RAG Pipeline Development
We build retrieval-augmented generation pipelines that answer from your data with citations — not pipelines that hallucinate past a demo. Our founder is a Senior Software Engineer at Qdrant, so production vector search is what we do every day, not a side project.
What's included
- Chunking and embedding strategy tuned to your content, not a generic default
- Vector store selection and deployment — Qdrant, pgvector, or Pinecone
- Hybrid retrieval (keyword + vector) and re-ranking for precision
- Access-controlled retrieval so users only see what they're permissioned for
- Citation-grounded answers and hallucination guardrails
- Evaluation harness so retrieval quality is measured, not assumed
When RAG is the right call
RAG fits when your knowledge changes often, needs citations, or is permissioned by user or team. If you're instead trying to teach a model a consistent format or behavior, fine-tuning is usually the better tool — see our breakdown in RAG vs Fine-Tuning: When to Use Which.
How we work
Weekly sprints, working retrieval demos from week one, first production release in 4-8 weeks. We instrument retrieval quality from day one so you can see precision and recall improve sprint over sprint, not just trust that it's working.
RAG is one piece of a larger AI system — see our full AI development service, or how we handle LLM integration once retrieval is solid.