Requirements Analysis: We started with a thorough analysis of ZoomX's requirements and goals. Understanding the hyperlocal delivery process was crucial to tailor the system effectively.
Design: Our approach extended to designing interfaces tailored to the specific needs of various user groups. We created distinct user interfaces for drivers, partner stores, and ZoomX admins, ensuring a customized and intuitive experience for each role.
Integration of AI: Implementing AI into the system was a cornerstone of our approach. We integrated machine learning algorithms to optimize vehicle routing, making real-time adjustments based on traffic conditions and delivery priorities.
Feedback Loops: We established feedback loops with ZoomX's operational teams to continuously gather insights and make iterative improvements. This direct collaboration ensured that the platform remained aligned with their evolving needs and industry trends.