Build intelligent commerce systems with Shopify integration, dynamic pricing, inventory automation, fraud detection, and customer support agents
TAM by 2030
Course Price
Students (Year 1)
Achieve ROI Year 1
15 sessions × 90-120 minutes each (22.5-30 hours total)
Live cohort-based + real Shopify store integration + hands-on projects
Python (intermediate), basic understanding of e-commerce, REST APIs
Certificate + Production Shopify app with AI agents
Agentic commerce fundamentals, Shopify integration, first AI shopping assistant
Market Opportunity Assessment: Analyze your e-commerce business/idea, use ROI calculator spreadsheet, identify top 3 use cases, calculate potential cost savings and revenue impact.
Business case with 3 prioritized use cases and 90-day roadmap
Product Search Tool: Build command-line tool that connects to Shopify API, fetches product catalog, implements search by title/description/tags, handles pagination, implements error handling and rate limiting.
Product search tool with filtering, inventory checks, webhook integration
Shopping Assistant: Build product Q&A agent with tools for product search, details lookup, inventory check, recommendations. Implement conversation memory, system prompt for personality, handle edge cases.
Complete shopping assistant with 5+ tools and conversation memory
Recommendations, dynamic pricing, inventory automation
Implement collaborative filtering and content-based filtering. Build hybrid recommendation system. Create real-time API with Redis caching. Measure with precision, recall, conversion metrics.
Exercise: Build recommendation API with endpoints for similar products, personalized recommendations, trending items. Implement caching and A/B testing framework. Target: 15-20% conversion lift.
Build dynamic pricing agent optimizing for revenue and margin. Implement competitor price monitoring. Use reinforcement learning (Q-learning, multi-armed bandits). Design A/B testing framework for pricing strategies.
Exercise: Create multi-strategy pricing system with rule-based baseline, demand elasticity model, competitor monitoring, RL agent, and business constraints.
Build demand forecasting with time series analysis (ARIMA, Prophet). Implement automated replenishment logic. Calculate optimal reorder point and safety stock. Integrate with supplier APIs for automated purchase orders.
Exercise: Create inventory intelligence system with Prophet forecasting, replenishment logic, automated triggers, Shopify integration, and simulation for backtesting.
Support automation, cart recovery, multi-agent orchestration
Build multi-tier support system with AI triage and escalation. Implement RAG for knowledge base queries. Create sentiment analysis for satisfaction tracking. Design seamless handoff to human agents.
Exercise: Build support bot with specialized agents (Order Status, Product Questions, Returns/Refunds, Technical Support), intent routing, sentiment analysis, escalation logic. Target: 70-80% resolution rate, 30-70% cost reduction.
Build predictive models to identify high-risk abandonment before it happens. Implement personalized recovery campaigns (email, SMS, push). Create AI agent optimizing offer timing and discount levels. Design A/B testing framework.
Exercise: Build cart recovery system with abandonment prediction model, customer segmentation (4 segments), multi-channel messaging (SendGrid, Twilio), timing optimizer, A/B testing. Target: 20-35% recovery rate.
Build complex multi-agent systems using CrewAI or LangGraph. Implement agent collaboration patterns (sequential, parallel, hierarchical). Create specialized agents working together. Design monitoring and observability.
Exercise: Build 4-agent CX system (Triage, Order Specialist, Product Expert, Billing Agent) with orchestration, shared memory, error handling, monitoring. Track resolution rate per agent, response latency, tool calls.
Stripe payments, Shopify apps, multi-platform architecture
Integrate Stripe AI Agent Toolkit for autonomous payment operations. Build agents handling subscriptions, refunds, billing inquiries. Implement usage-based billing for AI consumption. Create payment dispute automation.
Exercise: Build payment agent system with tools for payments, refunds, subscriptions, usage-based billing, dispute handling. Implement idempotency, error handling, webhook verification.
