Catalog Enrichment System

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1.2.0

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14-Oct-2025

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Antonio Martinez (NVIDIA)

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Product catalogs often contain minimal, low-quality information with basic product images and sparse descriptions. This limits customer engagement, search discoverability, and overall user experience. Manual enrichment of catalog data is time-consuming, error-prone, and doesn't scale. Human categorization and tagging of products is particularly susceptible to inconsistencies, subjective interpretations, and classification errors that can negatively impact search functionality and user experience...

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A GenAI-powered catalog enrichment system that transforms basic product images into comprehensive, rich catalog entries with enhanced titles, descriptions, categories, tags, variation images (2D/3D), and short video clips. The system leverages social media content analysis to incorporate trending styles, real-world usage patterns, and customer sentiment into product enrichment, ensuring catalog data stays current with market trends...

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1. **Input**: User submits product image along with existing product JSON data and optional locale specification 2. **Social Media Analysis** (Optional): System retrieves and analyzes social media content for similar products to extract: - Trending styles and terminology - Real-world usage scenarios and contexts - Customer sentiment and common feedback - Popular color combinations and styling preferences - Complementary products and accessories 3. **Content Augmentation**: System uses NVIDIA Nemotron VLM to enhance existing product data by:...

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### FR-1: Image Input Processing - Accept single or multiple product images (JPEG, PNG formats) - Support common image resolutions and file sizes - Validate image quality and content relevance ### FR-2: VLM Content Augmentation - Integrate with NVIDIA Nemotron VLM - Accept existing product JSON data alongside product images...

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### TR-1: Model Integration - NVIDIA Nemotron VLM API integration with locale-aware prompting - NVIDIA Nemotron LLM integration for culturally-aware prompt planning - FLUX model deployment for localized image generation - Microsoft TRELLIS model integration - Open-source video generation model setup ### TR-2: Infrastructure - GPU-enabled compute resources for model inference - Scalable storage for generated assets - Queue management for batch processing - API endpoints for system interaction

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### US-1: Basic Product Enrichment **As a** catalog manager **I want to** upload a product image along with existing product data and receive AI-enhanced catalog data **So that** I can augment and improve my existing catalog entries with richer, more accurate information ### US-1a: Localized Product Augmentation **As a** international catalog manager **I want to** upload a product image with existing product data and a target locale to receive culturally-appropriate enhanced catalog data **So that** I can improve my existing product listings with region-specific, culturally-relevant content that resonates with local customers...

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- **Processing Time**: <1 minute per product for complete enrichment (including quality assessment) - **Content Quality**: Generated descriptions and titles achieve >90% relevance rating in target locale - **Cultural Accuracy**: Generated backgrounds and contexts achieve >85% cultural appropriateness rating from regional reviewers...

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- [x] ~~FR-1: Image Input Processing~~ - [x] ~~FR-2: VLM Content Extraction~~ - [ ] ~~FR-3: 2D Image Variation Generation~~

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