---
title: "RAG Chatbot & Knowledge Assistant Development"
description: "We build retrieval-augmented (RAG) chatbots and knowledge assistants that answer questions accurately from your documents, manuals, and support data — with citations."
canonical_url: "https://mercurystk.com/services/rag-chatbot-development"
last_updated: "2026-06-29T04:31:09.798Z"
---

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## Answers from your knowledge — not guesses

A **retrieval-augmented generation (RAG)** assistant connects a language model to
*your* documents, so it answers real questions with real sources. It's the
difference between a chatbot that frustrates customers and a knowledge assistant
your team and customers actually trust.

We build the full pipeline — ingestion, retrieval, citations, permissions, and
evaluation — and deploy it where your users already are: your website, an internal
portal, or Slack/Teams.

## Who this is for

If your answers live in PDFs, manuals, policies, a help center, or a pile of
support tickets, a RAG assistant turns that scattered knowledge into instant,
accurate responses.

## What we build

A production-grade assistant with the unglamorous parts done right: clean
ingestion, grounded retrieval, citations, access control, and measurable quality.

## Example use cases

Customer support, internal help desks, sales enablement, and document search are
the highest-ROI starting points.

## How we work

Discovery → content audit → ingestion pipeline → retrieval & citations →
evaluation → deploy → monitor question gaps → improve.

## Frequently asked questions