```html Case Study 2 Technical Implementation

EGFFY

E-commerce Personalization Implementation

How we implemented AI-driven product personalization for a global fashion brand.

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Tech Stack

React 18

For dynamic client-side rendering and real-time personalization updates

Node.js

Microservices architecture for real-time recommendation processing

MongoDB

Scalable data storage for user preferences and interaction history

MLflow

For model versioning and AI-powered recommendation tracking

System Architecture
React Web
Recommendation Engine
GraphQL API
Node.js Cluster
3 instances
MLflow
Product Recommender v1.2.7
MongoDB

Technical Implementation Highlights

Real-Time AI Processing

On-device ML inference at the edge using ONNX runtime for sub-200ms response times

Personalization Engine

Distributed architecture with Redis cache for 150k+ real-time recommendation calculations/minute

Secure Data Processing

All user interaction data encrypted in transit and at rest using AES-256/GCM

Implementation Code Examples

class RecommendationEngine {
getRecommendation(user) {
const history = await
recommend = model.predict(
{'category': 'fashion', 'confidence': 0.94, 'products': [1029, 8704]}
}
GraphQL resolver
Query: { recommendations(id: ID!): [Product] }
Resolver: (parent, {id}, {dataSources}) => dataSources.recommender.getForUser(id)
Sklearn Export
from sklearn.pipeline import make_pipeline
model = make_pipeline(Vectorizer(), SGDClassifier())

Implementation Outcomes

The technical implementation achieved an 82% boost in conversion rates with 94% model accuracy and 47ms average response time for recommendations

View Implementation Results
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