RAG DevelopmentAI That Knows Your Documents.

RAG (Retrieval-Augmented Generation) is the work of making an AI model answer from your documents instead of from its training data. The work is the ingestion, the chunking, the embeddings, the retrieval, the reranking, the prompt, the eval suite and the guardrails. The prompt is one component, the system is the product.

We build RAG systems with the ingestion, the chunking, the embeddings, the retrieval, the reranking, the eval suite, the guardrails and the cost controls as part of the architecture from sprint one. The output is a RAG system that answers correctly, cites the source and is safe enough to ship to a customer.

This is the wrong engagement if the documents are not yet in shape, or if the use case is classification (not question-answering). We will say so on the call.

• Cited Answers — Every answer cites the source document, with the page or the section the user can verify.
• Current & Traceable — The answer is current, traceable to the source, and audit-logged for the compliance review.
• Access Control — Per-user access control, with the documents filtered to the user the answer is for.
• Cost-Defensible — Token budgets, caching, batching and the cost review that keeps the bill honest.