Build and deploy Shopify app with embedded AI agents. Implement app authentication and webhooks. Create app UI with Shopify Polaris design system. Submit to Shopify App Store for "Built for Shopify" certification.
Exercise: Create production Shopify app (choose: Smart Support, Cart Recovery, Product Recommender, or Price Optimizer). Setup OAuth, build Polaris UI, integrate AI agent, subscribe to webhooks, implement billing.
Architect headless commerce systems with AI agents. Integrate agents across multiple platforms (Shopify, WooCommerce, BigCommerce). Implement API gateway pattern. Build platform-agnostic agents using abstraction layers.
Exercise: Build multi-platform system with 3 adapters (Shopify, WooCommerce, BigCommerce), unified data models, abstraction layer, platform-agnostic AI agent, API gateway, sync engine.
Trust & safety, fraud detection, production scaling, cost optimization
Build fraud detection system using ML and anomaly detection. Implement content moderation for reviews and listings. Create seller verification and KYC automation. Design dispute resolution agents for marketplace transactions.
Exercise: Build trust & safety system with 4 modules: Fraud Detection (ML model, real-time scoring), Content Moderation (NLP for reviews), Seller Verification (KYC workflow), Dispute Resolution (evidence collection). Target: >90% fraud detection precision.
Optimize LLM costs through caching, prompt engineering, model selection. Implement production monitoring and observability. Design autoscaling architecture. Build cost analysis and ROI tracking systems.
Exercise: Optimize agent system: implement semantic caching, prompt compression, model router, structured logging, custom metrics, autoscaling, performance profiling. Target: 40%+ cost reduction, <3s latency, deploy to cloud.
Student presentations (15 min each): project overview, live demo, technical architecture, integration challenges, business impact analysis, Q&A.
Ship a production-ready AI agent system for e-commerce/marketplace demonstrating real business value with measurable ROI.
Python, LangChain/LangGraph, Shopify Admin API, Stripe AI Agent Toolkit, Claude 3.5 Sonnet, Redis, PostgreSQL, SendGrid/Twilio, AWS/Railway
Demonstrate ability to build and deploy AI systems that generate measurable business value. Perfect for landing roles as AI Product Manager, E-commerce Engineer, or founding AI-native commerce startup.
Joshua built and scaled infinite.tcgplayer.com, a developer platform that powers a $1B+ marketplace. He understands the unique challenges of marketplace platforms: multi-vendor coordination, trust & safety at scale, fraud prevention, seller/buyer experience balance, and API platform design. You'll learn from someone who's actually built the systems you want to create, with real-world lessons from managing thousands of developers and millions of transactions.
Expected Completion Rate
Avg Time to Deploy Capstone
Achieve ROI in First Year
# LangChain Shopping Assistant (Session 3 Example)
from langchain.agents import initialize_agent, Tool
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
# Initialize LLM
llm = ChatOpenAI(model="gpt-4", temperature=0)
# Define Shopify API tools
def search_products(query: str) -> str:
"""Search Shopify products by query"""
# Call Shopify API
products = shopify_client.search(query)
return format_products(products)
def get_product_details(product_id: str) -> str:
"""Get detailed product information"""
product = shopify_client.get_product(product_id)
return format_product_details(product)
def check_inventory(product_id: str) -> str:
"""Check product inventory availability"""
inventory = shopify_client.get_inventory(product_id)
return f"Available: {inventory.quantity} units"
# Create tools for agent
tools = [
Tool(name="ProductSearch", func=search_products,
description="Search for products by name or description"),
Tool(name="ProductDetails", func=get_product_details,
description="Get detailed info about a specific product"),
Tool(name="InventoryCheck", func=check_inventory,
description="Check if product is in stock")
]
# Initialize agent with memory
memory = ConversationBufferMemory(memory_key="chat_history")
agent = initialize_agent(
tools, llm, agent="conversational-react-description",
memory=memory, verbose=True
)
# Use agent
response = agent.run("Do you have running shoes under $100?")