Building a scalable AI-powered recommendation system for a fast-growing retailer
The client, a leading e-commerce organization, had reached a data ceiling. With more than 5 million active users and a growing product catalog, their legacy recommendation engine, which was based on hard-coded rules, could not keep up. The system delivered generic suggestions that did not take user behavior into account, which led to low engagement and missed sales opportunities. Conversion rates were stagnant, product discovery was poor, and customers often left the site without finding what they needed. At the same time, traffic surges during flash sales or holiday campaigns regularly cause system lags, slowing down page loads and frustrating users. The company needed a solution that could deliver personalization at scale without compromising performance.
We designed and deployed a hybrid recommendation engine powered by machine learning. The solution used collaborative filtering and content-based algorithms trained on user browsing behavior, purchase history, item metadata, and real-time interaction data. The system architecture included:
- A distributed model built on Apache Spark for scalable data processing
- Real-time inference served through Tensor
- Flow models deployed via REST APIs
- A fallback logic layer for cold-start scenarios and anonymous users
- Caching and load balancing to maintain sub-second latency even during peak traffic
We also integrated an A/B testing framework to compare AI recommendations against the control group, measuring engagement and purchase intent improvements across user segments.
The AI-driven system was fully operational within four months and began delivering measurable business value from day one:
- 28% increase in conversion rate across all personalized product pages
- 15% growth in average order value driven by better cross-sell and upsell suggestions
- 3x improvement in product discovery, measured through reduced bounce rates and increased time-on-site
- Sub-second latency maintained for 98% of all recommendation requests during seasonal peaks
- More than 10 million daily interactions are processed in real-time without system degradation
The recommendation engine is now a core part of the client's digital strategy and powers product suggestions while informing marketing campaigns, inventory placement, and customer segmentation. What started as a technical upgrade started driving revenue growth and customer loyalty.
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