architecture, design rationale, and empirical evaluation of an AI-Powered System for Dynamic Content Creation, Storage, and Retrieval—a centralized, web-based platform engineered to streamline the complete lifecycle of digital content. The system orchestrates multiple state of-the-art Large Language Models (LLMs), including NVIDIA NIM (Llama 3.1), Groq (Llama 3.1), Cerebras CS-3, and Cohere, within a unified multi-model orchestration layer built on Next.js, TypeScript, and PostgreSQL. Core contributions include: (1) a dynamic AI orchestration layer that routes generation requests across LLM providers based on latency and reasoning requirements; (2) an integrated real-time fact-checking module powered by Google Search APIs to detect and flag AI-generated hallucinations; (3) an automated content quality pipeline delivering readability, uniqueness, and factual-accuracy scores; and (4) a structured semantic knowledge base enabling Retrieval Augmented Generation (RAG) for document-centric workflows. Experimental results confirm that the platform reduces average content-generation cycles from multi-hour manual processes to sub-minute automated workflows, while measurably improving factual accuracy and content quality across academic and professional use cases. The modular architecture is designed for scalability, supporting future extensions including multimodal generation, collaborative editing, and decentralized P2P storage.</jats:p>