Glisten uses computer vision to break down product photos to their most important parts
At present, product data in ecommerce is incomplete, inconsistent, and unstandardized, leading to poor user experiences with severe bottom line impacts for the global retail industry. Glisten enriches, cleans, and standardizes this product data. Through Glisten, companies transform their existing product information and images into workable data by generating structured attribute information using computer vision and Natural Language Processing. Glisten automates the process of representing product data in consistent, meaningful ways that can be fed into ecommerce and retail technology solutions, such as recommendation engines, data feeds, search, analytics, marketplaces, and much more. Ecommerce sites can use Glisten to consolidate and combine inconsistent data, classify products according to a standardized taxonomy, generate new information about products, and represent product information in ingestible formats. Glisten’s product helps with the full spectrum of tasks from search and filtering to SEO to inventory management to personalization to business intelligence. Co-founder Sarah Wooders graduated from MIT where she studied computer science and holds a PhD in computer science from UC Berkeley.