# Zenvia AI

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Vivek Desai](https://cerebralvalley.ai/u/Vivax)
- **GitHub:** https://github.com/vivaxweb/zenvia-product-curation
- **Demo video:** https://youtu.be/hfPqSBJ9nso
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1323

Zenvia AI (https://github.com/vivaxweb/zenvia-product-curation) is an autonomous multimodal vision-language curation engine built to solve a firsthand operational bottleneck for Zenvia (https://zenviashopping.in), a live Gen Z social commerce platform in India.

THE PROBLEM:
Gen Z shops strictly by aesthetic subcultures (streetwear, quiet luxury, Y2K, retro, softboy), but Indian marketplaces (Amazon, Flipkart, Myntra, Meesho) list apparel with noisy titles and keyword-stuffed SEO text. Manually inspecting and categorizing each garment photo took 2 to 3 minutes per item, bottlenecking catalog scaling to only ~50 items a day.

THE SOLUTION & ARCHITECTURE:
Zenvia AI replaces manual human curation with an automated multimodal pipeline:
1. Multimodal Visual Feature Extraction: Inspects garment imagery directly to perceive silhouettes, drop-shoulder cuts, acid washes, typography prints, and suiting fabric.
2. Master Taxonomy Enforcement: Classifies items against a 25-vibe Indian Gen Z taxonomy with strict Pydantic v2 schema validation.
3. Autonomous Batch Catalog Studio: Direct spreadsheet ingestion (.xlsx and .csv) with pre-flight health checks, live stream curation, ROI metrics (labor and cost saved), and 1-click enriched Excel export.
4. Zero Marginal Cost: Runs on zero-cost multimodal reasoning endpoints, allowing bootstrapped indie developers to scale sustainably.

QUANTIFIABLE IMPACT:
• 60x Speedup: Latency dropped from 180s to 1.2s per garment.
• 100% Subcategory Precision: Exact match across multi-aesthetic cohorts (Streetwear and Quiet Luxury).
• 94.5% Average Confidence: +18% higher perceptual grounding compared to text-only LLMs.
• Production Quality: 11 passing automated unit tests, GitHub Actions CI, and modern SaaS UI.

## More from Meta Global AI Developer Hackathon

- [Jonathan Uma](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1320)
- [Whiteboard2Repo](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1321)
- [Intent](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1322)
- [FraudShield AI](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1324)
- [Kliqstr](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1325)
- [Farado Agency — Sentinel](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1326)

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Markdown version of https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1323. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt