Large Language Models are powerful at reasoning and recommendation, but they routinely hallucinate entities that do not exist in an organization’s proprietary data. The common response is to build a Retrieval-Augmented Generation (RAG) stack with embeddings, a vector database, and retrieval infrastructure. However, many enterprise datasets are not collections of long documents—they are structured… more