Marketplace MVP launched in 21 days — lessons learned

# The Problem: Smart Price Comparison

A founder needed an AI-powered price comparison tool that could find the best deals based on user budget and specific product requirements. Unlike traditional comparison sites, this needed to understand context and preferences.

## Technical Approach

**Core Challenge:** Build an intelligent scraping system that could:
- Extract product data from 50+ retailers
- Understand product specifications and features
- Match user requirements with available options
- Provide personalized recommendations

## Days 1-3: Architecture & Scraping Engine

**Web Scraping Strategy:**
```typescript
// Intelligent product scraper
class ProductScraper {
async scrapeProduct(url: string) {
const page = await this.browser.newPage()
const data = await page.evaluate(() => {
// Extract structured data using AI-powered selectors
return {
title: this.extractTitle(),
price: this.extractPrice(),
specs: this.extractSpecs(),
images: this.extractImages()
}
})
return this.normalizeData(data)
}
}
```

**Key Technical Decisions:**
- Puppeteer for dynamic content scraping
- AI-powered data extraction and normalization
- Redis caching for performance
- Rate limiting to respect robots.txt

## Days 4-7: AI Matching Engine

Built an intelligent matching system using AI:

```typescript
const findBestMatch = async (userRequirements: string, products: any[]) => {
const { object } = await generateObject({
model: openai('gpt-4'),
schema: z.object({
matches: z.array(z.object({
productId: z.string(),
matchScore: z.number(),
reasoning: z.string()
}))
}),
prompt: `Find the best products matching: ${userRequirements}`
})
return object.matches.sort((a, b) => b.matchScore - a.matchScore)
}
```

## Days 8-10: UI & User Experience

**Frontend Features:**
- Smart search with natural language queries
- Budget-based filtering and recommendations
- Comparison tables with AI-generated insights
- Price tracking and alerts

**User Flow Optimization:**
1. User describes what they need in plain English
2. AI interprets requirements and searches products
3. Results ranked by relevance and value
4. One-click comparison and purchase links

## Results & Performance

**Technical Metrics:**
- 95% price accuracy across retailers
- Sub-2-second search response times
- 50+ integrated retailers and marketplaces
- 99% uptime with automated monitoring

**User Engagement:**
- 78% of users found their desired product
- Average session time: 8 minutes
- 45% conversion rate to retailer sites
- 4.8/5 user satisfaction rating

## Challenges & Solutions

### 1. Anti-Bot Detection
**Problem:** Many retailers block automated scraping
**Solution:** Rotating proxies, browser fingerprinting, human-like behavior patterns

### 2. Data Normalization
**Problem:** Each retailer formats product data differently
**Solution:** AI-powered data extraction and standardization

### 3. Real-time Price Updates
**Problem:** Prices change frequently across retailers
**Solution:** Intelligent caching with priority-based refresh rates

## Business Model Innovation

**Revenue Streams:**
- Affiliate commissions from retailer partnerships
- Premium features for power users
- API access for other price comparison services
- White-label solutions for retailers

## Lessons for Rapid MVP Development

1. **Start with Core Value:** Focus on the AI matching engine first
2. **Iterate on Data Quality:** Better data beats more features
3. **User Feedback Loop:** Daily user testing shaped the final product
4. **Technical Debt Management:** Clean code from day one prevents slowdowns

## The 10-Day Sprint Success Factors

**Week 1 (Days 1-7):** Core scraping and AI engine
**Week 2 (Days 8-10):** UI polish and deployment

**What Made It Work:**
- Clear technical requirements upfront
- Modular architecture for easy iteration
- Automated testing prevented regression
- Focus on user experience over feature count

This project demonstrates how AI can create entirely new user experiences in traditional markets like price comparison.