Why teams pick RAGBase
Building a RAG system from scratch requires assembling a document parser, an embedding pipeline, a vector database, a retrieval layer, a reranking step, a generation prompt, a citation mechanism, and an evaluation suite. Each component has gotchas that only appear at production scale. A chunking strategy that works on clean PDFs fails on scanned documents. A retrieval approach that works on 10,000 documents degrades on 1,000,000.
RAGBase is a complete, production-tested RAG implementation that deploys as a hosted service or a self-contained installation. The hybrid retrieval model — BM25 for keyword precision, dense vectors for semantic recall, cross-encoder for final ranking — is the configuration that consistently outperforms single-method retrieval on technical documentation, policy documents, and support knowledge bases.
The confidence routing feature is the capability that makes RAGBase appropriate for customer-facing use. A system that answers every query — including the ones where the relevant information is not in the knowledge base — will hallucinate. RAGBase measures retrieval confidence per query and routes low-confidence queries to a human queue, which means the automated responses are accurate and the human queue handles the edge cases.
Who it is for
RAGBase is used by support teams building internal and customer-facing knowledge assistants, legal and compliance teams making large document collections queryable, enterprises replacing keyword search on internal documentation, and product teams adding AI-powered search to their applications.