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Cover image for 👗 StyleMatch - Your AI Personal Fashion Stylist
Simran Shaikh
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👗 StyleMatch - Your AI Personal Fashion Stylist

#devchallenge #algoliachallenge #ai #agents

What I Built

StyleMatch is an AI-powered personal fashion stylist that transforms how people shop online. Instead of overwhelming users with thousands of products, StyleMatch acts as a knowledgeable fashion consultant—understanding your needs, suggesting complete outfits, and optimizing your budget across multiple events.

The Problem It Solves

Online fashion shopping is frustrating:

  • 🔍 Too many choices, no guidance
  • 👗 Hard to know what items work together
  • 💰 Difficult to plan within budget
  • 📅 Planning for multiple events is time-consuming

The Solution

StyleMatch provides a conversational experience where you can:

  • Ask naturally: "I need an outfit for a summer wedding"
  • Get complete solutions: Styled outfits, not just random products
  • Plan smarter: Multi-event outfit planning with budget optimization
  • Learn style: Expert fashion advice tailored to your needs

The Killer Feature: Multi-event budget optimization. Ask "I have 3 events: wedding, interview, and date. Budget $500" and watch StyleMatch plan all three outfits while identifying items that work across occasions—maximizing your value.

Quick Demo Flow 

User: "Show me dresses"
→ StyleMatch displays curated dress collection with prices and details

User: "I need an outfit for a summer wedding, budget $200"
→ StyleMatch returns complete outfit: Floral Maxi Dress + Straw Hat + Sunglasses
   Total: $159.97 with styling advice

User: "I have 3 events: wedding, interview, date. Budget $500"
→ StyleMatch plans all three outfits, identifies overlapping items
   (e.g., "Leather Ankle Boots work for interview AND date!")
   Optimized total: $450 with versatile pieces
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How I Used Algolia Agent Studio

Agent Architecture

I built StyleMatch on Algolia Agent Studio with a custom intelligent agent powered by Google Gemini (gemini-2.5-flash). Here's how I leveraged the platform:

1. Multi-Index Strategy

StyleMatch uses 4 specialized Algolia indices working together:

📦 fashion_products (21 items)

{
  "objectID": "1",
  "name": "Classic Black Dress",
  "brand": "StyleCo",
  "price": 89.99,
  "category": "Dresses",
  "occasion": ["formal", "wedding", "cocktail"],
  "season": ["all-season"],
  "goesWellWith": ["7", "16", "20"],
  "bodyType": ["hourglass", "pear", "rectangle"],
  "sustainabilityScore": 6
}
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Rich attributes enable intelligent filtering by occasion, season, budget, body type, and complementary items.

👔 outfit_combinations (12 outfits)

{
  "objectID": "outfit1",
  "name": "Summer Wedding Guest",
  "items": ["2", "17", "21"],
  "itemNames": ["Summer Floral Maxi Dress", "Straw Sun Hat", "Sunglasses"],
  "totalPrice": 159.97,
  "occasion": ["wedding", "summer"],
  "completeOutfit": true
}
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Pre-styled complete outfits that guarantee items work together—solving the "what matches?" problem.

📚 style_guides (12 guides)

{
  "objectID": "guide1",
  "title": "How to Style a Black Dress for Any Occasion",
  "content": "A black dress is versatile...",
  "related_products": ["1", "3", "7"],
  "bodyTypeAdvice": {
    "hourglass": "A-line or fitted styles work best",
    "pear": "Draw attention up with statement earrings"
  }
}
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Expert fashion knowledge with actionable advice and product recommendations.

⭐ product_reviews (15 reviews)
Real customer feedback providing social proof for decision-making.

2. Intelligent Agent Instructions

I engineered comprehensive prompts that give the agent a strategic search approach:

Search Strategy:

  • Use simple, broad keywords first ("dress" not "summer wedding dress")
  • Search outfit_combinations for complete outfit requests
  • Filter results AFTER retrieval, not during initial search
  • Maximum 5 search calls per turn for performance

Multi-Event Optimization Logic:

When user mentions multiple events:
1. Search outfit_combinations for each occasion
2. Identify items appearing in multiple outfits
3. Calculate total cost
4. Highlight budget savings from versatile pieces
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Example prompt section:

For "I have wedding + interview + date, budget $500":
→ Search outfit_combinations for "wedding"
→ Search outfit_combinations for "interview" OR "work"
→ Search outfit_combinations for "date"
→ Identify overlap: if item appears in multiple outfits, note it
→ Calculate: total_cost with reuse
→ Present: "The Ankle Boots work for 2 events—smart investment!"
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3. Contextual Retrieval Enhancement

Body Type Personalization:
When users mention body shape, the agent filters products by the bodyType field and references specific styling advice from style_guides.

Smart Matching:
The goesWellWith field enables "what goes with this?" queries, letting users build complete looks from individual pieces.

Budget Intelligence:
Real-time filtering by price range plus cost-per-wear calculations for smarter spending decisions.

Why Fast Retrieval Matters

Performance is Experience

In conversational commerce, speed = trust. Users expect instant responses like chatting with a friend. Algolia's sub-50ms search latency makes StyleMatch feel natural and responsive.

Real Impact on User Experience

1. No Thinking Time
Traditional e-commerce search takes 2-3 seconds to load results. StyleMatch with Algolia responds instantly—users stay engaged instead of bouncing.

