Our REST API integration is designed to be frictionless, providing a smooth and seamless experience, streamlining the connection and data flow in an efficient and intuitive manner.

Recommender systems often face several drawbacks that can reduce their efficiency, such as bias, overfitting and limited data accuracy. Our proprietary engines are designed to overcome all of these challenges, as outlined below, offering greater flexibility and reliability in prediction.

We compared the results of our engine with the main public recommendation engines in a “state-of-the-art” study (cientific papers published by “Papers with Code”). This is a public study, where all engines were exposed to the same conditions (data and evaluation method).

Our results stand out at all Recall levels, we gained efficiency in scenarios of small and extremely targeted recommendations (Recall@2), as well as in large scenarios (Recall@100), for high recommendation volumes.

Our recommendation engine features five modules: one core module (“Recommended for You”) and four complementary ones. These modules enhance recommendations at specific points in the customer journey, providing the flexibility needed to create highly personalized touchpoints at key moments. This level of precision allows us to deliver tailored experiences that drive engagement and optimize the overall customer journey.