Rappi, Latin America's super-app delivering everything from groceries to cash across 9 countries, struggled with logistics efficiency as order volume scaled to 1 million daily deliveries. Traditional routing algorithms couldn't handle the complexity of multi-stop deliveries, real-time traffic patterns, and dynamic demand surges. Delivery times averaged 47 minutes while competitors promised 30-minute service. High logistics costs threatened unit economics as the company expanded into tier-2 cities.
Rappi deployed AI-powered delivery routing that dynamically optimized courier assignments and routes in real-time. Machine learning models predicted demand patterns by location and time, positioning couriers proactively before orders arrived. The system optimized multi-order batching, enabling couriers to fulfill 3-4 orders per trip. Real-time traffic data, weather conditions, and historical completion times fed into route optimization. Computer vision verified delivery completion, reducing fraud. The AI platform also predicted customer lifetime value to prioritize premium subscribers.
“AI logistics optimization is the only way to achieve 30-minute delivery economics across Latin America's challenging geography. Speed and efficiency are now our competitive moat.”— Chief Operations Officer, Rappi
This case study is based on publicly available information about Rappi.
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