Commerce has released two artificial intelligence tools designed to empower merchants by preparing their product catalogs for the burgeoning era of AI discovery. These offerings, Feedonomics Enrichment and BigCommerce Catalog Enrichment, are positioned as key enablers for both Business-to-Business (B2B) and Business-to-Consumer (B2C) enterprises, aiming to bridge the gap between existing product data and the sophisticated demands of generative AI and intelligent agents.
At its core, Commerce describes these solutions as "agentic commerce" capabilities, specifically engineered to refine product data and make it readily digestible for AI-driven discovery. This proactive approach is intended to allow merchants to seamlessly integrate AI tools into their operations, thereby facilitating the creation of highly personalized and efficient "agentic shopping experiences." The underlying principle is that the efficacy of AI agents in understanding and responding to customer queries about products is directly proportional to the quality and comprehensiveness of the underlying product data.
Sharon Gee, Senior Vice President of Product for AI at Commerce, emphasized this foundational aspect in an official announcement. "AI agents can only answer questions about your products as well as your data allows," Gee stated. "Whether you’re a marketer driving AEO [which can be interpreted as AI-Enhanced Optimization or a similar concept] or a product leader building shopping agents, better data is the foundation for better agentic experiences. Feedonomics Enrichment and BigCommerce Catalog Enrichment give brands on any platform a data pipeline that lifts performance everywhere agents meet shoppers from Google to OpenAI and on their own sites and agents."
The strategic importance of these tools is underscored by the nature of Commerce’s existing client base. Gee noted in a separate discussion with Digital Commerce 360 that many companies engaging with Commerce are deeply entrenched in B2B and enterprise retail sectors. These segments are witnessing a significant uptick in consumer and B2B buyer demand for AI tools that can not only compare products and answer complex questions but also, increasingly, facilitate direct purchases. This evolving landscape necessitates a robust data infrastructure that can support these advanced AI interactions.
Commerce, as the parent entity, oversees a portfolio of prominent e-commerce technology providers, including BigCommerce, Feedonomics, and Makeswift. This integrated ecosystem suggests a cohesive strategy for addressing the end-to-end needs of online retailers, from platform infrastructure to data management and content creation. The company’s influence is substantial; retailers leveraging Commerce as their e-commerce platform within the Top 2000 Database in 2025 collectively generated over $538 billion in online sales that year. The Top 2000 Database itself is a critical market research tool, meticulously tracking North America’s largest online retailers by annual web sales and identifying the key technology vendors that power their e-commerce operations. This demonstrates Commerce’s significant footprint in the digital commerce landscape.
Transforming Product Information into AI-Ready Assets
The core function of Commerce’s new AI product catalog enrichment tools is to transform existing product information into a more robust, consistent, and actionable data format. Both Feedonomics Enrichment and BigCommerce Catalog Enrichment are designed to generate a standardized set of enriched product attributes. While the specific list of generated attributes was not detailed in the provided text, typical outputs in such enrichment processes often include:
- Enhanced Product Titles and Descriptions: Optimized for clarity, keyword relevance, and AI comprehension.
- Detailed Product Specifications: Expanding on technical details, dimensions, materials, and performance metrics.
- Categorization and Tagging: More granular and AI-friendly categorization for improved search and filtering.
- Attribute Standardization: Ensuring consistent naming and value formats across all product listings.
- Image and Media Optimization: Preparing visual assets for AI analysis and rendering.
Beyond these standard enrichments, the tools are engineered to produce critical data elements such as facts, concise snippets, and structured question-and-answer (Q&A) fields. These components are vital for generative AI platforms, enabling them to accurately interpret product identities, pinpoint target audiences, and ascertain the relevance of a product to a specific user’s needs. This deep level of data preparation is what allows AI agents to move beyond basic keyword matching to true semantic understanding.
The enrichment process, as described by Commerce, aims to create complete and authoritative product records. This ensures that both human shoppers and AI agents receive consistent and reliable product details across a multitude of touchpoints, including brand storefronts, third-party marketplaces, search engines, and other emerging channels. A significant benefit highlighted is the elimination of cumbersome manual processes like CSV exports and the reliance on disparate third-party tools for data manipulation. This streamlining of the data pipeline is crucial for agility in the fast-paced e-commerce environment.
Sharon Gee provided a compelling real-world example to illustrate the scale of the challenge and the solution offered by these tools. She described a scenario where a retailer needs to translate its footwear catalog into 15 different languages. Historically, each of these translations would need to be meticulously prepared and disseminated to a variety of platforms and channels, including:
- E-commerce Storefronts: The retailer’s own website.
- Marketplaces: Platforms like Amazon, eBay, or specialized fashion marketplaces.
- Search Engines: For organic search visibility (SEO).
- Advertising Platforms: For paid search campaigns.
- Social Media Channels: For marketing and engagement.
- Comparison Shopping Engines: To facilitate price and feature comparisons.
However, the advent of agentic AI introduces a new layer of complexity. Retailers now must also ensure their translated product data is accessible and understandable to advanced AI answer engines such as ChatGPT, Claude, CoPilot, Gemini, and Perplexity. Gee emphasized the need for "Universal Content Platform (UCP) support for each of them," framing this as a significant "one-to-many problem – scale." The enrichment tools directly address this by creating a unified, high-quality data foundation that can be leveraged across all these diverse AI and human-facing channels.
Tailored Solutions for Diverse Merchant Needs
The BigCommerce Catalog Enrichment tool offers merchants a self-serve model, empowering them to directly manage and enhance their product content within the BigCommerce ecosystem. This provides a high degree of control and flexibility for businesses that have the in-house expertise to manage their data enrichment processes.
In contrast, the Feedonomics Enrichment tool provides a more adaptable approach, offering both a self-managed option for greater autonomy and a fully managed service. This dual-model strategy caters to a wider range of merchant needs and capabilities, acknowledging that some businesses may prefer to delegate the intricacies of data enrichment to specialists. Feedonomics’ established expertise in product feed management for marketplaces and advertising platforms makes its enrichment solution a natural extension for existing users.
Empowering B2B and Hybrid Commerce Models
Beyond consumer-facing applications, Commerce’s strategic focus on B2B and hybrid businesses is a notable aspect of its platform development. Gee pointed out that many merchants approach Commerce not only for their B2C capabilities but specifically for the advanced technology and tooling required to serve B2B clients effectively. This includes managing complex pricing structures, customer-specific catalogs, and the intricate ordering processes common in B2B transactions.
Hybrid businesses, which operate in both B2B and B2C spheres, often face unique challenges. They require a high degree of flexibility and composability within their technology stacks to cater to the distinct needs of each customer segment. Commerce’s integrated approach aims to provide a unified platform that can accommodate these diverse requirements without necessitating separate, siloed systems.
Gee articulated a vision where AI addresses long-standing inefficiencies in commerce. "It’s fairly trivial to sell things online anymore," she remarked, referring to the maturity of basic e-commerce functionality. "That’s a well-solved problem over the past two decades. There are many use cases that have not been solved well yet." She identified the manual input of purchase orders, particularly those with thousands of line items, as a prime example of an inefficient, time-consuming process that is ripe for AI intervention.
Commerce’s development of an AI-powered Purchase Order Agent exemplifies this focus on solving complex B2B challenges. This agent is designed to automate the process of creating orders from unstructured documents like PDFs. By allowing users to simply drag and drop a purchase order PDF, the agent can intelligently extract the necessary information, identify products, quantities, and pricing, and automatically build a shopping cart within the e-commerce platform.
The implication of such automation is significant for operational efficiency and workforce productivity. "Now those humans could get back to doing human work instead of rote, nasty data entry that is super manual that doesn’t actually add more value," Gee stated. This redeployment of human capital towards strategic, value-added tasks is a key benefit of advanced AI integration in the enterprise.
The Evolving Landscape of AI in E-commerce
The introduction of Feedonomics Enrichment and BigCommerce Catalog Enrichment by Commerce marks a significant step in the ongoing evolution of e-commerce technology. As AI agents become more sophisticated and integrated into consumer and business workflows, the quality and structure of product data will become a critical differentiator for merchants. The ability to provide AI with precise, comprehensive, and contextually rich product information is no longer a competitive advantage but a necessity for survival and growth.
This development aligns with broader industry trends, where AI is moving from a novel feature to a foundational element of digital commerce strategy. The demand for personalized shopping experiences, efficient B2B procurement, and AI-driven product recommendations all hinge on the underlying data infrastructure. Commerce’s investments in catalog enrichment are therefore strategically positioned to capitalize on these accelerating trends.
The implications extend to how businesses will interact with their technology vendors. As AI becomes more embedded, the emphasis will shift from simply providing a platform to providing intelligent systems that can actively enhance business operations. The "agentic commerce" paradigm suggests a future where software acts more autonomously, driven by sophisticated data analysis and AI-powered decision-making.
For retailers, particularly those in the B2B and enterprise space, the adoption of such tools represents an opportunity to unlock new levels of efficiency, improve customer satisfaction, and gain a competitive edge. The ability to present products accurately and persuasively to both human and AI audiences will be paramount. By focusing on data enrichment as the bedrock of AI-driven commerce, Commerce is positioning itself as a key enabler for businesses navigating this transformative period.
The company’s integrated approach, leveraging its stable of brands like BigCommerce and Feedonomics, offers a comprehensive solution that addresses the complex data challenges inherent in modern e-commerce. As AI continues to permeate every aspect of the digital marketplace, the demand for intelligent data preparation will only intensify, making tools like Feedonomics Enrichment and BigCommerce Catalog Enrichment indispensable for merchants looking to thrive in the AI-powered future of commerce.
Merchants interested in staying abreast of the latest trends in B2B e-commerce technology and business strategies can sign up for a complimentary subscription to Digital Commerce 360 B2B News. This publication offers in-depth coverage of the rapidly growing B2B e-commerce sector, providing valuable insights and analysis for industry professionals. Following Digital Commerce 360 on platforms such as LinkedIn, X (formerly Twitter), Facebook, and YouTube can also provide real-time updates and expert perspectives on the evolving e-commerce landscape.
