1. Multi-Institution Financial Chatbot – RAG-Powered AI Assistant

📌 Overview

Developed a Retrieval-Augmented Generation (RAG) chatbot using Azure OpenAI to support 15 financial institutions. The assistant provides real-time, context-aware answers to customers by processing over six years of financial reports and handling 50,000+ queries monthly.


🎯 Challenge

  • Customers required accurate, low-latency responses across various banks, each with its own documentation and service policies

  • Support teams were overwhelmed with repetitive inquiries, leading to high call/email volumes and inconsistent service quality

  • A scalable, unified AI solution was needed to distinguish between 15+ institutions and deliver precise, contextual answers in real time.


🛠️ Solution

Technologies Used: Azure OpenAI, Azure Document Intelligence, Power Automate, Custom Chunking & Embeddings, RAG Pipeline Key Features:

  • Processed six years of financial reports from 15 institutions

  • Integrated with Azure Document Intelligence to extract key data from PDFs and scanned reports

  • Used RAG to generate contextual answers, mapping user queries to precise document content

  • Unified chatbot experience for general and institution-specific queries

  • Seamless integration with backend CRM and web UI for deployment


⚙️ Architecture Diagram

User → Chatbot UI → Azure OpenAI (LLM) ↓ RAG Pipeline → Embedded Document Index (15 Institutions) ↓ Contextual Response → Display to User


📊 Analytics & Performance

  • 50,000+ customer queries processed per month

  • 96% response accuracy

  • Response time improved from ~1 minute to under 12 seconds

  • Embedded insights from 6 years of historical financial reports


📈 Impact

  • 40% reduction in manual workload

  • Saved 200+ man-hours/month through intelligent query automation

  • Query resolution latency reduced from ~1 minute to under 12 seconds

  • Unified chatbot experience across all participating financial institutions

  • Reduced frontline support load on high-frequency, document-driven queries

  • Built for future scale – easily extendable to more institutions and data sources

  • Fully aligned with governance practices and Center of Excellence (CoE) automation standards


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