Enterprise Knowledge Assistant with Source-Grounded RAG

Project overview

The Enterprise Knowledge Assistant turns policies, manuals, and internal documents into a searchable knowledge experience. Users can ask natural-language questions and receive answers based on retrieved source passages.

Problem

Enterprise information is frequently fragmented across policies, manuals, and internal documents, making reliable answers slow to find.

Solution

Built a retrieval-augmented assistant that converts documents into searchable knowledge, retrieves the most relevant passages, and generates answers with clear source grounding.

• Configurable document ingestion and chunking
• Vector embeddings and semantic retrieval
• Context-aware question answering
• Source citations and grounded response generation

Technology stack

My role

I developed the backend services, REST APIs, ingestion and chunking workflow, vector retrieval, prompt context strategy, quality controls, and React-based conversational interface.

Business impact

The solution provides a foundation for faster information discovery while reducing the risk of unsupported AI responses through retrieval controls and visible grounding.

A scalable foundation for secure enterprise Q&A that improves information discovery without presenting generated content as an unsupported fact.