Feedonomics Launches Agentic Catalog Exports: How Products Enter the AI Shopping Pipeline

13 min read · April 28, 2026
Feedonomics Launches Agentic Catalog Exports: How Products Enter the AI Shopping Pipeline

Feedonomics, a Commerce company (Nasdaq: CMRC), launched Agentic Catalog Exports (ACE) on April 27, 2026, enabling merchants including Dell to syndicate agent-ready product data to AI shopping surfaces including OpenAI/ChatGPT, Google Gemini, Microsoft Copilot, and PayPal. ACE represents a new infrastructure layer in agentic commerce: the product catalog feed optimized not for human shoppers browsing a storefront, but for AI agents querying APIs to make purchase recommendations.

This is not just another channel integration. It is the supply-side infrastructure that makes agentic commerce work at scale. Merchants who prepare agent-ready product data now will have first-mover advantage as AI shopping surfaces scale from experimental features to primary discovery channels.

What Is Agentic Catalog Exports (ACE)?

ACE is a product data syndication platform that transforms traditional e-commerce catalog feeds into structured, enriched data formats optimized for AI agent consumption. Unlike standard product feeds designed for human-readable shopping comparison sites or marketplace listings, ACE feeds are designed for machine-readable product discovery and evaluation.

The distinction matters. Traditional product feeds prioritize human-facing attributes like product names, descriptions, and marketing copy. ACE feeds prioritize structured attributes that AI agents need to make informed recommendations: technical specifications, compatibility information, use case patterns, comparative features, and performance data. The platform enriches raw catalog data with these attributes, making it actionable for AI systems.

Feedonomics is not a new player in product feed management. The company has been helping merchants syndicate data to shopping channels for years. ACE is a strategic pivot that anticipates the shift from human-initiated shopping to AI-agent-initiated shopping. The company is positioning its infrastructure at the intersection of e-commerce and agentic AI.

The AI Shopping Surfaces ACE Targets

ACE currently syndicates to four major AI shopping surfaces:

OpenAI/ChatGPT: The conversational AI with 900 million weekly active users, increasingly used for product research and shopping recommendations.

Google Gemini: Google's AI assistant integrated across Search, Android, and other Google services, with growing shopping capabilities.

Microsoft Copilot: Microsoft's AI assistant integrated into Windows, Edge, and Bing, with shopping features tied to Microsoft's retail partnerships.

PayPal: The payments giant that is building AI-driven shopping experiences and recommendation engines.

These four surfaces represent the vanguard of AI shopping. They are not full-fledged marketplaces yet, but they are rapidly becoming discovery channels where consumers ask AI agents for product recommendations. ACE ensures that merchant products are available for discovery when these AI agents query their databases.

The strategic implication is clear: if your product catalog is not formatted for AI agent consumption, you are invisible in these emerging shopping surfaces. ACE provides the bridge between traditional e-commerce catalogs and AI-native shopping infrastructure.

Agent-Ready Product Data: What Makes It Different

The concept of "agent-ready" product data is the core innovation of ACE. Traditional product feeds are designed for human shoppers to browse, compare, and select products. Agent-ready feeds are designed for AI systems to retrieve, analyze, and recommend products.

The key differences:

Structured attributes over marketing copy: ACE emphasizes structured data like technical specs, dimensions, compatibility matrices, and performance metrics. These are the attributes AI agents need to match products to user requirements.

Use case mapping: ACE maps products to specific use cases and scenarios. Instead of just listing a laptop's RAM and processor, the feed indicates that the product is suitable for video editing, software development, or casual gaming. This helps AI agents match products to user intent.

Comparative data: ACE includes comparative attributes that allow AI agents to evaluate products against each other. This includes pricing tiers, feature comparisons, and positioning relative to other products in the category.

Enrichment and normalization: ACE takes raw catalog data from merchants and enriches it with missing attributes, normalizes inconsistent data formats, and applies taxonomies that AI agents can parse reliably.

The goal is not just to make product data available to AI agents, but to make it actionable. AI agents can't just "read" product descriptions like humans do. They need structured, queryable data that they can use to match products to user requirements, compare options, and justify recommendations.

The Commercial Opportunity: $1 Trillion at Stake

The launch of ACE comes against a backdrop of massive growth projections for agentic commerce. Capgemini research shows that 38% of consumers already trust AI agents for routine purchases, and 55% are willing to let agents handle reorders within three years. McKinsey estimates that up to $1 trillion in US B2C retail revenue will flow through agentic commerce by 2030.

These are not speculative projections. They are based on current adoption patterns and the trajectory of AI capability development. Consumers are already using AI assistants for product research. The next step is AI agents handling the entire purchase flow from discovery to transaction.

