Executive Summary
V1SIX
A proof-of-concept generative commerce platform that solves the "Netflix Paradox" by dynamically generating personalized shopping interfaces based on user intent.
V1 Architecture: Manual API calls with one-shot layout generation. See V2 Documentation for the Agent-Native architecture with CopilotKit and real-time refinement.
The Problem
Traditional e-commerce is static and "data-starved."
- ✗Data Density Gap: Retailers rely on sparse purchase history (one coat every 3 years), while streaming services use dense behavioral signals (skipping songs, watching episodes).
- ✗Inventory Physics: Unlike digital media (infinite supply), physical inventory is finite. Personalization fails if it recommends out-of-stock items.
- ✗Intent Mismatch: Users alternate between "Hunter" (transactional, urgent) and "Gatherer" (discovery) modes. Static pages cannot accommodate both.
The V1 Solution
Move from Static Pages to Generative UI.
Instead of fetching a pre-built template, the frontend sends user context to an API endpoint which calls GPT-4o to "merchandise" the layout in real-time. The store shelves rearrange themselves the moment the user enters.
- ✓Intent-Driven: User explicitly selects persona (Hunter/Gatherer), solving the data density problem.
- ✓Inventory-Grounded: AI only selects from in-stock products, solving the physics constraint.
- ✓Context-Aware: Weather, time, and urgency influence product selection.
Two Distinct Experiences
The Hunter
"I Know What I Need"
- • Dense 4-column grid layout
- • No hero images—efficiency first
- • Quick-buy cards with Add to Cart
- • Products sorted by stock & relevance
- • Urgency banners for context
The Gatherer
"Inspire Me"
- • Hero section with mood-setting headline
- • Magazine-style editorial layouts
- • Curated sections with storytelling
- • Aspirational product photography
- • Discovery-focused browsing
V1 Architecture Decisions
| Decision | Choice | Rationale |
|---|---|---|
| AI Model | GPT-4o | Best structured output support for reliable JSON generation |
| Generation Method | generateObject() with Zod | Type-safe schema validation prevents hallucinations |
| API Pattern | Manual fetch() calls | Simple, one-shot request/response |
| Inventory | JSON file (not database) | Zero latency for POC; easy migration path to Supabase |
| Hallucination Prevention | 3-layer validation | Prompt + Schema + Post-validation with retries |
V1 Limitations
What V1 cannot do (addressed in V2):
- •No Refinement: User cannot say "too expensive" and get a modified layout
- •Static Inventory: Stock levels don't update in real-time
- •One-Shot Generation: Each request is independent—no conversation memory
- •Manual Orchestration: Frontend manages all state and API calls
See V2 Documentation for Agent-Native architecture with CopilotKit