Levi's encountered multifaceted issues, including unorganized product data, diverse image formats, and inconsistent data for a single style code.

Cataloging was done manually by agencies and was working out quite expensive and time consuming

Cataloging was outsourced to an agency and was proving to be a big problem with inaccurate data tagging and delays in product listings.

The cataloging content is prone to errors and requires many levels of QC to deliver the user experience that Bewakoof wishes to offer.

Writing a product description that highlights relevant features can be a laborious task to undertake

Product search is arguably the most influential touchpoint in a customer's shopping process

Most websites do not tag attributes such as Occasion or Style in their catalogs, discovery becomes a challenge

QC is a tedious process that requires large teams of stylists to manually verify data accuracy, leading to delays in go to market for sellers

When the search engine did not find the queried products, their search would default to that category, leading high intent shoppers to not convert.

For The Collective, the biggest challenge was to enable shoppers to discover relevant products during the browse, without having any information about the shopper.

VH Intimates wanted to use technology to personalize the buying experience and provide a rewarding navigation UX.

Forever 21 needed a fashion optimized AI system to surface not just the latest styles, but also the most relevant ones.

Biba wanted an AI partner that understood Indian fashion in order to surface the most relevant products and create an exciting journey for the shopper.

Utilizing UGC for conversions and driving brand loyalty on social media

Tagging products and converting it into multiple marketplace taxonomies manually, demands time, management and resources.