🎯 Project Overview

UniAssist Pro is an AI-powered student support system designed to streamline university services by integrating multiple systems through an Enterprise Service Bus (ESB) architecture.

It enables students to interact with a centralized chatbot for academic queries, reducing manual effort and improving efficiency.

🎓 DBIM MVP Implementation

An intelligent student support chatbot demonstrating enterprise-grade architecture for a hackathon MVP

---

🚀 Features

• Chatbot-based support
• Academic assistance
• User-friendly interface

🛠️ Tech Stack

• Python
• Flask / FastAPI (if used)
• HTML, CSS, JavaScript

👥 Team Contribution

This project was developed as part of a team-based implementation of an AI-powered student support system.

My Role:

• Led the team as Team Lead, coordinating development and ensuring project alignment
• Defined and structured the system requirements and overall solution architecture
• Contributed to designing the AI-driven student support workflow
• Actively worked on project documentation and presentation (PPT)
• Supported implementation planning and system integration decisions

📋 Quick Start (30 seconds)

# 1. Install dependencies
pip install -r requirements.txt

# 2. Run the server
uvicorn app.main:app --reload

# 3. Open in browser
# API Docs: http://localhost:8000/docs

Demo Login:

• Username: sarah.johnson@techedu.edu
• Password: demo123

---

🎯 What This MVP Does

Feature	Description	Status
✅ Student Authentication	JWT-based login system	Mocked (demo users)
✅ Submit Queries	Natural language questions	Working
✅ AI Response	Intent classification + response	Rule-based (mocked)
✅ Unified Student Data	Aggregated from multiple systems	Mocked (ESB pattern)
✅ Query Logging	Track all interactions	In-memory
✅ Admin Analytics	View metrics and KPIs	Working
✅ ESB Integration	Enterprise service bus demo	Simulated

---

🏗️ Architecture Overview

┌─────────────────────────────────────────────────────────────────────────┐
│                           CLOUD LAYER                                    │
│  ┌───────────────┐  ┌───────────────┐  ┌───────────────┐               │
│  │  API Gateway  │  │  AI Service   │  │   Analytics   │               │
│  │   (FastAPI)   │  │ (Rule-based)  │  │   Dashboard   │               │
│  └───────┬───────┘  └───────┬───────┘  └───────┬───────┘               │
└──────────┼──────────────────┼──────────────────┼────────────────────────┘
           │                  │                  │
           └──────────────────┼──────────────────┘
                              │
┌─────────────────────────────▼───────────────────────────────────────────┐
│                    ENTERPRISE SERVICE BUS (ESB)                          │
│         • Message Routing  • Data Transformation  • Security            │
└─────────────────────────────┬───────────────────────────────────────────┘
                              │
    ┌─────────────┬───────────┼───────────┬─────────────┐
    │             │           │           │             │
┌───▼───┐   ┌─────▼─────┐ ┌───▼───┐ ┌─────▼─────┐ ┌─────▼─────┐
│Admiss-│   │ Academic  │ │Financ-│ │  Housing  │ │ Directory │
│ ions  │   │  Records  │ │  ial  │ │  System   │ │ Services  │
│(SOAP) │   │  (JDBC)   │ │(REST) │ │  (REST)   │ │  (LDAP)   │
└───────┘   └───────────┘ └───────┘ └───────────┘ └───────────┘
           [============ ON-PREMISE SYSTEMS ============]

---

📁 Project Structure

student-support-system/
├── app/
│   ├── __init__.py
│   ├── main.py                 # FastAPI application & routes
│   ├── models/
│   │   └── schemas.py          # Pydantic data models
│   ├── services/
│   │   ├── auth_service.py     # JWT authentication
│   │   ├── ai_service.py       # Intent classification & responses
│   │   ├── esb_service.py      # ESB integration layer
│   │   └── analytics_service.py # Metrics & logging
│   └── data/
│       └── mock_database.py    # In-memory mock data
├── requirements.txt            # Python dependencies
├── Dockerfile                  # Optional container build
└── README.md                   # This file

---

🔍 What is Mocked (and Why)

Component	MVP Implementation	Production Implementation
AI/NLP	Keyword matching + templates	GPT-4 / Azure OpenAI
Database	In-memory Python dicts	PostgreSQL + Snowflake
Authentication	JWT with mock users	Azure AD / Okta SSO
ESB	Simulated integration	MuleSoft / Azure Service Bus
Analytics	In-memory logging	Azure Event Hubs + Power BI

Why Mock?

