AI Catalog Management

AI catalog management is the use of artificial intelligence to automate and improve how product catalogs are created, maintained, enriched, and distributed. For retailers with hundreds or thousands of SKUs, manually maintaining accurate, complete product data across every sales channel is a growing operational challenge -- one that AI is uniquely suited to solve.

Traditional catalog management involves manual data entry, spreadsheet-based updates, and channel-specific formatting. AI catalog management replaces much of this manual work with:

• Automated enrichment: AI generates missing product descriptions, attributes, and tags from images, existing data, and web sources
• Intelligent categorization: Products are automatically categorized according to each channel's taxonomy (Google Product Categories, Amazon Browse Nodes, etc.)
• Quality detection: AI identifies incomplete listings, inconsistent data, and potential errors before they reach sales channels
• Cross-channel optimization: Product data is automatically formatted and optimized for each destination -- whether that's Google Merchant Center, a Shopify storefront, or ChatGPT Shopping

Catalog management has always mattered for ecommerce, but the rise of AI shopping agents makes it critical:

AI agents can only recommend what they understand. When ChatGPT or Perplexity evaluates your products, they see your structured data -- not your beautiful website, not your brand story, not your in-store experience. Products with incomplete data are invisible to AI agents.

Channel proliferation is accelerating. Retailers now need optimized product feeds for traditional channels (Google Shopping, Meta, Amazon) and AI channels (ChatGPT, Perplexity, Google AI Mode, Copilot). Each has different data requirements and optimization strategies.

Manual processes don't scale. A retailer with 10,000 SKUs across 6 channels manages 60,000 product listings. Updating descriptions, adjusting for each channel's format, and maintaining accuracy across all of them is not feasible with spreadsheets and manual effort.

AI catalog management platforms like Paz.ai ingest product catalogs from any source (Shopify, Magento, Salesforce Commerce Cloud, CSV), enrich the data using AI, and generate optimized feeds for every channel -- traditional and AI-powered -- from a single catalog connection.

AI-powered enrichment. Product data enrichment uses AI to generate or improve product titles, descriptions, and attributes. This includes writing natural language descriptions optimized for AI comprehension, extracting attributes from product images, and standardizing data formats.

Automated feed generation. Product feed management across channels -- generating Google Shopping feeds, Meta product catalogs, and AI-optimized feeds (OpenAI commerce feed spec, Perplexity Merchant Program format) from a single source of truth.

Quality scoring. AI evaluates each product listing against channel-specific requirements and best practices, scoring completeness, accuracy, and optimization level. Retailers can prioritize which products need attention.

Real-time inventory sync. MCP integration enables real-time inventory and pricing updates to AI shopping agents, preventing recommendations for out-of-stock products.

Performance analytics. Track how products perform across channels -- which products are being recommended by AI agents, which are getting clicks, and which are converting.