AI-Powered E-Commerce Platform
Intelligent Recommendations, Dynamic Pricing & Demand Forecasting
Project Overview
A mid-sized fashion retailer was losing revenue to cart abandonment and stockouts. They needed an intelligent layer on top of their existing stack — one that could personalise browsing in real time, adjust prices dynamically, and predict inventory needs weeks in advance.
The Challenges
- 1
Real-time recommendation latency needed to stay under 200ms at peak traffic
- 2
Dynamic pricing had to adjust automatically without harming gross margin
- 3
Inventory forecasting across 500+ SKUs with inconsistent historical data
- 4
Integration with a legacy Shopify storefront with limited API surface
Our Approach
We built a collaborative filtering recommendation engine on TensorFlow, a rules-based dynamic pricing model trained on competitor price signals, and an LSTM demand forecasting pipeline running nightly on AWS Lambda. Everything was exposed via FastAPI and surfaced into the existing Shopify front end with a React layer.
Key Features & Metrics
Real-time product recommendations with sub-200ms latency
Dynamic pricing engine auto-adjusting 500+ SKUs based on demand signals
LSTM demand forecasting with a 4-week inventory look-ahead
A/B testing framework built in for live pricing experiments
Admin dashboard with live revenue impact and model confidence metrics
Conversion rate lifted from 2.1% to 3.0% — a 42% improvement
Results & Business Outcome
Conversion rate improved 42%. Stockout events reduced by 67%. The dynamic pricing engine alone recovered an estimated $180K in margin that would otherwise have been lost to manual discounting in Q1.
Personalisation and pricing intelligence are no longer luxuries — they are table stakes for any e-commerce business that wants to compete in the next five years.
Let's Build Something Intelligent Together
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