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E-COMMERCE & MACHINE LEARNING
Real-Time Personalization & Recommendation ML Engine for E-Commerce
Designed a vector-search ML recommendation engine serving 45 million monthly active shoppers with personalized product suggestions.
Conversion Rate
+28.4%
Increase
Average Order Value
$64 → $89
+39%
Inference Latency
14ms
Sub-second
Client SectorGlobal Retail & Marketplace Leader
Duration5 Months
Core StackQdrant, Python, Apache Flink
The Challenge
Generic recommendation algorithms failed to adapt to real-time shopper intent, causing lost cart conversions during peak Black Friday traffic spikes.
Our Engineering Approach
01
Implemented real-time user behavior embedding vectors powered by Qdrant vector database and transformer embeddings.
02
Deployed edge-rendered personalized carousels using Next.js and Redis cache layer.
03
Automated continuous online model retraining pipelines triggered by real-time clickstream data.
Key Architectural Highlights
Qdrant Vector DB storing 50M+ product & user embeddings
Apache Flink real-time clickstream feature engine
Multi-armed bandit reinforcement learning for dynamic offer placement
“Our recommendation click-through rate doubled within two weeks of launching InnoBrain's vector AI engine. It paid for itself in less than a month.”
Sarah Jenkins
Head of Digital Product
Technologies Deployed
QdrantPythonApache FlinkNext.jsRedisTensorFlowKubernetes
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