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30DaysOfAZD

1.2 - Enterprise Retail AI Scenario

Today we introduce the enterprise application scenario that will guide our learning journey: an intelligent retail assistant powered by AI.

What You’ll Learn

Section titled “What You’ll Learn”
  • The business context and requirements for an enterprise retail AI application
  • Key features and capabilities of the retail AI assistant
  • Technical architecture components needed to support the application
  • How this scenario connects to AZD templates

Resources

Section titled “Resources”

Before diving in, review these resources:

  1. 📘 Retail Industry Solutions on Azure - Understanding retail-specific Azure capabilities
  2. 📘 Build a RAG-based chatbot - Core pattern for conversational AI
  3. 🔗 Contoso Retail Sample - Reference implementation on GitHub

The Business Scenario

Section titled “The Business Scenario”

Contoso Retail is a nationwide retail chain facing common challenges:

  • Customer Service Bottlenecks: Call centers overwhelmed during peak seasons
  • Product Discovery Issues: Customers struggle to find relevant products in vast catalogs
  • Personalization Gaps: Generic recommendations that don’t match customer preferences
  • Employee Training Costs: High turnover requires constant onboarding and training

The Solution: Intelligent Retail Assistant

Section titled “The Solution: Intelligent Retail Assistant”

An AI-powered assistant that provides:

  1. Customer-Facing Chatbot

    • Natural language product search
    • Personalized recommendations
    • Order status inquiries
    • Return and exchange guidance

  2. Employee Support Tool

    • Inventory lookups
    • Policy and procedure assistance
    • Product knowledge on-demand
    • Escalation handling

Application Features

Section titled “Application Features”

Core Capabilities

Section titled “Core Capabilities”

Conversational AI

  • Natural language understanding
  • Multi-turn conversations
  • Context awareness
  • Intent recognition

Product Search & Discovery

  • Semantic search across product catalog
  • Visual similarity matching
  • Filtering and faceted search
  • Personalized rankings

Recommendation Engine

  • Purchase history analysis
  • Collaborative filtering
  • Trending products
  • Cross-sell and upsell suggestions

Knowledge Retrieval

  • Company policies and procedures
  • Product specifications and comparisons
  • FAQ and troubleshooting guides
  • Real-time inventory status

Advanced Features

Section titled “Advanced Features”

Multimodal Interaction

  • Text and voice input
  • Image-based product search
  • Video demonstrations
  • AR try-on capabilities

Sentiment Analysis

  • Customer satisfaction tracking
  • Issue escalation triggers
  • Feedback collection
  • Quality monitoring

Analytics & Insights

  • Conversation analytics
  • Popular product trends
  • Customer pain points
  • Performance metrics

Technical Requirements

Section titled “Technical Requirements”

To build this application, we need:

AI Services

Section titled “AI Services”
  • Large language model (GPT-4)
  • Embedding model for semantic search
  • Content moderation
  • Speech services (optional)

Data & Search

Section titled “Data & Search”
  • Vector database for product embeddings
  • Traditional search for structured data
  • Document storage for policies/procedures
  • Session state management

Application Layer

Section titled “Application Layer”
  • API gateway
  • Backend services
  • Authentication & authorization
  • Rate limiting & caching

Monitoring & Operations

Section titled “Monitoring & Operations”
  • Application insights
  • Cost tracking
  • Performance monitoring
  • Error tracking and alerting

Architecture Preview

Section titled “Architecture Preview”

While we’ll explore the detailed architecture in Day 4, here’s a high-level view:

User → Frontend → API Gateway → Backend Services
                                    ↓
                    ┌───────────────┴──────────────┐
                    ↓                              ↓
            AI Services                    Data Services
         (Foundry/OpenAI)              (Search, Database)

Connection to AZD Templates

Section titled “Connection to AZD Templates”

This retail scenario is complex, requiring multiple Azure resources working together. Manually provisioning and configuring these resources would be:

  • Time-consuming (hours or days)
  • Error-prone (configuration mistakes)
  • Inconsistent (different environments diverge)
  • Undocumented (knowledge locked in individual minds)

This is where AZD templates shine! An AZD template for this scenario would:

  • Provision all required Azure resources
  • Configure them to work together
  • Set up security and networking
  • Deploy application code
  • All with a single command: azd up

Real-World Applications

Section titled “Real-World Applications”

This retail scenario pattern applies to many industries:

  • Healthcare: Patient support chatbots with medical knowledge bases
  • Financial Services: Intelligent banking assistants for account inquiries
  • Manufacturing: Technical support for equipment troubleshooting
  • Education: Tutoring systems with personalized learning paths

Ask Copilot

Section titled “Ask Copilot”

Explore the scenario further by asking:

  1. “What are the key differences between building a customer-facing chatbot versus an employee support tool in terms of security and features?”
  2. “How would you handle sensitive customer data like payment information in a retail AI assistant?”
  3. “What are the main challenges in integrating a conversational AI system with existing retail inventory and order management systems?”
Section titled “Related Resources”

Next: Day 3 - App Development Lifecycle

Tomorrow we’ll explore the complete application development lifecycle using a helpful apartment analogy.