State-of-the-art deep learning architectures powered by frontier data, to increase revenue and reduce costs of e-commerce websites.
State-of-the-art architectures fine-tuned on proprietary e-commerce data. Built for scale, optimized for latency.
Next-generation recommendation architecture combining multiple deep learning models to capture complex relationships between customers, products, and behavior at scale.
State-of-the-art demand forecasting model with multi-horizon predictions.
Deep customer segmentation powered by graph neural networks. Identifies 30× more micro-segments than traditional clustering algorithms.
Real-time user experience adaptation using deep learning computation with super-fast edge inference.
Real-time market trend detection combining social signals, search patterns, visual analysis, and competitive intelligence.
State-of-the-art Graph Neural Networks capture complex multi-relational patterns between customers, products, and behaviors.
Frontier deep learning architecture with attention mechanisms for multi-horizon forecasting with interpretable variable importance.
Specific embeddings unite visual, textual, and behavioral signals in unified representations.