The escalating demand for intelligent and scalable matching systems across diverse domains, including ride-hailing, necessitates flexible and adaptable solutions beyond traditional monolithic approaches. This paper presents a multi-context matching platform, initially applied to the ride-hailing domain, emphasizing its algorithmic core. We detail the design and evaluation of two primary matching algorithms—Gale-Shapley Stable Matching and the Hungarian Algorithm—chosen for their proven efficiency and adaptability in scenarios where preferences and optimality are critical. A crucial aspect of this work involves a simulation framework that uses real-world New York City (NYC) ride data to benchmark the performance of these algorithms against a simpler Greedy approach, assessing metrics such as rider waiting time and algorithmic runtime. The findings reveal inherent trade-offs between match quality and computational cost, informing an adaptive strategy for algorithm selection based on operational conditions. The proposed system provides a flexible and extensible foundation for real-time matching, contributing towards building future-ready coordination platforms.