AP23489233 – SmartBuy Connect: AI- based intelligent group buying system

Objective of the project – is to create an intelligent group buying system based on artificial intelligence (AI) technology, transforming the traditional way of shopping, making it smarter, more interactive and personalized.

Relevance: The relevance of the project is driven by the rapid development of online commerce and the need to expand sales channels for entrepreneurs. Modern marketplaces provide access to a wide audience of customers, but require new solutions to improve sales efficiency and business competitiveness. At the same time, consumer demand for convenient, fast, and personalized shopping experiences with access to a wide range of products is increasing. Under these conditions, the development of hybrid trading models that combine online and offline formats becomes particularly important. The automation of interaction between sellers and buyers is also of great importance, as it enhances service quality and accelerates decision-making processes. The use of artificial intelligence and machine learning technologies enables the analysis of consumer behavior and the creation of personalized offers. This contributes to more efficient demand management and increases sales volumes without reducing pricing strategies. The project gains additional relevance through the implementation of group purchasing mechanisms and elements of “live trading,” including negotiations, discounts, and counteroffers. Such approaches increase user engagement and create new consumption models based on the principles of the sharing economy. Furthermore, the integration of fintech tools and trading mechanisms transforms trading platforms into multifunctional digital ecosystems. Thus, the project aligns with modern trends in economic digitalization and aims to improve trade efficiency and user satisfaction.

Scientific supervisor: Doctor of technical sciences, Professor, Uskenbayeva Raissa Kabievna

Expected and achieved results: A comprehensive literature and patent analysis was conducted, enabling the identification of key principles of the sharing economy and justifying the use of artificial intelligence to enhance the efficiency of group purchasing. It was established that the use of machine learning methods ensures a high level of personalization through the analysis of user behavior, recommendation systems, and dynamic pricing. Key algorithms were studied and selected, including collaborative filtering, clustering, dynamic pricing methods, hybrid recommender systems, natural language processing (NLP), and association rule learning. Based on the analysis, the conceptual foundations of a new business model were developed, focused on personalized group offers and flexible pricing depending on demand and group composition. The proposed model considers modern digitalization trends and the principles of the sharing economy, contributing to increased product accessibility and user engagement. The architecture of an intelligent group purchasing system was designed, including a SuperApp as a unified platform and MiniApps as a set of specialized services with integrated AI algorithms. For users, functionalities such as group purchasing, personalized deals, participation in auctions, and access to fintech services were implemented. For sellers, tools for offer management, AI-driven sales optimization, auction mechanisms, and Big Data analytics were provided. The developed solutions ensure an interactive and personalized trading format, improve sales efficiency, and create a foundation for a competitive digital product in the field of e-commerce.