RAG Development Services

Retrieval-augmented generation (RAG) answers a question by finding relevant passages in your approved content, then asking a language model to draft a reply that cites those sources. It is how assistants stay grounded in manuals, policies, product data, and support history instead of general training data alone.

Fine-tuning changes model behavior; RAG keeps answers tied to documents you control and can update. Most business knowledge systems start with RAG because citations, permissions, and refresh cycles map to how teams already govern content. Fine-tuning may help later for tone or format, but it does not replace authoritative sources.

Common sources include CMS pages, PDFs and office files, wikis, PIM or product catalogs, ticketing history, and internal databases exposed through APIs. We map what is authoritative for each audience, then build ingestion and chunking that respect how your team publishes and archives content.

We maintain evaluation sets of real questions, check that citations point to the right passages, and review failures with your subject-matter experts. Retrieval is re-run when sources change, and we tune chunking and metadata when answers drift. Human review stays in the loop for high-risk topics.

Retrieval runs with the same identity and roles your staff already use. Partner, customer, and internal corpora stay separated at index time and query time. Agents only see documents their login is allowed to read, and audit logs show which sources informed each answer.

A first phase targets one audience and a bounded set of sources, such as a product line, a policy library, or a support queue. We agree on golden questions, wire retrieval and citations, and run a pilot with logging and review before expanding corpora or channels.