It is based on a set of algorithms that attempt to model high-level abstractions in data using a deep architecture with multiple processing layers and that permit software to train itself to perform tasks.
As shown in the figure below, the Loop54 language model is augmented with a second, proprietary model, aptly named by Loop54 as “Generalized Organization in Layered Expanding Maps” (GOLEM). This second, in-house built model, consists of thousands of layered Neural Networks specialized in clustering quantifiable data objects and is designed to determine the “context” behind each and every search query (learn more about the evolution of the Loop54 algorithms).
So for a given query, it determines what area (or context) of the catalogue the most relevant results (i.e. products) are located - and uses visitor behaviour to continuously improve relevancy.