AI AgentFashion Agent
Fashion Agent that automates sales workflows using your data and tools.
The challenge
Personal styling only ever reaches the top tier of customers, and everyone else browses a grid of single products. Size advice is guesswork, a large share of returns come down to fit rather than taste, and what customers actually keep never reaches the buying team.
The outcome
Azure AI Search filters live stock by size and season, a Microsoft Foundry agent assembles complete looks rather than single items, and Azure Machine Learning predicts size from measurements and return history. VIP clients and unusual briefs pass to a human stylist with the brief already prepared.
At a glance
- Type
- ai agents
- Category
- specialized
01 — Architecture
End-to-end architecture
A shopper describes an occasion, a budget and a taste. The Foundry agent filters live stock by size and season, assembles complete looks rather than single products, and advises fit from measurements and return history. VIP clients and unusual briefs go to a human stylist.
- Shopper: Shopper / Stylist
- Channels: Azure Static Web Apps, Communication Services
- Application: Azure API Management, Azure Container Apps
- AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
- Data & records: Azure Blob Storage, Azure Cosmos DB, Azure Machine Learning
- Outcome & insight: Dynamics 365 / CRM, Human stylist, Power BI / Fabric
02 — Workflow
Process & decision workflow
How a brief becomes a wearable look — understand, search, style, fit and present, then branch. A confident match is shown with sizes and alternatives, while a VIP client or an unusual request hands across to a human stylist with the brief already prepared.
- Understand: Occasion, budget, body shape and taste captured
- Search: Catalogue filtered by stock, size and season
- Style: Complete looks assembled, not single products
- Fit: Size advised from measurements and return history
- Present: Look shown with imagery and styling notes
- Learn: Sell-through, keep rate and fit feedback tracked
- Path 1 · confident match — Present the look: Outfit shown with sizes and alternatives
- Path 2 · VIP or unusual request — Human stylist steps in: Brief, options and history handed across
03 — Components
Key Microsoft components
Styling advice must reflect live stock and honest fit — search, composition and imagery all stay on the Microsoft stack.
Azure Static Web AppsStyling and look-book experience across web and app.
Azure Communication ServicesLook sharing, back-in-stock and drop notifications.
Azure API ManagementSecure gateway for catalogue, stock and order APIs.
Azure Container AppsStyling and composition workers that scale at peak.
Microsoft Foundry Agent ServiceOutfit composition, sizing and escalation orchestration.
Azure OpenAI modelsLook narration, styling notes and conversational advice.
Azure AI SearchSemantic search over catalogue, attributes and trends.
Azure AI Content SafetyGuardrails on imagery, body-image language and tone.
Azure Machine LearningSize prediction, return risk and propensity models.
Azure Blob StorageProduct imagery, look boards and generated assets.
Azure Cosmos DBWardrobe, session and styling conversation state.
Azure SQLOrders, returns, sizes and preference records.
Dynamics 365 / CRMCustomer profile, loyalty tier and styling history.
Power BI / Microsoft FabricSell-through, keep rate and fit-feedback dashboards.
04 — AI
What the agent consumes
The capabilities the agent applies to every look, and the line it does not cross.
AI capabilities embedded in the agent
- Preference extraction
- Visual attribute tagging
- Outfit composition
- Size and fit prediction
- Trend and season awareness
- Retrieval-augmented generation
- Stock-aware recommendation
- Look narration
- Return-reason classification
- Multilingual conversation
- Sell-through analytics
- Workflow orchestration
AI responsibility boundaries
The agent styles from live stock and published product data, and never claims a fit it cannot support from measurements or return history. It does not comment on body image, hold payment data or apply discounts outside campaign rules. VIP clients and unusual briefs go to a human stylist with the brief already prepared.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the brand, the styling template, the fit rules and the value model — the page structure stays identical.
Brand & catalogue profile
Define the brand voice, categories, size systems, markets and seasonal calendar in scope. The personas are the shopper, the personal stylist, the buyer and the merchandising team.
Styling template
One consistent flow for every styling agent: understand the brief, search live stock, compose the look, advise on fit, present with imagery and learn from what is kept.
Fit & tone rules
Declare the size charts, fit tolerances, imagery standards, inclusive-language rules and the discount authority. Fit advice is always evidenced by measurements or return data.
Value model
Capture baseline metrics first, then map the expected benefits: basket size, keep rate, return rate, styling capacity, sell-through and customer satisfaction.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Basket size+20%Complete looks rather than single-product baskets.
- Return rate−15%Evidence-based size advice before the order is placed.
- Styling capacity5×Personal styling extends beyond the VIP client list.
- Fit evidencePer sizeEvery recommendation cites the data behind it.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Return rate: manual baseline 100, AI-assisted target 85
- Time to a look: manual baseline 100, AI-assisted target 20
- Stylist hours: manual baseline 100, AI-assisted target 55
Related & recommended
Derived automatically from our solution knowledge graph.
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Technologies
What powers this solution.
TechnologyAzure AI
Managed AI services for vision, speech, language and document.
TechnologyAzure AI Foundry
Platform to design, evaluate and operate AI apps and agents.
TechnologyAzure OpenAI
Enterprise access to GPT models with governance.
TechnologyMicrosoft Copilot Studio
Low-code platform to build custom copilots and agents.
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Keep it running and optimised.
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