Ralph Lauren
genAI conversational shopping assistant that generates complete shoppable outfits from natural-language prompts
Ask Ralph, the conversational assistant in the Ralph Lauren mobile app launched in September 2025 in the United States and built with Microsoft on Azure OpenAI, generates complete shoppable outfits from natural-language prompts, surfacing only items available in the Polo Ralph Lauren inventory.
Key points
- GenAI conversational shopping assistant generating complete shoppable outfits (Ask Ralph).
- Agent built with Microsoft on Azure OpenAI, natural-language search on the catalog.
- Rollout live in the US app (iOS and Android) since September 2025.
- Evidence B, status confirmed; no public adoption metric at this stage.
Objective
Turn product discovery in the app into a styling conversation that leads to a purchase. Ask Ralph reproduces the guidance of an in-store associate, foregrounds the brand's point of view on style, and pushes complete buyable outfits rather than isolated items, to deepen the customer relationship in the digital channel.
The deployment
Ask Ralph is a conversational assistant integrated into the Ralph Lauren mobile app, launched in September 2025 in the United States (iOS and Android). The user writes a natural-language request, such as "What should I wear to a concert?" or "How can I style my navy-blue men's blazer?", and receives complete outfits presented as a visual laydown, with style advice and gift ideas. Each piece is shoppable: you can buy one item or the whole outfit. The assistant, built with Microsoft on Azure OpenAI, interprets intent rather than matching keywords, asks clarifying questions, and refines its suggestions based on expressed preferences. It only offers items available in the Polo Ralph Lauren inventory, men's and women's. Ralph Lauren describes a tool meant to grow richer with use and announces a roadmap: voice commands, preference memory, image navigation, extension to other group brands and other markets. No adoption or conversion metric has been made public at this stage.
Results Proof B
The deployment is triangulated by three concordant sources: the official Ralph Lauren press release (T1 primary, Sept 9, 2025), the Microsoft customer story that documents the stack and quotes the Chief Digital Officer (T2), and established specialist press (Marketing Dive, T3). The feature is live at US national scale in the brand's flagship app and confirmed active in mid-2026. No public adoption or conversion metric exists to date, which caps the level below A.
How it works
Documented architectureThe stack in detail
- plateforme Microsoft Azure OpenAI Provides the generative and language AI models that interpret open prompts and power the conversation. Ralph Lauren describes a long-standing partnership with Microsoft, continued on Azure OpenAI for Ask Ralph.
- outil Agent d'achat personnalise Built jointly with Microsoft. Interprets intent rather than keywords, adapts recommendations to tone, satisfaction, and context (place, event), and composes complete outfits only from available inventory.
- outil Moteur de recherche en langage naturel Optimized for detailed inputs, it links the user's prompt to the Polo Ralph Lauren catalog to surface only genuinely available items.
- plateforme Application mobile Ralph Lauren The delivery channel for Ask Ralph, on iOS and Android, for account holders in the United States. The assistant is integrated there as a styling and shopping tool.
How it runs, concretely
For ops teams-
1User prompt customer
The customer writes an open request in the app (occasion, item to style, gift idea).
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2Intent interpretation AI
The agent, via Azure OpenAI, understands the context rather than keywords and, if needed, asks a clarifying question.
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3Catalog search AI
The natural-language search engine links the intent to product listings and keeps only Polo Ralph Lauren items in stock.
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4Composition and display AI
The assistant composes complete outfits as a visual laydown, with style advice, and lets the user buy a piece or the set.
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5Refinement and oversight marketing
The customer refines their preferences; the team tracks recommendation quality, frames the catalog, and extends the scope (new brands, voice, image).
The intent expressed in the prompt, crossed with real inventory availability. If the catalog and stock feed is not synchronized, the assistant risks composing outfits with unavailable items and breaks the shoppable experience.
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
- Structured product catalog with usable style attributes (color, material, occasion, category)
- Real-time availability and stock to offer only buyable items
- Clean product images usable in an outfit laydown
- Outfit-building rules or styling history to frame the associations
Org prerequisites
- Merchandising team that owns and qualifies the catalog used
- Digital/e-commerce team to integrate the assistant into the app and cart
- Cloud/LLM partnership and a governance framework for the AI's answers
Possible stack
- LLM via cloud (Azure OpenAI or equivalent) for conversation and intent interpretation
- Semantic search engine on the catalog (RAG approach) connected to stock
- Outfit-building layer that assembles complete looks from the surfaced items
- Commerce app and cart integration for one-gesture purchase
The plan, step by step
- Step 1Structure and enrich the product catalog with style and occasion attributes, and connect the real-time availability feed.Deliverable: Catalog usable by a semantic search, limited to items in stock.
- Step 2Set up a natural-language search engine that links a free prompt to relevant, available products.Deliverable: Intent search validated on a sample of customer queries.
- Step 3Add the conversational layer (LLM via cloud) that interprets context, asks clarifying questions, and composes complete outfits.Deliverable: Assistant able to render coherent complete outfits as a laydown.
- Step 4Integrate the assistant into the app and cart to make each piece and each outfit buyable in one gesture.Deliverable: End-to-end prompt-to-purchase journey in the app.
- Step 5Open to a segment of users, track recommendation quality and conversion, then broaden the scope (brands, channels, markets) and modalities (voice, image, memory).Deliverable: Assistant in production with a quality tracking loop and an extension roadmap.
First step: Qualify the catalog with style attributes and connect real-time stock availability: without this foundation, an assistant that composes outfits quickly surfaces unavailable items and loses its shoppable promise.
The same pattern, proven in other industries
Amazon
genAI shopping assistant
Booking.com
genAI trip planning assistant
Flipkart
conversational genAI shopping assistant
Sources
- S1 Ralph Lauren Introduces Ask Ralph, a New Conversational AI Shopping Experience Primary archive pending
- S2 Ralph Lauren redefines shopping with Microsoft AI-powered styling companion Ask Ralph Interested party archive pending
- S3 Ralph Lauren debuts Ask Ralph AI shopping assistant on mobile app Established press archive pending
An error, newer info, a source?
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