Enterprise Context-Aware RAG Agent

Built an Enterprise Context-Aware RAG Agent in n8n designed to provide intelligent, context-aware answers using information retrieved from a dedicated business knowledge base.

The system combines a conversational AI agent with Retrieval-Augmented Generation (RAG), allowing it to search relevant stored knowledge before generating a response. Instead of relying only on the language model’s general knowledge, the agent retrieves contextual information from a Supabase vector store and uses it to produce more relevant answers based on company-specific data.

Embeddings are used to convert stored knowledge and user queries into searchable vectors, enabling semantic retrieval of information based on meaning rather than simple keyword matching. Conversation memory also allows the agent to preserve context across interactions and provide more coherent responses during ongoing discussions.

The architecture can be adapted for internal knowledge assistants, customer support, company documentation, employee resources, and other enterprise use cases.

Key capabilities include RAG, vector search, semantic retrieval, Supabase integration, embeddings, conversational memory, contextual AI responses, knowledge-base integration, and n8n orchestration. The result is a scalable AI knowledge assistant that makes business information easier to access through natural conversation.