This graduation project focuses on designing an AI-powered reconciliation Software-as-a-Service (SaaS) platform for enterprise merchants to address inefficiencies and operational complexity in financial reconciliation. In large organizations, reconciliation is a continuous control process critical for compliance, fraud prevention, and cash flow accuracy, yet existing solutions are often rigid, fragmented, and heavily dependent on IT support. Through primary research with finance operations teams and secondary analysis of industry tools, the project identified four key challenges: inflexible rule configuration, delayed exception handling, fragmented monitoring, and manual reporting overhead. The proposed platform introduces customizable reconciliation workflows, a real-time run monitoring dashboard, streamlined exception resolution with audit trails, and a flexible report builder with scheduling and exports. AI capabilities further enhance the system by detecting anomalies, suggesting probable root causes, and reducing manual review effort. Designed with a modular architecture, the solution supports scalability across enterprise workflows and establishes a foundation for future predictive insights and deeper system integrations.