Product data didn’t matter as much in the old SEO era, when search engines focused on full pages instead of individual SKUs. That’s changed. Good product data now plays a big role in whether your items show up across AI search and recommendations.
LLMs depend on the information they can read and understand. The more complete and accurate your product data is, the higher the chance your products get mentioned or suggested for the right queries.
For brands with missing or inconsistent attributes, enrichment helps fill the gaps so AI systems can confidently recognize what each product is, what it’s for, and when it should appear.
The problem is that enrichment is slow, repetitive, and easy to get wrong when you’re dealing with thousands of SKUs. This is why it often gets pushed aside or done in a rushed way. AI-driven enrichment automates the hard parts, fills missing details, standardizes attributes, and keeps everything clean across the catalog.
And as more brands start enriching their data with AI, the ones who don’t will start losing visibility.
Why AI-driven product data enrichment matters more than ever
AI discovery has changed how products get surfaced. Search engines and recommendation models no longer rely only on keywords or page-level signals. They look at the actual attributes behind each SKU to understand what the product is, how it compares to others, and when it should appear.
For ecommerce teams, this means product data isn’t just an internal housekeeping task anymore. It affects:
- Search visibility on AI-powered platforms
- Filtering and category accuracy on your own site
- How well AI systems understand variants
- Whether your products get recommended over similar items
If your data is sparse or inconsistent, AI systems simply don’t have enough to work with, which leads to fewer impressions, mismatched queries, or incorrect categorization.
Good enrichment also impacts customer experience. Clean, complete attributes help shoppers compare items, understand key details, and find what they need faster. This reduces bounce rates, improves search performance, and lowers returns.
Best AI tools for ecommerce product data enrichment
Product data enrichment has traditionally been done manually or outsourced to content teams. These approaches can work, but they’re slow, inconsistent, and hard to scale.
Today, most enterprise ecommerce brands are moving toward AI-powered platforms that automate enrichment and keep data clean across large catalogs. Some of the leading options include:
Hypotenuse AI
Hypotenuse AI is an AI-native platform built for large ecommerce catalogs. It enriches product attributes, fills missing details, extracts data from images and PDFs, and generates publish-ready product content for every channel.
It’s built for teams that want enrichment and content creation working together in one place, rather than across disconnected tools. This shortens the path from raw data to channel-ready products that are optimized for SEO and GEO.
For many brands, Hypotenuse AI also functions as their AI PIM software. It centralizes attributes, enrichment, content, and images so teams can work from one unified product workspace.
Key features:
- Filling missing attributes from web sources, PDFs, supplier portals, or by extracting details from images
- Turning raw data from different sources into structured, consistent, and shopper-friendly attributes
- Product tagging and categorization for brands with complex hierarchies, variants, and multi-region catalogs
- AI product photography and batch image editing, including lifestyle scene generation, background removal, and upscaling
- Generating product copy such as descriptions, marketplace bullets, and metadata in over 40 languages
Zoovu
Zoovu helps retailers and manufacturers structure product information so customers can easily compare items and find the right match. It’s best known for powering guided discovery tools, product finders, and conversational shopping flows.
By standardizing attributes and mapping product relationships, Zoovu helps customers reach the right products faster and improves overall product discovery.
Key features:
- Automated product data cleansing, standardization, and enrichment
- Ability to ingest data from PIMs, ERPs, CRMs and transform it into structured attributes
- Linking and organizing product relationships, compatibility, and related use cases
- Multi-language and global support for enterprise teams
Constructor
Constructor helps retailers improve on-site search, browsing, and product discovery by enriching product metadata and inferring missing attributes. It uses AI to understand products more accurately, strengthening filters, facets, and category relevance.
It’s best known for its search and discovery engine, which combines product data, behavioral signals, and enrichment models to surface more relevant results and reduce the impact of messy or incomplete catalog data.
Key features:
- Automates enrichment of product attributes and categories when data is incomplete, inaccurate, or missing.
- Combines generative AI, machine vision, and behavioral-data to infer attributes and strengthen metadata.
- Enables review and management of enriched attributes via a merchant dashboard
- Applies high-scale attribute prediction and tagging across large catalogs
Feedonomics
Feedonomics helps brands prepare clean, optimized product data for marketplaces, retail media networks, and advertising channels. It standardizes product information, applies channel-specific rules, and makes sure every SKU meets each destination’s requirements.
It’s best known for powering multi-channel commerce at scale, with automation that keeps feeds consistent and compliant across Google Shopping, Amazon, Walmart, TikTok Shop, and hundreds of other destinations.
Key features:
- Automated data transformation and enrichment for marketplace and advertising feeds
- Channel-specific mapping and rules to meet each destination’s unique attribute requirements
- Preview and validation tools to check feed outputs before publishing
- Managed services for feed setup, troubleshooting, and ongoing optimization
Salsify
Salsify helps brands centralize product data, digital assets, and channel-ready content in one platform. It brings together PIM, DAM, workflows, and syndication so teams can manage product information end to end and keep it consistent across retailers and marketplaces.
