# 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.

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### 🎯 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.

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### 🛠️ 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

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### ⚙️ Architecture Diagram

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

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### 📊 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

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### 📈 Impact&#x20;

* **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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