• ✅ No API keys required
• ✅ Zero cost for demo
• ✅ Instant setup
• ✅ Predictable behavior for presentation
• ✅ Focus on architecture, not external services

---

🚀 How It Scales to Enterprise

Horizontal Scaling

Load Balancer
     │
     ├── API Instance 1 (Azure App Service)
     ├── API Instance 2 (Azure App Service)
     └── API Instance N (Auto-scaled)
            │
         Redis Cache (Azure Cache)
            │
         ESB Gateway (MuleSoft)
            │
    ┌───────┴───────┐
    │               │
On-Premise      Cloud Data
Systems         Warehouse

Key Scaling Strategies

1. API Layer: Azure App Service with auto-scaling (0-100+ instances)
2. AI Processing: Azure OpenAI with rate limiting and queuing
3. Data: Read replicas for queries, write to primary
4. Cache: Redis for frequently accessed student profiles
5. ESB: Message queues for async processing

---

🔌 ESB (Enterprise Service Bus) Explained

The ESB decouples cloud services from on-premise legacy systems:

Why ESB?

• Protocol Translation: Convert SOAP/XML to REST/JSON
• Security: Single authentication point
• Reliability: Retry logic, circuit breakers
• Monitoring: Centralized logging
• Compliance: Data masking for FERPA/PCI

Data Flow Example

Student asks: "When is my financial aid disbursement?"

1. API receives query → JWT validated
2. ESB routes to Financial Aid system (on-premise)
3. ESB transforms response (XML → JSON)
4. AI service generates personalized response
5. Analytics logs the interaction
6. Response returned to student

---

☁️ Hybrid Infrastructure

Cloud Components (Azure/AWS)

• API Gateway & Load Balancers
• AI/ML Processing
• Analytics & Dashboards
• Caching (Redis)
• CDN for static assets

On-Premise Components (Must remain local)

• Admissions System - FERPA compliance
• Academic Records - FERPA compliance
• Financial Aid - PCI-DSS compliance
• Housing System - Institutional data
• Active Directory - Identity management

Why Hybrid?

1. Compliance: FERPA requires educational records on-premise
2. Legacy Systems: Can't easily migrate mainframe systems
3. Security: Sensitive financial data stays local
4. Latency: Some operations need local speed

---

📊 API Endpoints

Endpoint	Method	Description
/	GET	API info and status
/health	GET	Health check
/token	POST	Login (OAuth2)
/api/chat	POST	Submit query to AI
/api/students/{id}	GET	Get student profile
/api/analytics	GET	System metrics
/api/esb/status	GET	ESB integration status
/api/knowledge-base	GET	View FAQ content
/docs	GET	Swagger documentation

---

🧪 Test the MVP

1. Login

curl -X POST "http://localhost:8000/token" \
  -d "username=sarah.johnson@techedu.edu&password=demo123"

2. Ask a Question

curl -X POST "http://localhost:8000/api/chat" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"student_id": "STU2024001", "message": "When is my financial aid disbursement?"}'

3. View Analytics

curl "http://localhost:8000/api/analytics" \
  -H "Authorization: Bearer YOUR_TOKEN"

---

🎤 DBIM Presentation Points

1. Business Problem

"Students spend 15+ minutes per query navigating multiple systems. This costs universities $25 per human-handled inquiry."

2. Solution

"UniAssist provides an AI-powered chatbot that integrates with legacy systems through an ESB, answering 73% of queries automatically."

3. Architecture Value

"Our hybrid architecture keeps sensitive data on-premise for compliance while leveraging cloud AI for scalability."

4. ROI Metrics

"At 73% automation, we project $91,000 monthly savings with 85% ROI in year one."

5. Technical Feasibility

"The MVP demonstrates the complete architecture. Production deployment uses the same patterns with real AI and databases."

---

📝 Running with Docker (Optional)

# Build image
docker build -t uniassist-pro .

# Run container
docker run -p 8000:8000 uniassist-pro

# Access at http://localhost:8000

---

👥 Demo Users

Username	Password	Role	Student ID
sarah.johnson@techedu.edu	demo123	Student	STU2024001
michael.chen@techedu.edu	demo123	Student	STU2024002
emily.rodriguez@techedu.edu	demo123	Student	STU2024003
admin@techedu.edu	admin123	Admin	-

---

⚡ Quick Demo Script

1. Open http://localhost:8000/docs
2. Click "Authorize" → Enter demo credentials
3. Try POST /api/chat with:

   {
     "student_id": "STU2024001",
     "message": "What's my GPA?"
   }

4. Show GET /api/analytics for metrics
5. Show GET /api/esb/status for architecture demo

---

✅ Hybrid cloud with ESB is industry standard
✅ Feasibility - Runs locally in 30 seconds, production-ready patterns
✅ Clean Explanation - Every file has architecture comments
✅ Low Risk - No external APIs, no secrets, no complex setup
✅ High Clarity - Clear separation of concerns, documented flows

---

📊 Project Presentation

You can view the complete project presentation here:

View Presentation

This presentation includes:

• Problem statement
• System architecture
• Solution design
• Business value and impact