Suldamiwa Launches Traditional Liquor Recommendation Service with Big Data AI Sommelier
Traditional Korean liquor curation platform Suldamhwa has opened a new horizon in the beverage recommendation market by unveiling its AI sommelier ‘Somrye’. Somrye offers 24-hour real-time personalized recommendations, intuitive exploration...
Traditional Korean liquor curation platform Suldamhwa has opened a new horizon in the beverage recommendation market by unveiling its AI sommelier ‘Somrye’. Somrye offers 24-hour real-time personalized recommendations, intuitive exploration with a conversational interface, and a one-stop experience that completes everything from recommendation to purchase in under 5 seconds. All of this is thanks to an accurate recommendation engine based on Suldamhwa's vast traditional Korean liquor database.
Accumulated over 6 years, 200,000 members, 100,000 products, 80,000 reviews, and over 1 million user interaction data points support Somrye's sophisticated recommendation system. In particular, a proprietary algorithm based on 'deep matrix factorization' comprehensively analyzes not only user preferences but also the preferences of other users with similar tastes, implementing objective and accurate recommendations.
With large language model (LLM)-based technology, supporting major languages worldwide, it provides expert-level traditional Korean liquor curation to overseas markets and foreign tourists visiting Korea, opening up possibilities for global expansion.
Recognizing the inadequacies of AI technology in the 2,400 trillion won liquor market, Suldamhwa declared its leap to become a deep-tech company providing AI sommelier SaaS solutions that encompass all types of alcoholic beverages, beyond traditional Korean liquor. In the long term, the goal is to build an environment where consumers can experience expert-level beverage recommendations anywhere, such as bars and restaurants. This technological prowess has also been proven by the registration of two patents for beverage recommendation methods based on preference analysis and the relationship with food flavors.
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