Enterprise RAG Floor

Based on community signals so far, Enterprise RAG Floor refers to the foundational infrastructure and practices that enterprises adopt to implement retrieval-augmented generation (RAG) using their proprietary data. The core idea is moving away from relying solely on general-purpose large language models toward systems that can access and reason over internal documents, databases, and knowledge bases. This shift is driven by the need for differentiated AI that understands company-specific context, maintains data privacy, and reduces hallucination risks. The 'floor' implies a minimum viable setup—often including a vector database, embedding model, LLM, and retrieval pipeline—that organizations can build upon. It addresses the problem that off-the-shelf models lack access to private enterprise data, limiting their usefulness for internal tasks like customer support, compliance, or product knowledge. While the term is still emerging, it signals a growing consensus that enterprise AI value comes from grounding models in proprietary information rather than generic knowledge.

The term is gaining traction as enterprises shift from experimenting with general LLMs to deploying RAG systems on proprietary data for differentiated AI, driven by needs for accuracy, privacy, and competitive advantage.