Product Requirements Document

Complete specification for building scalable commerce experiences with static components and dynamic data

Executive Summary

Problem Statement

Modern e-commerce platforms face a critical tension between performance (fast load times, offline capability) and personalization (AI-driven recommendations, real-time data). Traditional approaches force developers to choose:

  • Static Sites: Fast but limited personalization
  • SPAs/SSR: Dynamic but slow, complex build processes

This architecture solves the problem by separating static components from dynamic data, enabling both performance and personalization without compromise.

Solution Overview

Static Component/Dynamic Data Design Architecture uses:

  • Webflow Exports: Pure HTML/CSS components with no build step
  • Alpine.js: Lightweight reactive data binding (15KB)
  • Cloudflare Workers: Edge-based API routing and caching
  • Xano + OpenAI: Real-time AI enrichment middleware
  • Shopify Meta Objects: UCP-compliant data storage
  • n8n: Background automation and BigQuery sync
  • GTM/GA4: Client-side analytics and remarketing

Core Requirements

R1: Static Component Library

Requirement: All UI components must be static HTML/CSS files exported from Webflow with no JavaScript framework dependencies.

Acceptance Criteria:

  • Components load in < 100ms on 3G connection
  • Zero build step required for deployment
  • Works offline with Service Worker caching
  • Accessible (WCAG 2.1 AA compliant)

R2: Reactive Data Binding

Requirement: Alpine.js provides reactive data binding between static components and dynamic API data without virtual DOM overhead.

Acceptance Criteria:

  • x-data, x-bind, x-model directives work on all components
  • API responses update UI within 50ms
  • No page reloads required for data updates
  • Total JavaScript bundle < 50KB

R3: Edge-Based API Routing

Requirement: Cloudflare Workers route API requests to Xano middleware with sub-50ms edge latency and automatic failover.

Acceptance Criteria:

  • P95 latency < 50ms globally
  • 99.99% uptime SLA
  • Automatic DDoS protection
  • Rate limiting per IP/user

R4: AI-Powered Enrichment

Requirement: Xano calls OpenAI GPT-4o to enrich customer data with AI segments, LTV predictions, and personalized recommendations.

Acceptance Criteria:

  • AI inference completes in 200-500ms
  • Structured JSON output enforced
  • Confidence scores > 0.7 for all segments
  • Fallback to rule-based logic if API fails

R5: UCP Compliance

Requirement: Expose Universal Commerce Protocol endpoint at /.well-known/ucp for AI agent discovery of commerce capabilities.

Acceptance Criteria:

  • JSON schema matches UCP specification
  • All 5 capabilities exposed (Product Discovery, Cart, Identity linking, Checkout, Order)
  • Endpoint responds in < 100ms
  • CORS headers allow AI agent access

R6: Analytics Integration

Requirement: GTM DataLayer captures all user events and forwards to GA4 for analytics, remarketing, and conversion tracking.

Acceptance Criteria:

  • consent_updated, purchase, page_view, add_to_cart events tracked
  • Custom dimensions for AI segments
  • E-commerce transaction data complete
  • BigQuery export enabled for SQL analysis

Implementation Roadmap

1

Phase 1: Foundation (Week 1-2)

Set up Webflow project, export HTML/CSS components, configure Netlify deployment with custom domain

2

Phase 2: Data Layer (Week 3-4)

Implement Alpine.js data binding, create Xano API endpoints, configure Cloudflare Workers proxy

3

Phase 3: AI Integration (Week 5-6)

Connect Xano to OpenAI GPT-4o, build customer segmentation prompts, test structured outputs

4

Phase 4: Commerce Integration (Week 7-8)

Set up Shopify Meta Objects, implement GraphQL mutations, store AI segments and consent data

5

Phase 5: Automation (Week 9-10)

Build n8n workflows for BigQuery sync, scheduled reports, abandoned cart recovery

6

Phase 6: Analytics & UCP (Week 11-12)

Configure GTM/GA4 tracking, create /.well-known/ucp endpoint, test AI agent discovery

7

Phase 7: Testing & Launch (Week 13-14)

Load testing, security audit, performance optimization, production deployment

Success Metrics

Metric Target Measurement Method
Page Load Time (LCP) < 1.5s Lighthouse CI, Core Web Vitals
API Response Time (P95) < 500ms Cloudflare Analytics
AI Inference Time 200-500ms Xano function logs
Uptime > 99.9% UptimeRobot, Pingdom
Conversion Rate Lift +15% GA4 A/B testing
AI Segment Accuracy > 85% Manual validation sample
GTM Event Capture Rate > 98% GA4 event count vs. server logs

Technical Constraints

Performance Constraints

  • JavaScript Bundle: Total < 50KB gzipped
  • CSS Bundle: Total < 30KB gzipped
  • Image Optimization: WebP format, lazy loading
  • API Calls: Max 5 per page load

Security Constraints

  • Authentication: JWE tokens with 1-hour expiry
  • Rate Limiting: 100 req/min per IP
  • CORS: Whitelist approved domains only
  • PII Handling: GDPR/CCPA compliant encryption

Scalability Constraints

  • Shopify API: 2 req/sec rate limit
  • OpenAI API: 10,000 req/day tier
  • Cloudflare Workers: 100k req/day free tier
  • BigQuery: 1TB query/month free tier

Browser Support

  • Modern Browsers: Chrome 90+, Firefox 88+, Safari 14+
  • Service Workers: Required for offline support
  • ES6 Modules: No transpilation needed
  • CSS Grid/Flexbox: Native support required

Open Questions & Future Work

Questions to Resolve

  1. Multi-language Support: How to handle i18n in static components? Use Alpine.js with JSON translation files?
  2. A/B Testing: Should we use Cloudflare Workers for edge-based A/B tests or rely on GTM Optimize?
  3. Real-time Updates: Do we need WebSocket support for live inventory updates, or is polling sufficient?
  4. Mobile App: Can we reuse the same API layer for a React Native mobile app?

Future Enhancements

  • Voice Commerce: Integrate with Alexa/Google Assistant via UCP endpoint
  • AR Product Visualization: Add Three.js/A-Frame for 3D product previews
  • Predictive Shipping: Use AI to predict delivery times based on historical data
  • Dynamic Pricing: Real-time price optimization based on demand and inventory