It’s best known for helping global brands meet the different content and attribute requirements of each retail partner. Salsify streamlines collaboration between teams and ensures every SKU is complete, compliant, and ready to publish across the digital shelf.
Key features:
- Centralized management of product data, content, and digital assets
- Channel-specific templates and mappings for retailer and marketplace requirements
- Workflow and collaboration tools for approvals and content readiness
- Digital shelf analytics to monitor completeness and performance across channels
Akeneo
Akeneo helps brands structure and manage product data across complex catalogs. It provides a flexible PIM framework for organizing attributes, families, and taxonomies, giving teams more control over how product information is modeled and maintained across regions and sales channels.
It’s best known for its strong governance and workflow capabilities. Akeneo helps teams maintain data quality through validation rules, approvals, and standardized processes, making it easier to keep large catalogs consistent as they scale.
Key features:
- Flexible product data modeling with customizable attributes, families, and taxonomies
- Validation and governance rules to maintain data accuracy
- Workflow tools for multi-team collaboration and approvals
- Localization support for multi-language and multi-market catalogs
How These Tools Compare (and Where Each One Fits)
All of these platforms work with product data, but each solves a different part of the ecommerce workflow.
Hypotenuse AI: AI-native enrichment and all-in-one workflow
AI-native platform that enriches product data, extracts attributes from images, and generates SEO/GEO-optimized content. Also serves as a scalable and governed PIM for many brands that want data, images, and content workflows in one place.
Zoovu: Guided discovery
Helps brands structure product information for guided selling, product finders, and comparison tools. Strong for simplifying technical catalogs and helping shoppers reach the right product.
Constructor: On-site search and discovery
Focuses on improving search, filters, and browse by enriching metadata and inferring missing attributes. Strong for retailers looking to boost relevance and personalization.
Feedonomics: Channel and feed optimization
Transforms and prepares product data for marketplaces, retail media, and advertising channels. Best for keeping large multi-channel feeds accurate and compliant.
Salsify: PXM and syndication
Combines PIM, asset management, and syndication workflows. Used by brands that need to coordinate product data across many retailers and marketplaces.
Akeneo: PXM and data governance
A structured PIM for building taxonomies, maintaining data quality, and managing multi-team workflows. Strong for brands with complex catalogs and multi-region governance needs.
What enterprises should look for in product data enrichment software
Enterprise teams deal with large catalogs, multiple regions, and many upstream and downstream systems. Good enrichment software needs to handle this complexity while reducing manual work, improving accuracy, and supporting faster launches. Here are the areas that matter most.
1. Ability to handle unstructured and inconsistent data
Supplier feeds, PDFs, spreadsheets, and legacy systems rarely follow the same format. The software should be able to ingest messy data, extract information, standardize formats, and fill gaps without requiring manual cleanup first.
2. Strong attribute enrichment and standardization
Look for tools that can fill missing attributes, normalize terminology, and keep variant and family-level data consistent. Consistency across the entire catalog is essential for clean filters, accurate search, and smooth publishing.
3. Enrichment coverage and completeness
Look out for the enrichment coverage percentage, which reflects how thoroughly the software can populate your required attributes. Strong tools should be able to match or exceed what a manual team would complete, while maintaining high data quality.
4. Transparency into enrichment sources
There should be clear visibility into how attributes were enriched and which sources or signals were used. This helps with trust, validation, and compliance, especially when teams need to trace where a piece of information originated.
5. Support for complex taxonomies and product hierarchies
The software should handle custom taxonomies, variant structures, and parent–child relationships so attributes stay consistent across families. Good logic at this level prevents conflicting values and keeps related SKUs aligned.
6. Image understanding or image-linked enrichment
Often, data isn’t available in supplier files or anywhere on the web. Image-based enrichment helps fill these gaps by identifying colors, materials, textures, patterns, or product features directly from photos. Look for software that can accurately extract reliable product attributes from images when other sources fall short.
7. Flexibility to integrate with existing systems
The software must be able to integrate with current systems, whether it’s a PIM, ERP, DAM, or internal tools. This lets teams keep their existing workflows without having to change how they operate.
8. Multi-market and multi-language support
Every region has its own language, terminology, and nuances. The software should support localized attributes and content while still keeping core product information consistent across markets.
9. Impact on SEO and GEO
The software should enrich data in a way that aligns with how shoppers search. This includes clearer attributes, standardized terminology, and the ability to pull in relevant or trending search terms as they emerge.
10. Scalability and long-term catalog health
The software should be able to handle large SKU counts, frequent updates, and back-propagate changes across the taxonomy. This helps maintain data quality over time, not just during initial setup.
Conclusion
Complete and consistent product data is no longer just an operational need. It is now a core driver of visibility across search, marketplaces, and emerging AI-driven surfaces.
As enrichment becomes more complex and more channels depend on structured data, brands that invest in the right tools will move faster, publish with confidence, and show up more reliably where customers are searching.
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