--- title: Building a unified consumer memory for personalization... description: DoorDash built a unified consumer memory platform that extracts semantic understanding from behavioral signals (orders, browsing, searches) and makes it... canonical: https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Building a unified consumer memory for personalization at scale | daily.dev og:description: DoorDash built a unified consumer memory platform that extracts semantic understanding from behavioral signals (orders, browsing, searches) and makes it... og:url: https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0 og:image: https://api.daily.dev/og/posts/EmIYwZ2l0.png og:image:alt: Building a unified consumer memory for personalization at scale og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Building a unified consumer memory for personalization at scale **[Doordash](https://daily.dev/sources/doordash)** · 16 min read · 0 upvotes · 0 comments ## Summary DoorDash built a unified consumer memory platform that extracts semantic understanding from behavioral signals (orders, browsing, searches) and makes it available across all personalization surfaces. The system uses LLMs to generate structured 'memory blocks' — versioned, domain-specific summaries like dietary preferences, brand affinities, and dining patterns — organized into three layers: long-term, in-session, and explicit context. These memory blocks are encoded two ways for ML consumption: dense asymmetric embeddings for semantic similarity matching, and a heterogeneous context graph for relational reasoning between consumers, brands, taxonomies, and concepts. The platform powers personalized carousels and ranking models, with selective recomputation to manage LLM compute costs. Key lessons include treating memory blocks as semantic matching primitives for both ML and LLM systems, decoupling extraction from encoding, and maintaining full versioning/lineage for every component. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Offline LLMs, Online Personalization: Generating carousels at DoorDash](https://daily.dev/posts/offline-llms-online-personalization-generating-carousels-at-doordash-6e4ou6tbm) · Doordash · 0 upvotes · 0 comments - [How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone](https://daily.dev/posts/how-doordash-built-an-ai-shopping-assistant-that-doesn-t-rely-on-the-llm-alone-w3gyrfrce) · InfoQ · 5 upvotes · 1 comments - [Bridging Affordability, Familiarity, and Novelty: DoorDash’s LLM-assisted personalization framework](https://daily.dev/posts/bridging-affordability-familiarity-and-novelty-doordash-s-llm-assisted-personalization-framework-szl0rq8ml) · Doordash · 0 upvotes · 0 comments - [Building Ask DoorDash \(Part 2\): Intelligence](https://daily.dev/posts/building-ask-doordash-part-2-intelligence-4xcmvaewz) · Doordash · 2 upvotes · 0 comments - [Using LLMs to build content embeddings for DoorDash search and recommendations](https://daily.dev/posts/using-llms-to-build-content-embeddings-for-doordash-search-and-recommendations-fuy4zkqul) · Doordash · 0 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#embeddings](https://daily.dev/tags/embeddings), [#recommendation-systems](https://daily.dev/tags/recommendation-systems) [View this post on daily.dev](https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Building a unified consumer memory for personalization at scale","url":"https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0"},"datePublished":"2026-06-08T22:15:27.581Z","dateModified":"2026-06-08T22:15:59.972Z","description":"DoorDash built a unified consumer memory platform that extracts semantic understanding from behavioral signals (orders, browsing, searches) and makes it...","image":"https://media.daily.dev/image/upload/s--foaA6JGU--/f_auto/v1722860399/public/Placeholder%2004","thumbnailUrl":"https://media.daily.dev/image/upload/s--foaA6JGU--/f_auto/v1722860399/public/Placeholder%2004","isAccessibleForFree":true,"articleSection":"Doordash","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Doordash","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/3efd128f637f42ccb208d9829bdf007e","url":"https://daily.dev/sources/doordash"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,embeddings,recommendation-systems","timeRequired":"PT16M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Doordash","item":"https://daily.dev/sources/doordash"},{"@type":"ListItem","position":3,"name":"Building a unified consumer memory for personalization at scale"}]} ```