Our proprietary neural networks learn autonomously your catalog, your customers, your lifecycles. The more they learn, the more they specialize. On your e-commerce website, and not elsewhere.
Explore the architecture→Watch signals propagate in real time through our deep learning architectures. Each node represents a compute unit processing customer behavioral data, with connections showing gradient flow during training.
Our models rest on precise mathematical formulations. Here are the loss functions and distributions at the heart of our architectures.
Task weights are learned by the model, not tuned manually. σ_k are trainable parameters.
Predicts the exact timing of the next purchase via a parametric distribution. Not just "at risk or not".
Learns to distinguish similar pairs from random pairs in the latent space, with temperature τ.
Produces calibrated confidence intervals -- not a single point prediction but a distribution.
Real evaluation metrics, measured on independent test sets.