116-LQP-ARJCS 20261018142634 Lumina Quest Publishing m.arslansohail@gmail.com Lumina Quest Publishing Advanced Research Journal of Computer Science Adv. Res. J. Comput. Sci. 3134-884X 10.66590/arjcs https://lquestpub.com/archives.php?journal=advanced-research-journal-of-computer-science 12 16 2024 12 16 2024 1 2 10.66590/arjcs20240102 https://lquestpub.com/articles-list.php?journal=advanced-research-journal-of-computer-science&volume=1&issue=2 Digital Platforms for Local Service Provider–Client Matching a Review: As a Case Study the ZipZap Jobs Syed Ali Usman Muhammad Shahzaib The increase in the adaptation in mobile technology transform the way, the people are searching for services, such as, service engagement, arranging employments and communication with the professionals. Despite having this type of progress, finding skilled workers and local service providers, which should be reliable, such as carpenter, plumber, electrician remained difficult when distribution of information is made across informal networks, advertisements and personal recommendations. The ZipZap Job app addressed this problem by giving a mobile based platform that is used to connect clients with local service providers by a centralize digital environment. This article is examining the testing approach, security considerations, technical architecture, functional characteristics, concept and future use of this app for the platforms which are based on supplied project documentation. A special attention is given to administrative management, notifications, ratings, booking, job applications, messaging, filtering, location based research and user files. This review is also evaluating the limitations and strengths of the system, which is proposed including use of artificial intelligence for matching, offline payment models, internet connectivity dependence and its mobile based orientation. This project is also demonstrating how a simplified digital marketplace can be used to reduce friction in the local service discovery with the service providers having great visibility. Developments in future can strengthen the platforms through strong empirical evaluation by real users, improved filtering, appointment reminders, broader service categories, intelligence recommendation mechanism and advance verification system. 12 16 2024 12 16 2024 4 7 10.66590/arjcs2024010202 https://lquestpub.com/article/10.66590/arjcs2024010202