RAG systems that answer with citations from your own data.

Retrieval-Augmented Generation (RAG) is an AI architecture in which a language model's answers are grounded in specific documents retrieved at query time, rather than relying solely on what the model learned during pre-training. The practical difference is significant: a standard LLM can tell you generally how refund policies work, but a RAG system can tell you specifically what your company's refund policy says — because it retrieves your policy document and uses it as the source for its answer, then cites it. RAG systems are the foundation for knowledge base chatbots, internal Q&A tools, customer documentation assistants, and any AI application where accuracy on company-specific information matters more than general knowledge.

Building a RAG system that works reliably in production requires more than connecting a vector database to an LLM. The critical engineering decisions are chunking strategy (how documents are split before embedding — poor chunking destroys retrieval accuracy), embedding model selection (which model turns text into vectors that can be meaningfully compared), retrieval architecture (pure vector search versus hybrid vector + keyword search, which performs significantly better on precise queries), and re-ranking (a second-pass model that reorders retrieved chunks by relevance before passing them to the LLM). Nowgray implements all of these correctly and validates accuracy against a test set of real questions before deployment — not just the easy ones.

The content sources for a RAG system are typically distributed across multiple tools: documentation in Confluence or Notion, support tickets in Freshdesk or Zendesk, files in Google Drive or SharePoint, and structured data in databases. We build ingestion pipelines that connect to these sources, process and embed the content, and keep the index current as content changes — so the system answers based on the documentation that exists today, not last quarter's version. Access control is maintained throughout: if a user shouldn't see a document in the source system, they won't see it surfaced in an AI answer. This permission-aware retrieval is essential for any RAG system deployed in a business context where different users have different data access rights.