Enterprise RAG Assistant

A secure enterprise knowledge assistant designed to retrieve trusted information from internal documents, enforce role-based access to content, and return citation-grounded answers instead of relying on unsupported model memory.

Business Problem

Organizations often have critical information distributed across policies, procedures, reports, internal documentation, and other knowledge sources. Employees spend time searching manually, while general-purpose AI tools may not have access to the right internal context or may return answers without reliable evidence.

Solution

The Enterprise RAG Assistant combines dense retrieval, BM25 keyword search, reranking, access control, and answer grounding in a single workflow. Queries are filtered according to the user's role before relevant content is retrieved, and answers are returned with citations to the supporting source material.

Key Features

• Hybrid retrieval — combines semantic vector search with BM25 keyword retrieval
• Reranking — reorders retrieved evidence before generation to improve answer relevance
• Role-based access control — restricts retrieval to content authorized for the current user
• Citation-grounded responses — answers reference the retrieved evidence used to generate the response
• Abstention behavior — avoids confidently answering when supporting evidence is insufficient
• Configurable model backend — supports OpenAI or locally served Ollama models depending on deployment requirements
• Operational controls — includes authentication, rate limiting, and audit-oriented application controls

Measured Results

These results were measured on the project's frozen evaluation benchmark and are presented as system evaluation metrics rather than general claims about all deployments.

How It Works

1. The user authenticates and submits a question.
2. The system applies role-based filtering to determine which knowledge sources are accessible.
3. Dense retrieval and BM25 search independently find potentially relevant passages.
4. Results are combined and reranked to prioritize the strongest evidence.
5. The language model generates an answer using the retrieved context.
6. The final response includes supporting citations, or abstains when the available evidence is insufficient.

Technologies Used

Use Cases

• Internal knowledge search across policies, procedures, manuals, and operational documentation
• Enterprise Q&A where answers need evidence from approved internal sources
• Role-aware assistants for organizations where different teams require different document access
• Research and document review where users need faster access to supporting passages and citations
• Private deployment options using locally served models when organizational requirements favor local inference