Polanyi Labs

Polanyi Labs is a tool for DTC brands that need behavioral data to power recommendation systems but do not yet have enough real user history. It generates synthetic shopping journeys and event logs from any website URL, closing the cold-start gap that typically delays personalization.

The core feature is the ability to turn a catalog or website into structured behavioral data suitable for training and evaluating recommender systems. This lets teams build and test ranking logic before their own traffic reaches meaningful volume, without waiting years for organic signal.

The site is aimed at early-stage and growing DTC brands, as well as data and engineering teams responsible for personalization. It is most useful when real interaction data is sparse but the catalog and product structure are already defined.

A sensible approach is to use the synthetic data as a starting point for model development and experimentation, then blend it with real user events as they accumulate. Teams should validate generated journeys against their own product logic before relying on them in production.

Overall, Polanyi Labs offers a pragmatic way to accelerate recommender development when behavioral history is the missing ingredient.