Retrieval-Augmented Generation (RAG) connects your LLMs to your own documents, databases, and knowledge bases at inference time. Instead of relying on what a model was trained on, RAG retrieves the most relevant, current information from your data and uses it to generate accurate, sourced, and auditable responses.
Out of the box, LLMs generate answers from their training data, which is static, generic, and knows nothing about your business. RAG closes that gap by giving the model access to your actual documents, policies, product data, and operational knowledge at the moment it generates a response.
When a user asks a question, the system first retrieves the most relevant documents or data chunks from your knowledge base using vector search (semantic similarity) and keyword matching. Those retrieved chunks are then injected into the LLM prompt as context, and the model generates a response grounded in that evidence. The result is an answer that cites its sources and reflects your actual data.
RAG reduces hallucinations by grounding outputs in real documents. It keeps answers current without retraining. It makes AI outputs auditable because every answer points to its source. And it respects your access controls, ensuring users only retrieve documents they are authorized to see.
In 2026, hybrid RAG (combining vector search and keyword matching) is the production standard for enterprise deployments, with agentic RAG patterns emerging for complex, multi-step workflows.
Agents that answer customer questions from your product docs, FAQs, and policies. Accurate answers with citations, not generic responses.
Employees ask questions about company SOPs, HR policies, technical documentation, and get sourced answers instantly.
Search and synthesize across contracts, regulations, and compliance documents. Surface relevant clauses and requirements on demand.
Natural-language search across your product catalog, specs, and inventory. Power recommendation engines and sales tools.
Query earnings reports, financial filings, and market data. Summarize findings with sourced evidence.
Developer-facing search across codebases, architecture docs, runbooks, and API references. Context-aware and always current.
Tell us about your knowledge base and the questions your team needs answered. We will build a RAG system that delivers accurate, sourced responses from your own data.