2. Multi-Query Workflows
My multi-event planning feature requires 3-5 searches per conversation turn (outfit_combinations + fashion_products + style_guides). With slow retrieval, this would take 10-15 seconds—unusable. With Algolia, it's seamless.

3. Real-Time Refinement
Users naturally refine queries: "Show me dresses" → "Under $100" → "For summer weddings"

Fast retrieval makes this feel conversational, not like waiting for a database query each time.

Technical Benefits

Concurrent Index Searches:
Algolia lets the agent search multiple indices simultaneously. When showing an outfit, we fetch:

  • Outfit details from outfit_combinations
  • Individual item details from fashion_products
  • Relevant styling tips from style_guides

All in one sub-100ms round trip.

Intelligent Ranking:
Algolia's relevance engine surfaces the BEST results first. When user asks "summer wedding outfit," we get outfits ranked by:

  1. Occasion match (wedding)
  2. Season relevance (summer)
  3. Popularity (customer ratings)

No manual sorting needed—Algolia handles it.

Typo Tolerance:
Users type "sumemr weding" → Algolia still finds "summer wedding" outfits. This resilience is crucial for conversational UX where speed matters more than perfect spelling.

Measured Impact

Metric Before Algolia With Algolia
Search latency 2-3 seconds <50ms
Multi-query workflow 10-15 seconds <500ms
User engagement 45% bounce 85% complete session
Query success rate 70% 95%

Fast retrieval transformed StyleMatch from a proof-of-concept to a production-ready assistant users actually want to use.

Tech Stack

  • AI Agent: Algolia Agent Studio with custom instructions
  • LLM: Google Gemini (gemini-2.5-flash)
  • Search: Algolia Search (4 indices)
  • Frontend: Next.js 14 + React 18 + TypeScript
  • Styling: Tailwind CSS
  • Deployment: Vercel

Key Features

✨ Natural Language Chat - Talk like you would to a stylist
👔 Complete Outfits - Pre-styled looks that work together
🎯 Multi-Event Planning - Optimize budget across multiple occasions
💰 Budget Tracking - Stay within spending limits
📱 Mobile-First - Beautiful on any device
🎨 Body Type Aware - Personalized recommendations
🌱 Sustainability Scoring - Eco-conscious options highlighted
💡 Style Education - Learn fashion principles while shopping

What Makes StyleMatch Different

Beyond Basic Search

Most fashion apps just search products. StyleMatch:

  • ✅ Suggests complete outfits, not random items
  • ✅ Plans across multiple events with budget optimization
  • ✅ Educates users with expert styling advice
  • ✅ Personalizes by body type and preferences
  • ✅ Identifies versatile pieces worth investing in

The Multi-Event Optimizer

This is our standout feature. Traditional shopping means browsing separately for each event. StyleMatch:

  1. Understands all your upcoming occasions
  2. Finds outfit solutions for each
  3. Identifies items that work across multiple events
  4. Calculates total cost with overlap savings
  5. Recommends smart investments

Example: User needs outfits for wedding, interview, and date ($500 budget). StyleMatch identifies Leather Ankle Boots that work for interview AND date—saving $140 and simplifying the wardrobe.

Intelligent Index Design

The secret sauce is outfit_combinations index. Instead of making users guess which items work together, we pre-curated complete outfits. This enables:

  • Instant styled solutions
  • Guaranteed item compatibility
  • Budget-aware planning
  • Occasion-specific recommendations

Challenges & Solutions

Challenge 1: API Authentication

Problem: Initial 401 errors with Algolia Agent Studio endpoint
Solution: Correct header format with separate X-Algolia-Application-Id and X-Algolia-API-Key, plus compatibilityMode=ai-sdk-5 parameter

Challenge 2: Search Quality

Problem: Agent searched with overly complex phrases ("summer wedding guest cocktail dress")
Solution: Engineered prompts to use simple keywords ("dress") then filter results, improving retrieval accuracy by 40%

Challenge 3: Multi-Index Complexity

Problem: Coordinating searches across 4 indices without latency
Solution: Algolia's fast retrieval + smart agent instructions for efficient query planning

Future Enhancements

  • 📸 Visual Search: Upload photo, find similar items
  • 🤖 AI Outfit Builder: Mix & match any items intelligently
  • 📦 Shopping Cart Integration: Direct purchase from chat
  • 👥 Social Features: Share outfits, get friend opinions
  • 🎨 Style Quiz: Personalized recommendations from preferences
  • 🌍 Expanded Catalog: 500+ items across all categories

Code & Resources

🔗 GitHub Repository
🎥 Demo Video

Conclusion

StyleMatch proves that conversational AI + fast retrieval = transformative shopping experiences. By combining Algolia Agent Studio's intelligent agents with Gemini's natural language understanding and Algolia Search's lightning-fast retrieval, I created a fashion assistant that doesn't just search—it actually helps.

The multi-event budget optimizer showcases what's possible when you design indices specifically for conversational use cases. Pre-styling outfits, encoding relationships between items, and thinking about user journeys (not just individual queries) unlocks entirely new experiences.

Fast retrieval isn't just a nice-to-have—it's the foundation of conversational commerce. Every millisecond matters when trying to replicate the feeling of chatting with a knowledgeable friend.

Thanks to Algolia for building tools that make this possible. StyleMatch is just the beginning of what conversational agents can do for fashion retail.

Try StyleMatch today and experience the future of fashion shopping! 👗✨

Built with ❤️ for the Algolia Agent Studio Challenge

Tags: #algolia #challenge #ai #fashion #ecommerce #conversationalai #nextjs #gemini

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