RAG as a service: answers grounded in your own data
Retrieval-augmented generation is easy to stand up and hard to make good. The demo works because the test question happens to match a chunk. Then real questions arrive, retrieval pulls the wrong passage, and the model answers confidently from it. Almost every RAG problem that looks like a model problem is a retrieval problem.
Book a strategy callWhat you get
- A chunking and embedding strategy chosen against your documents, not a default copied from a tutorial
- Retrieval you can measure — recall and precision on a real question set, so 'it seems better' becomes a number
- Hybrid search and reranking where they earn their cost, and left out where they do not
- Citations back to source documents, so an answer can be checked rather than trusted
- An ingestion pipeline that handles updates and deletions, not just a one-time backfill
- Monitoring for retrieval quality drift as your corpus grows
How it runs
- 01
Audit the corpus
What the documents actually look like decides the chunking strategy. Contracts, tickets and marketing pages want different treatment.
- 02
Build the question set
A set of real questions with known-good sources. Without this there is no way to tell whether a change helped.
- 03
Tune retrieval
Chunking, embeddings, hybrid search, reranking — measured against the question set at each step rather than adjusted by feel.
- 04
Ship and watch
Ingestion for ongoing updates, citations in the interface, and monitoring so quality drift is visible.
Questions
- What is RAG as a service?
- Having the retrieval layer designed, built, measured and handed over as a single engagement, rather than assembling it from tutorials and hoping. It covers ingestion, chunking, embeddings, retrieval, reranking, evaluation and monitoring.
- Why is my RAG returning irrelevant chunks?
- Usually chunking that ignores document structure, an embedding model mismatched to the domain, or pure vector search where the queries are keyword-shaped. All three are diagnosable once there is a question set to measure against — which is the part most implementations skip.
- Do I need a vector database?
- Not always. At small corpus sizes Postgres with pgvector is usually enough and much less to operate. A dedicated vector database earns its keep at scale or when you need filtering and hybrid search it does better.
- How do you evaluate a RAG pipeline?
- Separately at each stage. Retrieval is measured on whether the right source came back, generation on whether the answer is faithful to what was retrieved. Collapsing them into one score hides which half is broken.