E-commerce Recommendations Example

Full Implementation Available to AWS Marketplace Customers Contact Sales | AWS Marketplace

Overview

Build production-ready AI-powered recommendation engines with AxonFlow. This example demonstrates advanced patterns for:

Personalized product recommendations using customer data and AI
Multi-source data integration (inventory, customer history, reviews)
Real-time pricing and availability checks
A/B testing for recommendation algorithms
Performance optimization with caching and parallel queries
Policy-based business rules (pricing tiers, promotions, inventory limits)

Difficulty: Intermediate Time to Complete: 20 minutes Industry: E-commerce, Retail Use Cases: Product recommendations, personalized shopping, upselling, cross-selling

What You'll Build

A production-ready recommendation engine that:

Generates personalized recommendations based on browsing history, past purchases, and preferences
Retrieves product data in parallel from multiple data sources (Snowflake, PostgreSQL, Redis)
Applies business logic via policies (inventory limits, pricing tiers, promotional rules)
Uses AI for intelligent suggestions (AWS Bedrock, OpenAI)
Optimizes performance with caching and batch processing
Provides A/B testing for different recommendation strategies

Architecture

Customer Request → AxonFlow Agent → Policy Enforcement → Data Sources
                          ↓                ↓                    ↓
                   License Check    Business Rules      MCP Connectors
                          ↓                ↓                    ↓
                   A/B Test Split   Inventory Check    (Snowflake, PostgreSQL)
                          ↓                ↓                    ↓
                   AWS Bedrock      Pricing Rules       Product Data
                          ↓                                     ↓
                   AI Recommendations ←─── Results ←──────────┘
                          ↓
                   Cache Results → Response → User
                          ↓
                   Analytics Event Log

Key Features

1. AI-Powered Personalization

Leverage machine learning for intelligent recommendations:

Browsing behavior analysis - Track user interests
Purchase history patterns - Predict future needs
Collaborative filtering - "Customers who bought X also bought Y"
Content-based filtering - Similar product attributes
LLM-enhanced suggestions - Natural language explanations

Full ML recommendation algorithms and implementation available to customers.

2. Multi-Source Data Integration

Query multiple systems in parallel for complete context:

// Example architecture (simplified)
const dataSources = {
  productCatalog: 'Snowflake',      // 10M+ products
  customerData: 'PostgreSQL',       // User profiles, orders
  inventory: 'PostgreSQL',          // Real-time stock
  sessionData: 'Redis',             // Active sessions
  reviews: 'Snowflake',             // Customer reviews
  analytics: 'Snowflake'            // Behavioral data
};

Complete multi-source integration patterns with 5+ connectors available to customers.

3. Business Rules Engine

Enforce complex business logic via policies:

Pricing tiers (bronze, silver, gold, platinum)
Promotional rules (discount codes, flash sales)
Inventory thresholds (exclude low-stock items)
Geographic restrictions (shipping zones)
Customer segmentation (VIP, new, at-risk)

Full policy library with 20+ ecommerce patterns available to customers.

4. A/B Testing Framework

Test recommendation strategies in production:

Traffic splitting - Route users to different algorithms
Performance tracking - Measure conversion rates
Statistical significance - Automated analysis
Multi-variant testing - Test 3+ strategies simultaneously

Complete A/B testing framework with analytics integration available to customers.

5. Performance Optimization

Achieve sub-100ms recommendation latency:

Connection pooling - Reuse database connections
Redis caching - Cache frequent queries (5-10 min TTL)
Parallel queries (MAP) - 10x faster multi-source retrieval
Batch processing - Handle high-volume requests

Full performance optimization guide with benchmarks available to customers.

Performance Benchmarks

Operation	Without AxonFlow	With AxonFlow	Improvement
Single recommendation	250ms	15ms	16x faster
5 parallel queries (MAP)	1,250ms	45ms	27x faster
AI-powered recommendations	3,500ms	2,100ms	1.6x faster
Cached recommendations	N/A	2ms	125x faster

Why faster with AxonFlow?

Connection pooling reduces latency
MAP executes queries in parallel (not sequential)
Built-in caching layer
Optimized data serialization

Full performance optimization playbook available to customers.

Production Features

Dynamic Pricing Engine

Implement real-time pricing adjustments:

// Pricing tiers (example structure)
const pricingLogic = {
  bronze: { discount: 0, perks: [] },
  silver: { discount: 5, perks: ['free_shipping'] },
  gold: { discount: 10, perks: ['free_shipping', 'early_access'] },
  platinum: { discount: 15, perks: ['free_shipping', 'early_access', 'concierge'] }
};

Full dynamic pricing implementation with seasonal adjustments available to customers.

Cross-Sell and Upsell

Maximize order value with intelligent suggestions:

Frequently bought together - Product association rules
Similar products - Feature-based matching
Premium alternatives - Higher-margin upsells
Bundle recommendations - Package deals

Complete cross-sell/upsell algorithms with revenue optimization available to customers.

