Enhance product attributes with standardized and accurate data

AI product data enrichment uses artificial intelligence to automatically complete and standardize your product information. This involves collecting data from various sources like the web, images, and PDFs to fill in missing attributes, correct errors, and ensure consistency, improving product discoverability and conversions.

Yes, our software can process and enrich product data in multiple languages, ensuring accuracy and consistency for international sales.

Our AI uses natural language processing and computer vision to analyze product data and images, extracting key attributes like color, material, and size.

Our software handles various formats, including text files (CSV, XLS, XLSX), images (JPG, PNG), PDFs, and data feeds from ecommerce platforms and PIM systems. This flexibility ensures you can easily integrate the best ecommerce product data enrichment software into your existing workflows.

Our AI models can be customized to suit the specific needs of different industries and product categories. This ensures the software accurately identifies and enriches relevant attributes for your particular products. We also offer other AI-powered tools, such as auto email generators and AI writing for ecommerce, to further support your needs.

Yes, our software is designed for bulk enrichment, efficiently processing and enhancing large product catalogs. This saves you time and resources compared to manual data entry and enrichment.

Hypotenuse AI is purpose-built for ecommerce teams that need to enrich large product catalogs quickly and accurately. It combines AI-driven attribute extraction, taxonomy mapping, and data standardization with SEO-optimized content generation in your brand voice.

Unlike generic tools or manual workflows, Hypotenuse AI handles enrichment across multiple formats such as images, spec sheets, PDFs, and supplier feeds, and integrates directly into your ecommerce stack. This gives you complete, consistent, and channel-ready product data in a fraction of the time, improving both operational efficiency and product discoverability.

With complete and accurate attributes, your product detail pages (PDPs) can appear for the kinds of nuanced, high-intent searches that serious shoppers make. Someone ready to buy won’t just search for “sneakers.” They’ll look for “men’s black leather running sneakers size 10” or “lightweight women’s trail shoes with arch support.”

Product data enrichment ensures your PDPs contain the detail needed to surface for these specific queries, improving SEO rankings and helping your products get picked up in GEO where AI search tools prioritize structured, descriptive data.

Hypotenuse AI automates this by filling missing attributes accurately, standardizing data, and generating optimized content in your brand voice.

Multi-source enrichment is the process of pulling product information from multiple places to complete and verify your data. For example, a product’s size might be taken from a supplier feed, its material from a PDF spec sheet, and its color confirmed from the product image. Combining these sources creates a more accurate and complete dataset than relying on one alone.

Hypotenuse AI uses multi-source enrichment by extracting and validating attributes from images, spec sheets, supplier sites, and other inputs, then standardizing everything into your taxonomy. This ensures your product data is both complete and consistent across channels.