Evidence · E-commerce

E-commerce Platform Scaling

Scaling an e-commerce platform to handle peak-season traffic.

The situation

Triolem's managed e-commerce platform was struggling with seasonal traffic spikes, particularly during Black Friday and holiday seasons. The system would crash under high load, resulting in significant revenue loss. Additionally, their recommendation engine was outdated and not driving conversions effectively.

The approach

We implemented auto-scaling cloud infrastructure, developed an AI-powered recommendation system using machine learning, and established comprehensive monitoring and SRE practices. The solution included CDN optimization, database sharding, and real-time analytics.

What changed

Successfully handled 6x traffic increase during peak times
Increased conversion rates by 35% with AI recommendations
Reduced page load times by 60%
Achieved zero downtime during peak shopping periods

Technology

Frontend

React · Next.js · TypeScript · Tailwind CSS

Backend

Python · Django · PostgreSQL · Redis · Elasticsearch

AI/ML

TensorFlow · Scikit-learn · Apache Spark · Jupyter

Cloud & Infrastructure

AWS EKS · AWS RDS · CloudFront · Auto Scaling Groups

Bring us the hard problem.

Tell us what has to run, and how it has to hold up. We will tell you how we would build it, secure it and operate it.

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