Inventory-Aware Recommendations

Never recommend out-of-stock products:

Real-time inventory checks - Sub-50ms queries
Multi-warehouse support - Check all locations
Low-stock alerts - Prioritize fast-moving items
Backorder handling - Alternative suggestions

Full inventory integration patterns available to customers.

Cost Analysis

Per 1M Recommendations

Traditional Approach:

Infrastructure: $5,000/month (8 servers, databases, cache)
Development: $200K (6 months @ 2 engineers)
Maintenance: $15K/month (DevOps, monitoring)

AxonFlow Approach:

Platform fee: $15K-50K/month (based on tier)
Development: $40K (4 weeks @ 2 engineers)
Maintenance: Included in platform fee

Savings: $300K+ in year 1, 75% faster time-to-market

Why AxonFlow for E-commerce?

vs Building In-House

Requirement	Build In-House	AxonFlow
Time to production	4-6 months	2-4 weeks
Multi-source queries	Sequential (slow)	Parallel (MAP)
A/B testing framework	2-3 months	Pre-built
Caching layer	Custom Redis	Built-in
Policy engine	Custom build	Production-ready
Total cost (Year 1)	$500K+	$180K-600K

vs Other Platforms

Feature	LangChain	LlamaIndex	AxonFlow
Sub-100ms queries	❌	❌	✅ Guaranteed
Built-in caching	❌ Manual	❌ Manual	✅ Redis
A/B testing	❌ Custom	❌ Custom	✅ Built-in
Multi-source MAP	❌	❌	✅ Unique
Business rules	❌ Custom	❌ Custom	✅ Policy engine
Production support	❌	❌	✅ 24/7

Get Full Implementation

What's Included for Customers

✅ Complete Source Code:

TypeScript and Go implementations
React frontend components
Snowflake/PostgreSQL connectors
Redis caching integration
A/B testing framework

✅ Recommendation Algorithms:

Collaborative filtering
Content-based filtering
Hybrid recommendation models
LLM-enhanced personalization
Cross-sell/upsell logic

✅ Business Logic Templates:

Dynamic pricing policies
Inventory management rules
Promotional campaigns
Customer segmentation
Geographic restrictions

✅ Deployment Resources:

Docker Compose configurations
Kubernetes Helm charts
CloudFormation templates
CI/CD pipeline examples
Monitoring dashboards

✅ Support:

24/7 technical support
Architecture review sessions
Performance optimization guidance
Regular feature updates

Customer Success Stories

"AxonFlow's MAP reduced our recommendation latency from 800ms to 45ms. Conversion rates increased 23% after deployment."

— VP Engineering, Top 50 E-commerce Company

"The built-in A/B testing framework helped us optimize our recommendation algorithm in 2 weeks instead of 3 months. ROI was immediate."

— Head of Data Science, Fashion Retailer

"We saved $400K in Year 1 by not building a custom recommendation engine. AxonFlow's business rules engine was exactly what we needed."

— CTO, Consumer Electronics Marketplace

Live Demo

Interactive Recommendations

Try our live demo to see AxonFlow recommendations in action:

Launch Demo → (Customers only)

Demo Features:

Browse 100K+ products
See personalized recommendations
Test A/B variants
View latency metrics
Inspect policy decisions

Pricing

Professional

$15K/month

Up to 3M recommendations/month
25 users
Email support (12hr SLA)
Basic A/B testing
Standard connectors

Enterprise

$50K/month

Up to 10M recommendations/month
Unlimited users
Priority support (4hr SLA, 24/7)
Advanced A/B testing
Custom connectors
Dedicated success manager
Performance guarantees

View on AWS Marketplace

Next Steps

Contact Sales - Schedule a demo with e-commerce use case
AWS Marketplace - Subscribe directly
Book Demo - See live recommendation engine

Resources

Support

For questions or to access the full implementation:

Sales: [email protected]
Support: [email protected] (customers only)
Documentation: https://docs.getaxonflow.com
AWS Marketplace: Subscribe Now

Full e-commerce implementation with production-ready recommendation algorithms available exclusively to AWS Marketplace customers.

Overview​

What You'll Build​

Architecture​

Key Features​

1. AI-Powered Personalization​

2. Multi-Source Data Integration​

3. Business Rules Engine​

4. A/B Testing Framework​

5. Performance Optimization​

Performance Benchmarks​

Production Features​

Dynamic Pricing Engine​

Cross-Sell and Upsell​

Inventory-Aware Recommendations​

Cost Analysis​

Per 1M Recommendations​

Why AxonFlow for E-commerce?​

vs Building In-House​

vs Other Platforms​

Get Full Implementation​

What's Included for Customers​

Customer Success Stories​

Live Demo​

Interactive Recommendations​

Pricing​

Professional​

Enterprise​

Next Steps​

Resources​

Support​