RAG Knowledge Base

Most teams start their AI journey by trying to feed all their documents into a model. RAG turns that intuition into an architecture: store embeddings, retrieve the relevant chunks at query time, and let the model answer with context.

We deploy the vector store, build the ingestion pipeline in n8n so it can be inspected and modified without code changes, and tune the chunking strategy to match your documents — long-form policy text and short FAQ snippets need different handling.