Product Data Enrichment

Product data enrichment is the process of enhancing raw product catalog data with additional attributes, richer descriptions, better categorization, and supplementary metadata to improve product discoverability, search relevance, and conversion rates.

Raw catalog data from ERPs and PIMs is often minimal -- a SKU, a short title, a price, and maybe a basic description. Enrichment fills the gaps: adding materials, dimensions, use cases, compatibility information, lifestyle imagery, and structured attributes that channels require.

As search engines and ecommerce platforms adopt AI-driven shopping experiences, product visibility increasingly depends on the quality and completeness of product data rather than traditional keywords alone (Feedonomics, 2025). AI agents evaluate products based on structured attributes, rich descriptions, and data completeness -- not keyword density.

When an AI agent processes a query like "waterproof hiking boots for wide feet under $200," it evaluates products based on specific attributes: waterproof rating, width options, price, and intended use. Products missing these attributes get filtered out, regardless of how well they match the consumer's actual needs.

GEO research shows that AI systems prioritize information density over keyword optimization. The Princeton/Georgia Tech GEO study found that adding specific, sourced data points can boost visibility in AI responses by up to 40% (Aggarwal et al., SIGKDD 2024). This applies to product data too -- richer attributes mean better AI comprehension and more accurate recommendations.

Modern enrichment increasingly uses AI to scale the process. AI-powered enrichment tools can generate detailed descriptions from basic product data, suggest missing attributes based on product category patterns, and optimize content for natural language queries -- transforming catalog data into what Paz.ai calls an "agentic catalog."

• Description enhancement: Expanding brief product titles into natural language descriptions that AI agents can parse
• Attribute completion: Adding missing specifications -- materials, dimensions, weight, compatibility, certifications
• Category mapping: Aligning products to each channel's taxonomy (Google Product Category, Amazon Browse Nodes, AI protocol schemas)
• Image optimization: Adding alt text, lifestyle imagery, and multiple angles for visual AI systems
• Cross-channel compliance: Ensuring data meets the specific requirements of each platform -- OpenAI's commerce feed spec, Google Merchant Center, Amazon's content standards