Large language models are most useful in the enterprise when answers are grounded in your policies, product docs, and support history. Retrieval-augmented generation (RAG) does that by fetching relevant chunks before the model responds—reducing hallucinations and keeping responses aligned with source material.
Getting RAG right is as much about data engineering as it is about models: clean chunking, metadata for filtering, re-ranking, and evaluation sets that reflect real user questions. Security matters too—access controls on the index should mirror who may see which documents.
When RAG is the right pattern
RAG fits Q&A over manuals, internal search that answers in sentences, and copilots that cite sources. It is a poor fit when the task is mostly numerical reasoning across fresh transactional data without a clear retrieval story—in those cases, traditional analytics or structured APIs often win.
We help teams design RAG pipelines that fit their cloud or on-prem constraints, measure quality over time, and integrate with existing identity and logging. If you are moving from experiments to a governed internal assistant, we would be glad to map the path with you.