Atlas AI

Atlas AI creates real-time, 1:1 personalized experiences, elevating customer engagement and driving more revenue.  

The model is trained with your vectorized product data. This allows us to extract all the features and attributes from a product using a series of embeddings. This training data is used to create a large number of neural networks that act as a detailed representation of your assortment.

Atlas groups products in a similar context together and thereby creates contextual maps of your assortment. Distances between them on the map represent differences between the products in that specific dimension.

The neural maps it creates have several layers so that it is able to understand your products and their relationships in a very granular way. The first layer could be as simple as grouping the products within the same category (eg. Smartphones).

Once the first layer is created the model creates deeper layers (eg. screensize), followed by another (eg. price) and so on until it has a contextual, human-like understanding of your assortment – how deep the model gets depends on your specific catalog.

Now when shoppers enter your shop and search or click on stuff, they are not interacting with your actual products, they are interacting with your Atlas AI model. And as Atlas understands your assortment in a contextual, human-like fashion, it learns from just a single click. This is the basis for 1:1 relevance in an unprecedented way.

The Atlas AI Difference?

Atlas AI is above and beyond traditional segmentation. When it comes to delivering spot-on relevance, we have a three-step approach that ensures a tailored shopping experience for every customer:

1. General relevance based on shopper’s behavior  

What it does: Atlas AI optimizes search results and recommendations based on shoppers’ overall behavior on the site. This includes search queries and interaction patterns.  

How it benefits you: this significantly reduces the need for manual search optimization.

2. Relevance based on the current shopping intent of each shopper  

What it does: this step focuses on the immediate intent of the shopper’s current session, adapting search results based on the latest interactions and signs of buying intent.  

How it benefits you: this creates a more natural buying experience for the shopper as the model understands what they are looking for and only shows them products that are relevant in this context.