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At Zalando, the in house search engine currently matches full text queries with products through a cascaded architecture. Each step in the cascade processes the input in a specific way, for example, locating mentions of brands, spell checking, and disambiguation. Finally the preprocessed input is used to filter the product attributes (such as color=red, brand=Nike). This architecture has multiple drawbacks, such as fragility, limited scalability and extensibility. Duncan and his colleagues are working on replacing this cascade with a single step end-2-end deep learning architecture which involves no textual preprocessing and directly filters image content as well as product meta-data. In this talk, Duncan described the components involved in such a system, as well as potential advantages and disadvantages.