A trend-arbitrage engine that identifies emerging social signals and "re-skins" Shopify vendors' products to embody specific internet subcultures. By attaching viral terminology to everyday items, we allow consumers to purchase the "status" of a trend before it reaches mainstream saturation.

The inspiration came from observing the massive disconnect between digital velocity and physical retail. Trends like "Aura," the "Chill Guy," and the "Off-Duty CEO" create overnight demand for specific identities, yet traditional brands take months to react. We realized that any generic item could become a high-status asset if it were rebranded with the right "cultural code" at the exact moment a trend peaks.

TrendID is a trend-arbitrage engine. We identify emerging social signals and "re-skin" Shopify vendors' products to embody a specific internet subculture. By attaching viral terminology to everyday items, we allow consumers to purchase the "status" of a trend before it reaches mainstream saturation.

We use a comprehensive pipeline that processes trends end-to-end:

1. Trend Discovery: We begin by identifying seven metadata tags per call via API-based web scraping using Perplexity's Sonar API.

2. Video Analysis: These tags are fed into the YouTube Data API (Google Cloud), which scrapes two videos published within a short timeframe with the highest viewer/hour gross rate across these seven metrics.

3. Semantic Analysis: Videos are processed through TwelveLabs Pegasus 1.2 API to uncover video-based semantic insights—from scene descriptions to analysis of which actions place videos in trend territory.

4. Data Structuring: The output is a structured JSON file focusing on key trend aspects, paired with another JSON encapsulating all key Shopify store data including product types.

5. AI Optimization: This creates a semantic planning table pushed into MongoDB—the hive mind of marketable tweaks to maximize traction and sales.

6. Autonomous Updates: We use Shopify's Admin GraphQL API to autonomously update store pages with trend-aligned product descriptions, tags, and marketing copy.

Backend:

• Python (Flask) - API and pipeline orchestration
• Node.js (Express) - Shopify API integration
• MongoDB Atlas - Trend and suggestion storage

AI/ML:

• TwelveLabs Pegasus 1.2 - Video semantic analysis
• Google Gemini - Content generation
• Perplexity Sonar API - Trend metadata scraping

APIs:

• YouTube Data API v3 (Google Cloud)
• Shopify Admin REST & GraphQL APIs

Infrastructure:

• Render - Node.js backend hosting
• Vercel - Frontend hosting
• MongoDB Atlas - Database

Frontend:

• React + TypeScript
• Shopify Polaris UI components
• Vite build system

Meme/Culture: aura, chill guy, sigma, 365 buttons, demure, very mindful

Food: dubai chocolate, matcha, boba, crumbl cookie, tinned fish

Aesthetic: y2k, cottage core, dark academia, clean girl, mob wife, off-duty CEO

Animals: capybara, axolotl

Lifestyle: hot girl walk, that girl, glow up

Pop Culture: stanley cup, lululemon style

Built for UofTHacks 13 - combining AI, video analysis, and e-commerce automation to bridge the gap between viral trends and retail velocity.