Product Requirements Document (PRD)

Project: Catalog Enrichment System

Version: 1.3.0 Last Updated: 16-Apr-2026
Owner: Antonio Martinez (NVIDIA)

Problem Statement

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.

Additionally, product catalogs quickly become outdated as market trends, customer preferences, and styling conventions evolve. Catalog managers lack visibility into how customers are actually using products in real-world contexts, what terminology resonates with target audiences, and what trends are emerging on social media platforms. This disconnect between catalog content and market reality leads to missed opportunities for engagement and conversion.

Solution Overview

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.

Core User Flow

Input: User submits product image along with existing product JSON data and optional locale specification
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
Content Augmentation: System uses NVIDIA Nemotron VLM to enhance existing product data by:
- Enriching product title with more descriptive details (localized to target region)
- Expanding product description with richer, more verbose content (using regional terminology)
- Improving and refining attributes (e.g., expanding "Black" to "Matte Black with Silver Hardware")
- Enhancing categories and subcategories based on visual analysis
- Generating more comprehensive and accurate tags
- Validating and correcting product specifications against visual evidence
- Incorporating trending terminology and insights from social media analysis (when available)
Cultural Prompt Planning: System uses NVIDIA Nemotron LLM to create culturally-aware prompts for image generation based on:
- Product analysis
- Target locale/country cultural context
- Regional aesthetic preferences
- Social media trend insights (when available)
Localized Image Generation: System creates variation images using FLUX models with culturally-appropriate backgrounds
Quality Assessment: System evaluates generated images using VLM-based reflection to verify:
- Product consistency and fidelity
- Size and scale proportions
- Anatomical accuracy (if applicable)
- Background quality and realism
3D Asset Creation: System generates 3D product assets using Microsoft's TRELLIS model
Video Generation: System produces 3-5 second product video clips using open-source models
Output: Culturally-enriched catalog entry with quality-assessed generated assets optimized for target market, enhanced with social media insights

Functional Requirements

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
Analyze visual product features and compare with existing data
Augment and enrich existing titles with more descriptive, compelling content
Expand existing descriptions with richer, more detailed information
Enhance product attributes with visual insights (colors, materials, style details)
Refine and improve categories, subcategories, and tags
Validate specifications against visual evidence
Preserve structured data format including specs, attributes, and metadata

FR-3: 2D Image Variation Generation

Use NVIDIA Nemotron LLM to plan and generate optimized prompts for image variations
Use FLUX models to create product variations based on generated prompts
Generate multiple angle views
Create lifestyle/contextual images
Maintain product accuracy and consistency

FR-4: 3D Asset Generation

Integrate Microsoft TRELLIS model
Generate 3D models from 2D product images
Export 3D assets in standard formats
Ensure model quality and accuracy

FR-5: Video Clip Generation

Create 3-5 second product video clips
Use open-source video generation models
Generate smooth, professional-quality clips
Support common video formats (MP4, WebM)

FR-6: Multi-Language & Cultural Localization

Support multiple output languages including English, Spanish, and French across 10 regional locales
Generate product titles, descriptions, categories, and tags in selected regional language variant
Maintain language consistency across all text outputs using regional terminology (e.g., "ordenador" vs "computadora")
Generate culturally-appropriate product backgrounds reflecting regional aesthetics and lifestyle contexts
Adapt image generation prompts to include cultural elements specific to target country/region

FR-7: Social Media Content Integration

Extract trending styles, real-world usage patterns, and customer reviews from social media platforms for similar products
Analyze visual and video content from social media sources (TikTok, YouTube, Instagram, etc.)
Identify product usage contexts, styling trends, and customer sentiment from user-generated content
Integrate social media insights into catalog enrichment to enhance product descriptions and tags with trending terminology
Support both API-based and MCP-based integration patterns for social media data retrieval
Extract key visual elements from social media content:
- Popular color combinations and styling preferences
- Real-world product usage scenarios and contexts
- Complementary products frequently shown together
- Seasonal trends and emerging fashion/lifestyle patterns
Aggregate customer sentiment and common feedback themes from social media reviews and comments
Identify trending hashtags, keywords, and product descriptors relevant to similar products
Maintain compliance with platform terms of service and data privacy regulations
Support both real-time monitoring and periodic batch analysis modes

