Vialytics has transformed inconsistent road imagery into AI-powered solutions for smart cities by developing a scalable, unified data platform to handle the variability and challenges of real-world data. Initially constrained by limited resources, vialytics relied on self-hosted annotation tools and local storage, which quickly became unsustainable as their dataset grew. By adopting Encord, they centralized their data management and annotation workflow, improving data accessibility, visibility, and quality control through automated review processes and flexible annotation workflows. This enabled vialytics to manage diverse and unpredictable data, ensuring consistent model performance across different geographies and allowing for faster iteration cycles. The unified AI data stack allowed vialytics to reduce annotation overhead, focus on edge cases, and deliver reliable outputs, ultimately supporting the development of robust computer vision models essential for smart city infrastructure. Looking forward, vialytics plans to integrate more diverse data types, including sensor data, to enhance their ability to interpret infrastructure conditions, emphasizing the importance of a scalable AI data stack for effective urban infrastructure management.