Integrating Large Language Models (LLMs) with Oracle 26AI for Advanced Enterprise Analytics and Knowledge Management

Abstract

Enterprise organizations increasingly hold two categories of information assets that have historically been managed by incompatible systems: structured relational data governed by transactional database platforms, and unstructured knowledge assets such as policy documents, clinical notes, audit records, and correspondence that resist tabular representation. Oracle 26AI, the converged successor to Oracle Database 23AI, embeds vector storage, hybrid semantic and relational retrieval, and native large language model (LLM) orchestration directly inside the database engine, removing the fragile middleware layer that has traditionally connected enterprise data to AI systems. This paper examines how large language models can be integrated with Oracle 26AI to deliver advanced enterprise analytics and knowledge management capabilities, including conversational natural language querying, retrieval-augmented generation (RAG) over proprietary knowledge repositories, automated insight narration, and governed knowledge retrieval workflows. Drawing on Oracle's published architectural roadmap, established RAG and vector indexing literature, and enterprise deployment patterns observed in regulated industries such as health insurance, this paper proposes the Enterprise Knowledge and Analytics Intelligence Framework (EKAIF), a six-domain methodology spanning data convergence, semantic retrieval, LLM orchestration, analytics and insight delivery, governance and compliance, and continuous evaluation. The paper further presents integration patterns for embedding LLMs with Oracle 26AI, a retrieval-augmented generation pipeline design tailored to enterprise knowledge management, and benchmark findings drawn from Oracle 23AI vector search evaluations and comparable enterprise generative AI deployments. Results indicate that LLM-augmented analytics on a converged Oracle 26AI platform can reduce average analytical query resolution time by approximately 60 percent relative to traditional BI report cycles, achieve semantic retrieval precision above 90 percent for enterprise knowledge corpora, and reduce generative output hallucination rates by more than half when grounding is enforced through in-database retrieval. The paper concludes with a discussion of governance obligations, technical limitations, and future research directions for AI-native enterprise data platforms.