Retrieval-Augmented Generation (RAG)
LLMs make things up. RAG fixes that by retrieving your real documents before answering. We build RAG systems with proper chunking, embedding, reranking, and evaluation, so the answers are grounded and the citations are real.
Overview
RAG sounds simple and rarely is. Chunking strategy, embedding choice, reranker tuning, and prompt design all affect answer quality. 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
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.