In the rapidly evolving landscape of digital retail, the bridge between a consumer’s query and a completed purchase is no longer just a search engine—it is an intelligent, autonomous agent. Recognizing that the success of these AI agents hinges entirely on the quality of the underlying product data, Commerce—the parent company of BigCommerce, Feedonomics, and Makeswift—has launched two sophisticated artificial intelligence tools designed to prime merchant catalogs for the era of "agentic discovery."
The new offerings, Feedonomics Enrichment and BigCommerce Catalog Enrichment, represent a strategic shift in how retailers approach data management. By transforming standard product listings into AI-ready assets, these tools aim to ensure that brands remain visible and actionable as shoppers increasingly rely on platforms like ChatGPT, Gemini, Perplexity, and specialized shopping bots to make buying decisions.
Main Facts: The New Tools for AI Readiness
At its core, the initiative addresses a fundamental bottleneck in modern ecommerce: "garbage in, garbage out." As Sharon Gee, senior vice president of product for AI at Commerce, noted in the company’s announcement, "AI agents can only answer questions about your products as well as your data allows."
The enrichment tools serve as a data pipeline that automatically processes existing product information into robust, consistent, and context-rich records. This process is critical for both B2B and B2C merchants who are struggling to keep up with the demands of AI-driven search and shopping environments. Key functionalities include:
- Semantic Data Structuring: Generating facts, snippets, and comprehensive question-and-answer (Q&A) fields that allow large language models (LLMs) to interpret product features, intent, and target audience accurately.
- Omnichannel Synchronization: Ensuring that product descriptions, specifications, and availability remain consistent across traditional storefronts, global marketplaces, and the new wave of AI "answer engines."
- Operational Efficiency: Eliminating the manual overhead associated with CSV exports, third-party mapping tools, and repetitive data entry, effectively automating the "one-to-many" scaling problem that merchants face when trying to support multiple AI platforms simultaneously.
Chronology: The Evolution of Commerce’s AI Strategy
The release of these enrichment tools marks the latest milestone in a broader roadmap Commerce has been executing to position itself as a leader in the AI-native ecommerce stack.
- Phase 1: Foundational Infrastructure: Over the past decade, Commerce (and its primary entity, BigCommerce) solidified its position as a robust platform for enterprise-level retail. Its clients generated over $538 billion in online sales in 2025, according to the Top 2000 Database. This established the trust and data volume necessary to train advanced tools.
- Phase 2: The Integration of Makeswift and Feedonomics: By acquiring Feedonomics, Commerce gained a powerhouse in product feed management, allowing it to bridge the gap between back-end inventory and front-end marketplaces.
- Phase 3: The Pivot to "Agentic Commerce": Recognizing the rise of conversational commerce, Commerce began focusing on how to facilitate transactions via AI agents. This included the development of the "Purchase Order Agent," a B2B-focused solution that automates the extraction of data from PDFs to build shopping carts, moving away from manual entry.
- Phase 4: Launching Enrichment Tools (Current): The release of Feedonomics Enrichment and BigCommerce Catalog Enrichment serves as the "enabling layer," ensuring that the data processed by the company’s other AI agents is high-quality, structured, and ready for real-time inference.
Supporting Data: Why "Agentic Readiness" is Essential
The shift toward AI-mediated shopping is not merely a trend; it is a fundamental transformation of the consumer journey. For retailers, the complexity of this shift is compounded by the sheer number of channels they must manage.
According to Gee, a modern retailer might need to translate its catalog into 15 languages and distribute that data across traditional channels (Google, Amazon, Meta) and a growing list of AI answer engines (ChatGPT, Claude, CoPilot, Gemini, and Perplexity). For an enterprise retailer, the manual effort required to optimize for every single one of these platforms is unsustainable.
The economic implications are clear: Companies that fail to structure their data for AI will suffer from "invisibility." When a user asks an AI agent, "Which industrial adhesive is best for high-heat automotive applications?" the agent will only suggest products that have the specific data points to support that claim. If a retailer’s product description lacks the necessary technical metadata, they are effectively invisible to the AI, regardless of the quality of their product.
Official Responses and Strategic Vision
Sharon Gee, speaking on behalf of Commerce, emphasizes that the company is specifically targeting the "deep B2B and enterprise retail" sectors. These merchants often face the most complex data challenges, including managing thousands of SKUs, complex pricing tiers, and hybrid sales models (selling to both businesses and individual consumers).
"It’s fairly trivial to sell things online anymore," Gee told Digital Commerce 360. "That’s a well-solved problem over the past two decades. There are many use cases that have not been solved well yet."
For Gee, the "unsolved problem" is the high-friction, manual labor that still plagues B2B transactions. Her team is focused on using AI not just for "discovery," but for "operation." By automating the conversion of complex B2B documents—such as multi-page PDF purchase orders—into digital cart items, Commerce is freeing up human capital from rote, low-value tasks to focus on strategic growth.
"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 added.
Implications: The Future of Commerce
The introduction of these tools carries significant implications for the future of digital retail, particularly regarding the concept of "Composable Commerce."
1. The Death of the "Manual Pipeline"
Retailers have long relied on cumbersome CSV exports and brittle third-party integrations to manage their product data. Commerce’s new tools suggest that the future of ecommerce lies in "autonomous data pipelines." If the enrichment happens at the source—within the BigCommerce or Feedonomics ecosystem—the need for third-party middleware diminishes, reducing the risk of data degradation during synchronization.
2. The Rise of "Answer Engine Optimization" (AEO)
Search Engine Optimization (SEO) focused on ranking for keywords on Google. The new frontier, AEO, focuses on providing clear, factual, and structured answers to LLMs. Retailers who adopt these tools are essentially preparing their digital storefronts for a future where the "Search Engine Results Page" (SERP) is replaced by a conversational response.
3. B2B as the Primary Beneficiary
While B2C retailers will benefit from improved product discovery, the B2B sector stands to gain the most from "agentic commerce." B2B transactions involve longer, more complex cycles with specific contract pricing, high-volume order history, and recurring technical requirements. By applying AI agents to these processes, Commerce is helping enterprises remove the friction that has traditionally slowed down the digital transformation of wholesale and industrial commerce.
4. Competitive Moats
For Commerce, these tools function as a powerful retention mechanism. By making it easier for merchants to optimize their data for AI, Commerce creates a "stickier" platform. Merchants who build their entire AI strategy on top of Commerce’s enrichment tools will find it increasingly difficult to migrate to competitors, as their data architecture will become deeply intertwined with the platform’s proprietary enrichment logic.
Conclusion
The release of Feedonomics Enrichment and BigCommerce Catalog Enrichment is more than a simple product update; it is a tactical acknowledgment of where the retail industry is headed. As shopping habits shift from active searching to passive agentic discovery, the ability to "speak the language of AI" will become the primary competitive advantage for retailers.
By automating the enrichment process and providing a seamless pipeline for data distribution, Commerce is providing the infrastructure for a future where commerce is not just something a consumer does, but something that an agent handles on their behalf. For the enterprise retailer, the choice is becoming increasingly stark: embrace AI-ready data infrastructure, or risk being filtered out of the conversation entirely.
