AI Customer Support Bot

Project Overview

An enterprise-grade AI chatbot that understands context, learns from your documentation, and provides accurate, helpful responses to customer queries 24/7. Unlike traditional chatbots, this solution uses RAG to ground responses in your actual knowledge base, eliminating hallucinations.

The system integrates seamlessly with existing support workflows, escalating to human agents when needed and continuously improving through feedback loops.

Key Features

• RAG-Powered Responses

  Retrieves relevant documents and generates accurate, grounded answers

• Multi-Language Support

  Automatically detects and responds in the customer's language

• Sentiment Analysis

  Detects customer frustration and escalates appropriately

• Knowledge Base Ingestion

  Automatically indexes PDFs, docs, websites, and databases

• Conversation Memory

  Maintains context across the entire conversation session

• Analytics Dashboard

  Track resolution rates, popular topics, and customer satisfaction

Technical Implementation

The architecture combines Azure OpenAI for language understanding with a vector database for semantic search. Documents are chunked, embedded, and indexed for fast retrieval. The orchestration layer manages conversation flow, retrieval, and response generation.

Azure OpenAI

.NET Core

Python

React

Architecture Highlights

• RAG Pipeline: Document ingestion → Chunking → Embedding → Vector storage → Retrieval → Generation
• Vector Database: Azure AI Search with vector indexing for semantic similarity
• LLM Orchestration: Semantic Kernel for prompt management and chain-of-thought reasoning
• Guardrails: Content filtering and response validation to prevent harmful outputs
• Human Handoff: Seamless escalation to live agents with conversation context