Enterprise Knowledge Management with Azure AI

Transforming Internal Documentation into an Intelligent Assistant

Executive Summary

When a large organization struggled with new customer service representatives spending weeks searching through scattered documentation, they needed a modern solution. I designed and implemented a RAG (Retrieval-Augmented Generation) system that transformed their static knowledge base into an intelligent, conversational assistant that answers questions instantly and accurately.

Challenge

The organization faced a common but costly problem: critical business knowledge was trapped in hundreds of documents, wikis, and training materials. New customer service representatives spent their first several weeks struggling to find answers, leading to:

• Extended Onboarding: New reps took 3-4 weeks to become productive
• Inconsistent Responses: Different reps gave different answers to similar customer questions
• Information Overload: Searching through dozens of PDFs and documents for simple answers
• Knowledge Gaps: Valuable tribal knowledge wasn't documented or was hard to find

Solution

I developed a comprehensive RAG solution that combines the power of Azure's AI services with a user-friendly chat interface:

Architecture Components

• Azure AI Search: Vector store for semantic document search with hybrid retrieval
• Azure OpenAI Service: LLM for natural language understanding and response generation
• LibreChat Interface: Familiar chat UI for intuitive user interaction
• Custom .NET API: Orchestration layer handling retrieval, inference, and response streaming
• Azure Entra SSO: Seamless authentication integrated with existing corporate identity

Technical Implementation

Document Processing Pipeline

I built a Python ETL pipeline that:

• Ingests documents from multiple sources (PDFs, Word docs, internal wikis)
• Chunks content intelligently while preserving context
• Generates embeddings using Azure OpenAI's embedding models
• Indexes content in Azure AI Search with metadata preservation

Retrieval and Response Flow

1. User asks question in natural language through LibreChat interface
2. Custom API processes query and generates search embedding
3. Hybrid search (vector + keyword) retrieves relevant document chunks
4. Context is passed to Azure OpenAI with carefully crafted prompts
5. Response streams back to user with source citations

Key Features

• Semantic Search: Understands intent, not just keywords
• Source Attribution: Every answer includes links to source documents
• Follow-up Handling: Maintains conversation context for clarifying questions
• Role-Based Access: Respects document permissions through Azure Entra integration

Results

The POC demonstrated immediate value even before full production deployment:

• Instant Answers: Reduced average search time from 15-20 minutes to under 30 seconds
• Accurate Information: Responses directly quote source materials with citations
• Onboarding Efficiency: New reps can self-serve common questions immediately
• Knowledge Preservation: Surfaced valuable information previously buried in documentation

Technical Skills Demonstrated

• Azure AI Search configuration and vector indexing
• Azure OpenAI integration and prompt engineering
• Python ETL development for document processing
• .NET Core API development with streaming responses
• LibreChat deployment and customization
• Enterprise SSO integration with Azure Entra
• Hybrid search optimization (vector + keyword)
• Embedding generation and similarity search

Conclusion

This project showcases how modern AI technologies can transform static documentation into dynamic, accessible knowledge. By combining Azure's powerful AI services with thoughtful architecture design, I delivered a solution that makes institutional knowledge instantly accessible to those who need it most.