Commerce launches new AI product catalog enrichment tools to revolutionize agentic commerce discovery
Commerce, the parent company encompassing BigCommerce, Feedonomics, and Makeswift, has officially launched two sophisticated artificial intelligence solutions designed to bridge the gap between traditional e-commerce data and the burgeoning era of agentic AI. The new tools, branded as Feedonomics Enrichment and BigCommerce Catalog Enrichment, are specifically engineered to prepare merchant product data for discovery by autonomous AI agents, marking a significant shift in how retailers must manage their digital presence to remain competitive in an increasingly automated marketplace.
The Rise of Agentic Commerce
The fundamental challenge facing modern retailers is that AI agents—ranging from LLM-powered shopping assistants to sophisticated procurement bots—are only as effective as the data they are fed. As consumers and B2B buyers increasingly turn to platforms like ChatGPT, Claude, Microsoft Copilot, Gemini, and Perplexity to research, compare, and eventually purchase goods, the reliance on structured, machine-readable product data has reached an inflection point.
Historically, e-commerce optimization focused on Search Engine Optimization (SEO), where keywords and meta-tags were designed to appease traditional search algorithms. However, the current landscape has shifted toward Answer Engine Optimization (AEO). In this new environment, an AI agent must be able to interpret a product’s utility, technical specifications, and suitability for a buyer’s unique needs in real-time. Without clean, consistent, and enriched data, a brand effectively becomes invisible to these intelligent systems.
Chronology and Strategic Development
The introduction of these tools follows a period of rapid consolidation and technical maturation within the Commerce ecosystem. Over the past two years, the organization has integrated the data-handling capabilities of Feedonomics with the robust infrastructure of BigCommerce to create a more unified data pipeline.
By late 2024 and early 2025, it became clear to industry observers that the "one-to-many" problem—where retailers must syndication product data to dozens of different platforms—was becoming unsustainable. Retailers were struggling to maintain consistent product records across traditional marketplaces, social media channels, and the newly emerging array of generative AI answer engines.
The launch of these enrichment tools serves as the culmination of this internal development strategy. By eliminating the need for manual CSV exports and fragmented third-party data management tools, Commerce is positioning its suite as the primary "source of truth" for enterprises managing complex catalogs.
Technical Capabilities and Data Enrichment
The core function of Feedonomics Enrichment and BigCommerce Catalog Enrichment is to transform raw product information into structured, machine-interpretable data. The tools automate the generation of:
- Semantic Metadata: Creating facts and snippets that provide context to AI models about what a product actually is, rather than just its name or SKU.
- Contextual Q&A Fields: Pre-emptively answering potential customer queries to ensure that AI models have the necessary context to recommend products accurately.
- Automated Localization: Managing catalog translations across multiple languages while ensuring technical specifications remain consistent, a critical requirement for global enterprise retailers.
By generating these rich data sets, the tools allow retailers to define their product identity for any platform that uses natural language processing to query the web. For instance, a retailer might have a footwear catalog requiring translation into 15 languages; the new tools manage the syndication of these assets to global marketplaces and AI answer engines simultaneously, ensuring that the brand’s voice and data integrity remain intact across all touchpoints.
Data-Driven Impact on the Retail Landscape
The urgency of this technological shift is underscored by the sheer volume of commerce flowing through these platforms. In 2025, retailers within the Top 2000 Database—a primary market research index tracking North America’s largest online retailers—that utilized Commerce technology generated over $538 billion in online sales. As these giants move to integrate agentic shopping, the ability to automate catalog maintenance is no longer a luxury but a fundamental operational requirement.
Industry analysts note that for a company with thousands of SKUs, the traditional manual approach to data entry is a significant drag on productivity. Sharon Gee, senior vice president of product for AI at Commerce, emphasized that "AI agents can only answer questions about your products as well as your data allows." For the enterprise segment, this means that every hour spent on manual data entry is an hour lost to higher-value strategic initiatives.
Transforming B2B Procurement
While the B2C sector focuses on shopping assistance, the B2B sector is witnessing a more radical transformation through the application of AI agents to procurement. One of the most significant pain points in B2B e-commerce has historically been the processing of manual purchase orders (POs). Large-scale B2B transactions often involve PDFs with thousands of line items that require manual entry into an ERP or e-commerce system.
Commerce has addressed this through its "Purchase Order Agent." This tool enables B2B buyers and sellers to bypass the legacy manual entry process entirely. By simply dragging and dropping a PDF purchase order into the system, the agent parses the data, verifies the inventory, and automatically builds a digital cart.
This automation has profound implications for labor allocation. By offloading rote, error-prone data entry to AI agents, businesses can reassign human talent to relationship management, complex negotiations, and strategic account planning. This shift supports the growing trend of "hybrid businesses" that require composable technology stacks to handle both the high-volume, automated nature of B2B and the high-touch, experience-driven nature of B2C.
Implications for the Future of E-commerce
The introduction of these enrichment tools signals a broader trend: the commoditization of the basic e-commerce transaction. As Gee noted, "It’s fairly trivial to sell things online anymore; that’s a well-solved problem over the past two decades." The next decade of competition will not be defined by who can build a functional storefront, but by who can ensure their data is the most discoverable, accurate, and useful to the AI agents that act as the modern-day gatekeepers of consumer intent.
For the retail industry, the implications are three-fold:
- Data as a Strategic Asset: Data quality is no longer just a backend administrative task; it is the primary driver of search visibility in the age of AI. Companies that neglect data hygiene will find themselves excluded from the recommendations generated by popular AI assistants.
- The End of Manual Syndication: The move toward automated, "one-to-many" data pipelines is accelerating. As the number of AI interfaces continues to grow, manual management will become impossible, forcing a reliance on integrated enrichment platforms.
- B2B Efficiency Gains: The application of agentic AI to administrative tasks like PO processing will likely become a standard benchmark for B2B digital maturity. Firms that adopt these tools early will achieve lower operational costs and faster order-to-fulfillment cycles.
Market Response and Outlook
While the long-term adoption rates for these specific tools remain to be seen, the market reception among enterprise retail stakeholders has been one of cautious optimism. The dual-model approach—offering self-serve enhancements for the agile BigCommerce user and managed-service options for complex, enterprise-level Feedonomics clients—suggests that Commerce is positioning itself to capture both the mid-market and the high-end enterprise space.
As the retail landscape moves away from static web pages and toward dynamic, AI-driven discovery, the infrastructure provided by Commerce represents a foundational shift. By standardizing the way product data is packaged for consumption by machines, the company is effectively writing the "instruction manual" for how brands will survive the next wave of the digital economy.
The transition to agentic commerce is expected to continue throughout the remainder of the decade. With the tools now available, the focus for Commerce and its competitors will likely shift toward increasing the sophistication of these agents, allowing them to handle not just simple product discovery and ordering, but also complex tasks like returns, warranty claims, and personalized subscription management. For now, however, the focus remains on the foundational step: ensuring that the world’s products are ready for the AI-first future.
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