A global e-commerce platform with over 15 million monthly active users was losing engagement due to static, batch-calculated product recommendations that took up to 6 hours to update. Users were repeatedly shown items they had already purchased or abandoned.
WOVEN DATA deployed an autonomous real-time recommendation engine powered by streaming graph neural networks. By combining clickstream telemetry via Apache Kafka with an in-memory feature store, user session profiles are updated in under 8 milliseconds.
The platform achieved a 38% increase in conversion rates, a 24% boost in average order value, and saved $1.2M annually in cloud infrastructure fees through GPU quantization.
