Commerce Unveils AI Catalog Enrichment Tools to Prepare B2B and B2C Merchants for Agentic Discovery
As artificial intelligence rapidly transforms how consumers and business buyers search for goods, parent company Commerce has launched two groundbreaking tools designed to optimize product catalogs for the era of AI-driven commerce. The newly introduced solutions, Feedonomics Enrichment and BigCommerce Catalog Enrichment, are engineered to help both Business-to-Business (B2B) and Business-to-Consumer (B2C) merchants format, structure, and refine their product data so it can be seamlessly discovered, interpreted, and utilized by advanced AI agents.
The launch comes at a critical juncture for the global retail and wholesale sectors. Traditional search engine optimization (SEO) is increasingly sharing the spotlight with Answer Engine Optimization (AEO), as users lean on conversational AI platforms to research, compare, and eventually purchase products. By providing a streamlined data pipeline, Commerce aims to solve a fundamental friction point in modern digital retail: the reality that generative AI models are only as accurate as the underlying data they process.
The Foundation of Agentic Commerce: Better Data for AI Discovery
The rollout of Feedonomics Enrichment and BigCommerce Catalog Enrichment reflects a broader industry shift toward "agentic commerce," a paradigm where autonomous software agents act on behalf of human users to execute complex tasks. Whether a shopper asks ChatGPT to recommend the best running shoes for a marathon or a procurement manager instructs an AI assistant to source industrial-grade fasteners, these systems rely heavily on structured, high-fidelity product feeds.
Sharon Gee, Senior Vice President of Product for AI at Commerce, emphasized the critical link between data quality and AI performance during the product 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 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."
According to the company, the tools are designed to take existing product information and automatically transform it into robust, consistent data sets. This enrichment process generates specialized facts, descriptive snippets, and dynamic question-and-answer (Q&A) fields. These elements allow generative AI platforms to easily comprehend what a product is, who its target audience is, and how relevant it is to a specific user query. Furthermore, the technology eliminates the cumbersome reliance on manual CSV exports and fragmented third-party software plugins.
Addressing the One-to-Many Distribution Challenge
Historically, managing product catalogs across multiple channels required a linear, often tedious workflow. Retailers frequently had to translate their inventory data into numerous languages and format it to meet the stringent requirements of various regional marketplaces, social media platforms, and comparison shopping engines.
Today, that operational burden has multiplied exponentially. Merchants must now ensure their catalogs are properly optimized for an expanding roster of answer engines and conversational AI interfaces, including OpenAI’s ChatGPT, Anthropic’s Claude, Microsoft Copilot, Google Gemini, and Perplexity.
"You need to be able to have unified catalog protocol support for each of them," Gee explained in a separate interview with Digital Commerce 360. "It’s a one-to-many problem—it’s scale."
To address this structural challenge, Commerce has structured its offerings to provide flexible deployment models. The BigCommerce enrichment tool empowers merchants to execute self-serve enhancements directly within their existing workflows. Meanwhile, the Feedonomics enrichment tool provides versatile options, supporting both self-managed configurations and fully managed-service models to accommodate enterprise-level retail demands.
Scaling Up: The Economic Footprint of Commerce
The release of these AI enablement tools is backed by substantial market reach. Commerce operates as the parent organization for a suite of prominent ecommerce technology brands, including BigCommerce, Feedonomics, and Makeswift.
Market data underscores the immense scale of the company’s ecosystem. Retailers listed in the prestigious Top 2000 Database that utilized Commerce as their underlying ecommerce platform generated more than $538 billion in online sales. The Top 2000 Database serves as an authoritative market research tool tracking North America’s largest online retailers by annual web sales, while also cataloging the preferred vendors powering their technical infrastructure.
Because Commerce serves a client base deeply embedded in enterprise retail and complex B2B markets, the introduction of AI-driven catalog optimization directly addresses the high-stakes operational needs of high-volume sellers.
Beyond Retail: Transforming B2B Workflows with AI Agents
While consumer-facing retail has dominated headlines regarding AI adoption, B2B commerce presents its own unique set of operational bottlenecks. Gee noted that modern digital commerce platforms must cater to hybrid businesses—enterprises that manage both wholesale B2B relationships and direct-to-consumer storefronts simultaneously. While basic online transaction processing has matured into a well-solved commodity over the past two decades, many complex B2B workflows remain stubbornly manual.
A prime example is the processing of traditional purchase orders. Wholesale buyers frequently submit extensive purchase orders via unstructured PDF documents containing thousands of individual line items. Historically, processing these documents required human administrative teams to spend hours manually keying data into enterprise resource planning (ERP) systems.
To alleviate this administrative burden, Commerce has integrated complementary AI capabilities, such as its Purchase Order Agent. By allowing users to simply drag and drop a PDF purchase order into the system, the AI agent can automatically parse the document and build a comprehensive shopping cart in a fraction of the time.
"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 noted, emphasizing the productivity gains unlocked by agentic automation.
Industry Implications and Future Outlook
The launch of Feedonomics Enrichment and BigCommerce Catalog Enrichment highlights a broader transformation across the digital economy. As search behavior migrates from traditional keyword-based queries on web browsers to intent-driven dialogs with AI assistants, merchants face a stark reality: invisibility to AI equals invisibility to the modern buyer.
Industry analysts suggest that tools bridging the gap between raw merchant data and AI readiness will become indispensable components of the modern enterprise technology stack. By automating the generation of rich semantic data, structured Q&A fields, and multi-channel translations, solutions like those offered by Commerce help level the playing field, allowing mid-market and enterprise merchants alike to compete effectively in an automated marketplace.
As artificial intelligence continues to mature from a novelty feature into the primary interface for online discovery and procurement, the success of digital storefronts will increasingly depend on the quality of their data infrastructure. With its latest product offerings, Commerce has positioned itself at the forefront of this transition, equipping businesses with the technical foundation required to thrive in the age of agentic commerce.
