For investors, analysts, auditors, legislators, and researchers, financial records including quarterly reports, yearly 10-K filings, and regulatory disclosures include a wealth of information. Dense textual sections, financial figures, legal disclaimers, footnotes, and forward-looking assessments are all included in these agreements, which can total hundreds of pages. Their intricacy makes hand analysis slow, inconsistent, and prone to biased interpretation. While natural language comprehension has been enhanced by recent developments in large language models (LLMs), these models lack source-grounded reasoning and display hallucinations when dealing with lengthy, unstructured financial data. The AI Based Web Synchronise Analyzer, a multi-agent system that combines Retrieval-Augmented Generation (RAG), LangChain components, LangGraph-orchestrated decision routing, vector embeddings, document graders, hallucination evaluators, and a Streamlit-driven interface, is presented in this study in order to overcome these constraints.