RAG development — answers grounded in your real documents

A real, live example

tunovix.com/api/ai-search is a genuine RAG endpoint over this entire site's own content — services, pricing, blog posts, case studies — built on Cloudflare's AI Search product. Query it with a natural-language question and it returns ranked, relevant chunks with a real relevance score, grounded in what's actually published here, not a paraphrase from general training data. It's built for AI agents/tools to query directly, not for a human search box — see llms.txt for how it's advertised to agents, or the API reference for a real example request/response.

Where RAG actually matters

The clearest case is internal knowledge: "what does our contract with this client say," "what's the status of this project," "what did we decide about this feature" — questions with a real, specific answer that lives in a document somewhere, not something an LLM should ever be guessing at. See the internal company knowledge agent use case for the full pattern: Google Drive, Notion, Slack, a database, and internal APIs, all searched and cited, not summarized from memory.

Grounded answers cite their source

A RAG agent that gives confident answers with no way to check where they came from just moves the trust problem somewhere less visible. Every build includes source attribution — which document, which record — so an answer can actually be verified, not just trusted on faith. This is part of the same guardrails-and-evals discipline every agent ships with.

Cost and timeline

A RAG-grounded agent over an existing knowledge base typically runs $1,850–$3,750 and 2-4 weeks — see the full cost breakdown by agent type, or run the calculator against your actual document sources.