Retrieval-Augmented Generation (RAG)
LLMs make things up. Retrieval-Augmented Generation (RAG) fixes that by retrieving your real documents before answering. Our RAG development services cover the full build — chunking, embedding, reranking, and evaluation — so answers stay grounded and every citation is real.
Overview
RAG sounds simple and rarely is. Chunking strategy, embedding choice, reranker tuning, and prompt design all affect answer quality. Whether you need an enterprise RAG knowledge base, a document-search platform, or a domain-specific assistant, we design the retrieval layer around your actual corpus — not a generic template. RAG also pairs well with AI agents that act on what they retrieve. We've built RAG systems across coaching, research, and document intelligence, and we know where the edge cases bite.
Why choose this service
Responses backed by your source documents, with inline citations for every claim.
Chunking, embedding, and reranking configured for your specific document types.
Evaluation pipelines that catch retrieval regressions before they hit users.
OpenAI, Anthropic, Gemini, or open-source. Qdrant, Pinecone, pgvector. We swap without rewrites.
How we work
Source documents, access control, refresh strategy, and chunking approach.
Embeddings, vector store setup, and metadata filtering.
Query expansion, reranking, prompt design, and streaming generation with citations.
Golden test sets, answer quality evaluation, and drift monitoring in production.
Applications
Technologies
FAQ
RAG is a technique that retrieves relevant information from your own documents and data before the language model generates an answer. Instead of relying on what the model memorised during training, it answers from your real, current sources — with citations you can verify.
By grounding every response in retrieved source material. The model is instructed to answer only from the documents it retrieved, and each claim carries an inline citation — so unsupported answers are visible immediately rather than presented as fact.
RAG is the right choice when your knowledge changes often or must be cited — it updates the moment your documents update. Fine-tuning suits fixed tone, format, or specialised behaviour. Many production systems use both, and we can scope which applies to your case.
A focused proof of concept typically takes weeks, not months. Full production deployment depends on corpus size, access-control requirements, and integration — we scope this before we start.
As few as a dozen documents works for narrow domains. Larger corpora need more tuning on chunking and reranking.
Golden test sets with expected answers, retrieval hit rate, citation accuracy, and blind human reviews.
Explore more
Tell us about your product. We'll tell you how we'd build it, and how fast.