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Case study 04 / 19

Enterprise RAG Knowledge Assistant

On-premise LLM assistant redesigned into a retrieval pipeline with document chunking, embeddings, vector retrieval, contextual prompts, and output handling.

Stack
Qwen · LLM · RAG · Embeddings · Vector Search · Backend Inference · Enterprise UI
Enterprise RAG assistant showing retrieved IT security policy document

The problem

Internal operational knowledge was spread across documents, systems, and teams. A generic assistant could answer questions, but without retrieval grounding it risked incomplete context, inconsistent answers, and hallucination in an enterprise environment where reliability matters.

What I built

Architected an on-premise Qwen-powered internal knowledge assistant and redesigned it into a RAG pipeline. The system uses document chunking, embedding generation, vector retrieval, contextual prompt orchestration, backend inference services, and enterprise UI integration.

In use

Enterprise RAG assistant welcome screen
Enterprise RAG assistant welcome screen

Architecture

Documents and SOPs are chunked and embedded into a vector index ahead of time. At question time the backend retrieves the most relevant passages, assembles a grounded prompt for the on-premise Qwen model, and checks the answer before it reaches the enterprise UI. Nothing leaves the company network.

Enterprise RAG architecture: documents are chunked and embedded into a vector index; at question time the backend retrieves passages, assembles a grounded prompt for an on-premise Qwen model and checks the output before the enterprise UI shows it

Open the image for full size

Proof

  1. 01 Designed the assistant for an on-premise enterprise environment rather than a public-only API workflow.
  2. 02 Moved from simple prompting toward retrieval-grounded answers using chunking, embeddings, vector retrieval, and context assembly.
  3. 03 Used Qwen with controlled response/output handling to reduce hallucination risk and make answers easier to integrate into internal workflows.

Result

Improved the architecture of an internal AI assistant from general chat behavior into a more reliable enterprise knowledge workflow with retrieval grounding and clearer backend integration boundaries.