The DoorDash Data team is responsible for generating accurate data insights and answers to key business questions:
Why did the order volume dip in California last week?
How has a new product been pacing since launch?
Which merchants are at risk of churning from our marketing products?
This post covers the client and platform boundaries behind user experience and interactions with Ask DoorDash: how we moved from hackathon carousels to that model, how one artifact serves three readers, and how context and state stay consistent as consumers move through DoorDash.
DoorDash’s experimentation platform manages over 60,000 feature flags across roughly 623 repositories.
Introduction
We built Ask DoorDash on a common platform that lets domain teams build and evolve their agents without rebuilding the systems beneath them.
How we built a measurement layer that tells us where, why, and how much to trust an agentic code reviewer, and why a single metric never could.
DoorDash serves a vast and diverse set of merchants, with every restaurant, menu, and dish expressed in its own unique way.
Following our earlier engineering overview of Ask DoorDash, this third post in the blog series takes a close look at the evaluation harness behind the system.
Following our earlier engineering overview of Ask DoorDash, this second post in the blog series takes a close look at the intelligence behind the system.
First of a blog series on the engineering behind DoorDash Assistant.
DoorDash’s marketplace spans restaurants, groceries, convenience stores, retail outlets, and more.
