Agentic Enterprise Knowledge & Decision Intelligence Platform using Advanced Retrieval-Augmented Generation (RAG)

This document presents the design, math, code and test results for an Agentic Enterprise Knowledge and Decision Intelligence Platform that uses Advanced Retrieval-Augmented Generation or RAG. Modern company knowledge systems are broken up into different data stores. They hold piles of unstructured files such as contracts, standard operating procedures, human resources manuals, regulatory filings and emails from many departments. They also hold data lakes such as enterprise resource planning databases, customer relationship management stores and financial ledgers. The usual RAG methods and stand‑alone large language model chatbots have fatal problems when used in the real world. They hallucinate a lot when they try to reason across documents. They cannot link a table to an unstructured text. They do not check evidence in a loop. They also cannot run tasks on their own. To fix these core problems the new platform introduces a Functional Architecture. Level 1 is the Knowledge Layer. It mixes layout‑aware chunking with a sparse‑dense search that uses BM25 and BGE‑Large 1024‑dimensional embeddings. It then fuses ranks with Reciprocal Rank Fusion. Refines results with a cross‑encoder re‑ranker. Level 2 is the Intelligence Layer. It uses agents that split big queries generate text‑to‑SQL that follows the database schema combine evidence from different modes and keep checking facts. Level 3 is the Action Layer. It turns insights into real workflows executive briefing reports, prioritized ticket replies and safe webhook links. We tested the platform on an enterprise benchmark with 50,000 documents and 2.5 million transaction rows. The results show the Agentic Enterprise Knowledge and Decision Intelligence Platform has 89.2 % context precision 94.6 % factuality and 91.3 % success, on multi‑hop tasks. We also show the Agentic Enterprise Knowledge and Decision Intelligence Platform works in banking, healthcare, legal audit IT DevOps and a full diagnostic study that fixed sales anomalies and pricing changes.