FR-8: Brand Voice & Taxonomy Customization

Accept custom brand instructions to guide content enrichment and generation
Support brand-specific voice, tone, and writing style preferences
Apply custom taxonomy and categorization guidelines consistent with brand standards
Incorporate brand-specific terminology, keywords, and messaging preferences
Enable brands to define product classification rules and hierarchies
Maintain consistency across all enriched content (titles, descriptions, tags, categories)
Allow optional brand instructions parameter alongside product data and images

FR-9: Automated Quality Assessment for Generated Images

Integrate VLM-based reflection module to evaluate generated image variations
Compare generated images against original product photos for quality control
Evaluate product consistency (colors, materials, textures, reflective properties)
Assess size and scale proportions (product must appear realistic in new context)
Verify anatomical accuracy (hands must have correct finger count and natural proportions)
Validate background quality (photorealism, appropriate context, technical correctness)
Generate quality score (0-100 scale) with strict evaluation criteria
Provide detailed list of detected quality issues for developer feedback
Support automated filtering or flagging of low-quality generated images
Ensure background differences from original are not penalized (backgrounds should differ)

FR-10: Product FAQ Generation

Generate 3-5 frequently asked questions and answers for each product from enriched catalog data
FAQs are derived from the final enriched catalog data (after VLM analysis, user data merge, and branding)
Questions cover practical shopper topics: materials, care instructions, sizing, use cases, compatibility, durability
Answers are concise (1-3 sentences), factual, and grounded in the enriched product data
Support locale-aware FAQ generation across all 10 supported regional locales
Separate /vlm/faqs endpoint allows asynchronous generation — details display immediately while FAQs load in the background
UI displays FAQs in a dedicated tab with collapsible accordion items

FR-12: Product Manual PDF Enhancement for FAQs

Accept an optional product manual PDF to enrich FAQ generation with detailed product knowledge
Stateless architecture: /vlm/manual/extract processes the PDF, returns structured knowledge as JSON, and frees all server-side resources — no server-side storage required
Dynamic query generation: LLM generates 5-8 product-type-specific questions based on title and categories (not description) to avoid FAQ duplication with the description
Targeted RAG retrieval: text is chunked, embedded in-memory using NVIDIA nv-embedqa-e5-v5, and relevant chunks retrieved per query via cosine similarity
When manual knowledge is provided to /vlm/faqs, generate up to 10 richer FAQs that surface details from the manual that go beyond the description
FAQ prompt explicitly avoids duplicating information already in the description
Supports concurrent processing: each request is independent with no shared server state, enabling batch processing of many products in parallel
UI provides a "Product manual for FAQs" upload section under Advanced Options
PDF file size limit: 50 MB; embedding requests batched at 128 chunks per call

FR-11: Policy Compliance Checking

Accept PDF policy documents through a persistent policy library (/policies endpoint)
Parse and normalize uploaded PDFs into structured policy summaries
Embed normalized policy records using NVIDIA embeddings and store in Milvus vector database
During product analysis, perform semantic retrieval of relevant policy records
Run compliance classification against enriched product data and retrieved policy records
Return pass/fail status with matched policies, rule details, reasons, evidence, and warnings
Support deduplication of repeated policy uploads by content hash
Display compliance results in the UI with visual pass/fail indicators

FR-13: Protocol Schema Export (ACP & UCP)

Generate ACP (Agentic Commerce Protocol) and UCP (Unified Commerce Protocol) schema instances from enriched product data
Use LLM to extract structured attributes (brand, material, age_group, gender, product_details, product_highlights, short_title, google_product_category) from enriched title and description
Merge LLM-extracted fields with enriched data and deterministic defaults (availability, condition, adult, is_bundle) into full schema templates
ACP schema covers product, pricing, FAQs, agent actions, fulfillment, campaigns, certifications, energy efficiency, bundling, marketplace, and metadata
UCP schema follows Google Merchant Center Product Data Specification across all 9 sections (basic product data, price/availability, product category, identifiers, detailed description, shopping campaigns, marketplaces, destinations, shipping/returns)
UCP uses structured_title and structured_description with digital_source_type: "trained_algorithmic_media" for AI-generated content
Single /protocols/generate endpoint calls LLM once and returns both schemas
UI displays schemas in a Protocols tab with ACP/UCP sub-tabs, syntax-highlighted JSON, and copy-to-clipboard
Schema generation fires in the background after FAQ generation completes, ensuring FAQs are included in both schemas

