Commerce Launches Advanced AI Catalog Enrichment Tools to Bridge the Gap Between Product Data and Agentic Commerce

Commerce, the parent organization behind BigCommerce, Feedonomics, and Makeswift, has officially unveiled two sophisticated artificial intelligence tools designed to optimize merchant product catalogs for the era of AI discovery. The new offerings, branded as Feedonomics Enrichment and BigCommerce Catalog Enrichment, represent a significant pivot in how B2B and B2C enterprises manage digital product data. By automating the transformation of raw product information into structured, AI-ready datasets, Commerce aims to enable seamless "agentic shopping" experiences, where autonomous AI agents can reliably interpret, compare, and recommend products to consumers and business buyers alike.

The Strategic Shift Toward Agentic Commerce

The rapid rise of generative AI has fundamentally altered the landscape of digital retail. Where traditional e-commerce relied on search engine optimization (SEO) to drive traffic to static product pages, the next generation of commerce is shifting toward "answer engines." Platforms such as ChatGPT, Claude, Perplexity, and Gemini are increasingly becoming the primary interface through which consumers and procurement officers conduct research and make purchase decisions.

However, the efficacy of these AI agents is entirely dependent on the quality of the data they ingest. If a product catalog is fragmented, poorly labeled, or lacks semantic context, AI agents may struggle to surface the item during a user’s query. Commerce’s new tools address this by generating standardized facts, descriptive snippets, and specific question-and-answer fields. This allows retailers to provide the "contextual fuel" necessary for AI models to accurately identify product relevance, target demographics, and technical specifications.

The Problem of Scale in Digital Retail

Sharon Gee, senior vice president of product for AI at Commerce, emphasizes that the primary obstacle for modern retailers is the "one-to-many" complexity of managing data across an expanding array of digital touchpoints. In previous years, a merchant might have needed to maintain data for their primary storefront and perhaps one or two major marketplaces. Today, that same merchant must provide consistent, accurate, and localized data to a sprawling network of search engines, social media platforms, and, most importantly, generative AI answer engines.

The challenge is amplified by the requirement for multilingual support and platform-specific formatting. For a global retailer, translating a catalog into 15 languages and distributing that content to dozens of distinct channels represents an immense operational burden. By automating the creation of consistent, high-fidelity data records, Commerce is effectively removing the reliance on manual CSV exports and fragmented third-party integration tools, centralizing the data pipeline within its own ecosystem.

Evolution of the E-commerce Technology Stack

The release of these tools follows a period of intense innovation within the Commerce ecosystem. Since the acquisition of Feedonomics—a leading product feed management provider—Commerce has moved to integrate data management more deeply with its core e-commerce engine.

The timeline of this transition reflects a broader industry trend toward "headless" and composable commerce architectures. Over the past two decades, the industry focused on solving the logistical challenges of digital storefronts: payment processing, inventory management, and basic shipping integrations. With those problems largely commoditized, the current focus has shifted toward the "intelligence layer" of the stack. Companies that once prioritized basic web presence are now re-engineering their entire data architecture to accommodate the needs of automated purchasing systems.

Impact on the B2B Sector and Operational Efficiency

While consumer-facing retail is the most visible beneficiary of these tools, the impact on the B2B sector may be even more transformative. B2B commerce has historically been hampered by manual, high-friction processes, particularly regarding procurement. The manual entry of purchase orders containing thousands of line items remains a significant drain on human resources, contributing to errors and operational delays.

Commerce’s recently introduced "Purchase Order Agent" is a prime example of how these enrichment capabilities facilitate deeper automation. By allowing a user to drag and drop a PDF purchase order into an AI agent, the system can automatically parse the document and populate a digital cart. This shift allows human employees to pivot away from low-value data entry tasks and toward strategic account management and relationship building.

According to data tracked by the Top 2000 Database, which monitors North America’s largest online retailers, companies using the Commerce platform generated over $538 billion in online sales in 2025. This massive volume of transactions highlights the critical need for scalable, automated solutions. As these enterprises grow, the ability to manage millions of product variations through AI-driven pipelines will likely become a primary competitive differentiator.

Analytical Implications for Retailers

The move by Commerce signals a permanent shift in how retailers must approach their digital catalogs. In the future, "Product Detail Pages" may become less important than "Product Knowledge Graphs"—structured, machine-readable representations of inventory that AI can traverse.

Industry analysts suggest that the retailers who win in the next five years will be those who treat their product data as a strategic asset rather than a back-end necessity. By providing both self-serve enhancements through BigCommerce and managed-service models through Feedonomics, Commerce is positioning itself to support both mid-market merchants and massive enterprise retailers who require high levels of customization and white-glove service.

Broader Industry Reactions and Future Outlook

While official responses from competitors remain cautious, the general market sentiment is that Commerce is addressing a latent, yet massive, demand for "AI readiness." The core assertion from the Commerce leadership team is that data is the "foundation for better agentic experiences." This sentiment is echoed across the broader tech industry, where the race is on to ensure that the massive language models (LLMs) driving modern AI do not suffer from "hallucinations" caused by poor-quality source data.

As more businesses move toward hybrid models—selling both B2B and B2C—the need for flexibility and composability will continue to grow. Retailers are increasingly wary of "vendor lock-in" but are simultaneously demanding tools that solve the complexity of omni-channel distribution. By focusing on a data-centric approach, Commerce is effectively providing a "connective tissue" that works regardless of the front-end platform, potentially insulating merchants from the volatility of changing AI search algorithms.

Conclusion: The Road Ahead

The integration of Feedonomics Enrichment and BigCommerce Catalog Enrichment marks a turning point in the maturation of AI-driven commerce. As AI agents move from merely answering questions to actively initiating and completing transactions, the quality of the data underlying these interactions will determine the success or failure of digital storefronts.

For Commerce, the strategy is clear: by building the infrastructure that makes product data understandable to the machines of tomorrow, they are securing their position as a vital utility for the future of global retail. As the industry watches, the successful implementation of these tools will likely provide a blueprint for how other platforms manage the delicate, complex intersection of human shopping intent and artificial intelligence execution.