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Cutting delivery-time errors for a quick-commerce platform

We replaced rule-based delivery estimates with a machine learning model that learns from traffic, store load and weather, and moved order events to a streaming architecture that holds up during evening peaks.

National quick-commerce platform, Food delivery and quick commerce

38%
lower ETA error
2.4x
peak order capacity
21%
fewer “where is my order” tickets

The challenge

Customers saw delivery estimates that were often wrong at peak hours, which drove cancellations and support calls.

What we built

An ETA model trained on historical orders, live store load and traffic, served from an event-driven pipeline with automatic retraining.

“Customers now trust the number on the screen. That changed our support load overnight.”
VP Engineering, Quick-commerce platform

Last updated

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