Technical Requirements

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

TR-3: Performance

Process single product within 1 minute
Support concurrent processing of multiple products
Maintain >95% model inference success rate

TR-4: Data Management

Secure storage of uploaded images
Organized asset storage structure
Metadata tracking and versioning
Cleanup policies for temporary files

TR-5: Social Media Integration

API integration with social media platforms (TikTok, YouTube, Instagram, Pinterest)
Support for MCP (Model Context Protocol) based data retrieval
Web scraping infrastructure for platforms without API access
Rate limiting and quota management for API calls
Content deduplication and similarity detection across social media sources
Video analysis pipeline for extracting frames and analyzing video content
Natural language processing for sentiment analysis and review extraction
Trend detection algorithms for identifying emerging patterns
Data caching and refresh strategies for social media content
Privacy compliance framework (GDPR, CCPA) for user-generated content
Content filtering to exclude inappropriate or irrelevant material

User Stories

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

US-2: Batch Processing

As a catalog manager
I want to process multiple products simultaneously
So that I can efficiently enrich large catalog datasets

US-3: Asset Generation

As a marketing team member
I want to receive multiple image variations and video content
So that I can use diverse assets across different marketing channels

US-3a: Cultural Asset Generation

As a international marketing team member
I want to receive culturally-localized image variations that reflect regional aesthetics
So that I can create marketing campaigns that feel authentic and familiar to local audiences

US-4: 3D Visualization

As a e-commerce platform
I want to display 3D product models
So that customers can interact with products before purchase

US-5: Trend-Informed Product Enrichment

As a catalog manager
I want to enrich my product descriptions with trending styles and terminology from social media
So that my catalog stays current with market trends and uses language that resonates with customers

US-5a: Social Media Sentiment Analysis

As a product manager
I want to understand customer sentiment and common feedback about similar products from social media reviews
So that I can improve product descriptions by addressing common questions and highlighting popular features

US-5b: Real-World Usage Context

As a marketing team member
I want to see how customers are actually using and styling similar products in real-world scenarios from social media
So that I can create more authentic and relatable marketing content and product imagery

US-5c: Competitive Intelligence

As a merchandising manager
I want to identify trending color combinations, styling preferences, and complementary products from social media analysis
So that I can optimize product assortments and create effective cross-selling opportunities

US-6: Brand-Consistent Enrichment

As a brand manager
I want to provide custom brand voice, tone, and taxonomy guidelines to the enrichment system
So that all generated product content maintains consistency with my brand identity and uses our preferred terminology and classification standards

US-7: Automated Quality Control

As a catalog operations manager
I want to receive automated quality assessments with detailed scoring and issue detection for generated product images
So that I can quickly identify and filter out low-quality variations without manual review, ensuring only high-quality assets enter my catalog

US-8: Product FAQ Generation

As a e-commerce content manager
I want to automatically generate frequently asked questions and answers for each product based on its enriched catalog data
So that I can populate product FAQ sections without manual copywriting, improving the customer shopping experience

US-9: Policy Compliance Checking

As a catalog compliance officer
I want to upload policy PDFs and have the system automatically check enriched product listings against those policies
So that I can ensure all catalog entries comply with marketplace regulations and internal guidelines before publishing

US-10: Manual-Enhanced Product FAQs

As a e-commerce content manager
I want to upload a product manual PDF and have the system generate richer FAQs that include specific details like specs, care instructions, safety warnings, and warranty information
So that my product FAQ sections provide genuine value beyond what the description already covers, reducing customer support inquiries

Success Criteria

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
Asset Generation: Successfully generate 2D variations, 3D models, and video clips for >95% of input products
Quality Assessment Accuracy: Automated quality scores correlate >85% with human expert evaluations
Quality Detection: System identifies >90% of major quality issues (scale problems, anatomical errors, material inconsistencies)
Localization Coverage: Support 10 regional locales across English, Spanish, and French
System Reliability: 99% uptime for processing requests
User Satisfaction: Positive feedback on generated content quality and cultural authenticity
Social Media Integration Accuracy: Extracted trends and sentiment achieve >85% relevance to target product category
Trend Freshness: Social media insights refreshed within 24-48 hours of platform posting
Content Diversity: Aggregate insights from minimum of 50+ relevant social media posts per product category

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