Entity resolution (ER) is a critical task in data management which identifies whether multiple records refer to the same real-world entity. Despite its significance across domains such as healthcare, finance, and machine learning, implementing effective ER systems remains challenging due to the abundance of methodologies and tools, leading to a paradox of choice for practitioners. This paper proposes Resolvi, a reference architecture aimed at enhancing extensibility, interoperability, and scalability in ER systems. By analyzing existing ER frameworks and literature, we establish a structured approach to designing ER solutions that address common challenges. Additionally, we explore best practices for system implementation and deployment strategies to facilitate largescale entity resolution. Through this work, we aim to provide a foundational blueprint that assists researchers and practitioners in developing robust, scalable ER systems while reducing the complexity of architectural decisions.

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