Instacart
genAI conversational search
Ask Instacart, launched in May 2023, is a genAI conversational search that combines OpenAI's ChatGPT API and a catalog of more than a billion items across more than 80,000 retail partner locations, deployed to all US customers.
Objective
Turn the search bar into a food inspiration tool to remove indecision (what to cook, what to buy) and grow the basket.
The deployment
Ask Instacart is a conversational search integrated into the app's search bar. The customer asks an open-ended food question (side-dish ideas for a dish, options for a diet) and the tool returns organized recommendations plus information on preparation, product attributes, and dietary considerations. It combines OpenAI's ChatGPT API, Instacart's in-house models, and a catalog of more than a billion items across more than 80,000 retail partner locations. The model is deliberately specialized in food questions. Launched on May 31, 2023, rolled out to all US customers in the following weeks.
Results Proof C
Official Instacart announcement corroborated by press (Search Engine Land, PR Newswire release). Proof of scale through catalog coverage and national rollout, but no published financial result or conversion metric. Still live in 2026 (extension to OpenAI's ChatGPT app).
How it works
Documented architectureThe stack in detail
- llm OpenAI ChatGPT API interpretation of the open-ended food question and generation of the answer
- llm Modeles ML proprietaires Instacart alignment of the answer with the catalog and reranking of products by location
- infra Catalogue Instacart more than a billion items across more than 80,000 retail partner locations, with dietary and preparation attributes
- outil Garde-fous de perimetre thematique in-house framing that limits the model to relevant food questions
How it runs, concretely
For ops teams-
1Open-ended food question customer
The customer types a question into the search bar rather than a product keyword.
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2Understanding and generation AI
The ChatGPT API interprets the intent, Instacart's models align the answer with the catalog.
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3Organizing the results AI
Products are grouped with preparation info and dietary attributes, and matched to brands' sponsored campaigns.
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4Scope framing data team
The model is constrained to answer only relevant food questions.
The match between the food question and the local partner catalog. If an item is not carried by the customer's retailer, the recommendation does not convert into a basket.
How your customers perceive this type of use
Sourced studiesLes consommateurs n'acceptent pas les chatbots par defaut : 64% prefereraient que les entreprises n'utilisent pas d'IA dans leur service client (Gartner, 2024) et pres d'un utilisateur sur cinq du service client par IA n'en retire aucun benefice (Qualtrics, 2025). L'acceptation se construit sur trois conditions mesurees par Salesforce : savoir qu'on parle a une IA, pouvoir escalader vers un humain, comprendre la logique de l'agent.
Acceptance conditions
- Etre informe qu'on parle a une IA et non a un humain (pres de 75% le demandent, Salesforce 2024)
- Un chemin d'escalade clair vers un agent humain (45% plus enclins a utiliser l'agent IA, Salesforce 2024)
- Une logique de l'agent clairement expliquee (44% plus enclins, Salesforce 2024)
Red lines
- Rendre l'humain injoignable : c'est la premiere inquietude des consommateurs sur l'IA dans le service client (Gartner 2024) et 50% craignent que l'IA les coupe du contact humain (Qualtrics 2025)
- Remplacer le service client par l'IA sans alternative : 53% envisageraient de partir chez un concurrent (Gartner 2024)
Sources: Salesforce 2024 · Gartner 2024 · Qualtrics 2025
How to replicate
Inference, not sourcedData prerequisites
- broad catalog localized by point of sale
- dietary and preparation attributes
- brands' sponsored campaigns
Org prerequisites
- agreement to use a third-party LLM API
- topic-scope guardrails
Possible stack
- LLM API (e.g. OpenAI)
- in-house reranking models
- RAG over the catalog
The plan, step by step
- Step 1Enrich the catalog with usable attributes (dietary, preparation, localization by point of sale)Deliverable: Enriched catalog queryable by attribute
- Step 2Define the topic scope and the guardrails (what the assistant refuses to handle)Deliverable: Scoping spec + set of test questions
- Step 3Connect the LLM API to the catalog and build the in-house reranking layerDeliverable: Conversational search prototype on a subset of the catalog
- Step 4Open a beta on a traffic segment and compare against keyword searchDeliverable: Engagement and add-to-basket metrics vs classic search
- Step 5Roll out progressively and monitor the answer-to-local-catalog matchDeliverable: Full rollout + tracking of basket conversion
First step: Restrict the scope to one domain (here food) and connect an LLM API to the enriched catalog.
The same pattern, proven in other industries
Booking.com
genAI trip planning assistant
Burberry
genAI copilot for consultants (search, image recognition, complete-the-look)
Homes.com
natural-language voice and text search, then conversational assistant
Sources
- S1 Bringing Inspirational, AI-Powered Search to the Instacart app with Ask Instacart Primary archive pending
- S2 Ask Instacart brings generative AI to Instacart's search experience Secondary archive pending
- S3 Instacart Launches New AI-Powered Food Inspiration Search Tool, Ask Instacart Established press archive pending
An error, newer info, a source?
This page lives on its accuracy. If a figure has moved, if the deployment has changed, or if you have a higher-quality source, tell us. Every sourced correction is verified before publication.