DoorDash cuts grocery reorder latency from ~16s to ~2s using AI agents

KEY POINTS

1. DoorDash reduced latency in its AI-powered grocery reorder flow from about 16 seconds to 2 seconds by precomputing recurring purchase candidates (from 90 days of history) with batch LLM inference, applying deterministic validation, and storing reusable shopping-list data; the serving path then resolves items against the live catalog.
2. The change doubled shopping-list views and increased the flow’s order rate by roughly 34%, and it builds on DoorDash’s shared agent platform and grounded shopping interface (Ask DoorDash).
3. This shows an effective agent-architecture tradeoff—moving historical reasoning offline with batch LLMs while resolving live catalog state at request time can dramatically cut latency and improve conversion for conversational shopping.