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This article details DoorDash's architectural shift from static prediction models to agentic recommendation platforms capable of multi-step planning and reasoning within consumer commerce contexts. The core technical focus lies on implementing language-native memory, RQ-VAE semantic IDs for catalog representation, and grounded search to enhance relevance in complex shopping missions like grocery delivery.
Presentation: From Models to Agents: Building Context-Aware Consumer AI at Scale at DoorDash

Sudeep Das shares how DoorDash shifts from legacy one-shot predictions to an agentic recommendation platform. He discusses leveraging language-native consumer memory, RQ-VAE semantic IDs for catalog representation, and grounded search to dramatically boost relevance and conversion metrics. By Sudeep Das
Sudeep Das: I'm the head of AI for new verticals at DoorDash. In the last week, how many of you have ordered something from DoorDash, restaurants? How many of you have ordered grocery or something other than restaurants from DoorDash? This is the work we have to do. The talk that I wanted to give you today is about how grocery planning, shopping is like multi-state and it requires planning, reasoning, going back and forth about your preferences, and things like that. Personalization in the old way, the legacy way of one-shot predictions no longer work. With AI, we're in this unique juncture where you can actually build such systems that can reason, adapt, plan for the consumer and make a shopping mission, like grocery or something come true. I want to tell you about a few things that we are building at DoorDash that are the raw ingredients of that transformation, from one-shot prediction systems to systems that can plan and understand the consumer at a different level, and the items and the intent. A lot of people still think of DoorDash as a restaurant delivery business, but we are actually shaping ourselves up for a time now to…
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