For merchants, the opportunity is two-fold. First, early adopters of agent-ready product data can capture the first wave of AI-driven shopping volume before competitors catch up. Second, merchants with superior agent-ready data—more comprehensive attributes, better use case mapping, richer comparative data—will be favored by AI agents making recommendations.

The risk is equally significant. Merchants who ignore agent-ready product data risk becoming invisible in AI shopping surfaces. As more consumers rely on AI agents for shopping recommendations, traditional SEO and paid acquisition channels may become less effective relative to AI discovery channels.

Dell as Early Adopter: The Enterprise Signal

Dell is one of the first merchants to adopt ACE and syndicate its product catalog to AI shopping surfaces. This is a significant signal for several reasons.

First, Dell is a large enterprise with a complex product portfolio spanning consumer laptops, enterprise servers, workstations, and peripherals. If Dell can successfully format its entire catalog for AI agent consumption, it demonstrates that ACE can handle enterprise-scale product data complexity.

Second, Dell's product categories—especially technical hardware like servers and workstations—require rich structured data for accurate AI recommendations. A laptop recommendation depends on multiple technical specifications, use cases, and compatibility requirements. Dell's adoption suggests ACE's attribute mapping and enrichment capabilities are sufficiently sophisticated for complex product categories.

Third, Dell as a brand is highly visible in AI conversations, particularly around AI infrastructure and hardware. Its adoption of ACE reinforces the narrative that agentic commerce is moving from experimental to operational for major brands.

For smaller merchants, Dell's adoption is both validation and a competitive threat. Validation that the technology works and that major brands are investing in it. A competitive threat because early adopters will capture the initial AI shopping volume and establish patterns that AI agents may learn to favor.

The "Invisible Shelf": How AI Agents Discover Products

SiliconANGLE's coverage of Google Cloud Next introduced the concept of the "invisible shelf"—the digital inventory that AI agents can discover, evaluate, and recommend to consumers, even if humans never see it in traditional browsing interfaces. ACE is infrastructure for the invisible shelf.

In traditional e-commerce, products sit on visible shelves: product pages, category listings, search results, marketplace grids. Humans browse these shelves, click through, compare options, and make purchase decisions. The shelf metaphor works because humans can see and navigate the interface.

In agentic commerce, products sit on invisible shelves: structured databases that AI agents query, filters that agents apply, and recommendation algorithms that agents use to match products to users. Humans never see the shelf. They only see the final recommendation that the AI agent presents.

ACE builds the invisible shelf. It takes products from merchant catalogs and places them on shelves that AI agents can access. The quality of the shelf—the completeness of product data, the accuracy of attribute mapping, the richness of use case information—determines how often and how favorably AI agents will recommend those products.

The Supply-Side Complement to UCP

The Universal Commerce Protocol (UCP), covered previously in Searchless, is the demand-side standard for how AI agents initiate and execute purchases. ACE is the supply-side complement: it ensures that product data is available in the format that UCP-enabled agents need to make recommendations.

Together, UCP and ACE form the infrastructure stack for agentic commerce:

Merchants need both. UCP without ACE means agents can initiate purchases but may have incomplete or poorly-structured product data to work with. ACE without UCP means products are agent-ready but lack a standardized protocol for agents to execute purchases.

The synergy between UCP and ACE suggests that agentic commerce infrastructure is converging around a small number of standards and platforms. Merchants who invest in both will be well-positioned as AI shopping surfaces scale.

How Merchants Can Prepare for Agent-Ready Product Data

Even for merchants not ready to adopt ACE immediately, there are steps to prepare product catalogs for the agentic commerce era:

Audit product data completeness: Review your catalog for missing attributes, inconsistent formats, and gaps in technical specifications. These are the gaps that ACE would fill, but identifying them now lets you prioritize internal data improvement.

Map products to use cases: For each product category, document the primary use cases and scenarios. A laptop is not just "a laptop"—it's a device for video editing, software development, casual gaming, or business productivity. Mapping products to use cases makes them more discoverable by AI agents.

Develop comparative frameworks: Create structured comparisons between your products and competitors. Highlight differentiating features, positioning, and value propositions. Comparative data helps AI agents explain recommendations to users.

Invest in structured data over marketing copy: Shift some content investment from persuasive marketing copy to structured product information. Marketing copy influences human buyers; structured data influences AI agents.

Monitor AI shopping surface developments: Track how ChatGPT, Gemini, Copilot, and other AI surfaces are evolving their shopping capabilities. Understanding where these surfaces are heading helps you prioritize which product categories and attributes to prepare first.

The merchants who win in agentic commerce will not necessarily be those with the best marketing or the lowest prices. They will be those with the best product data—the most complete, structured, and actionable information that AI agents can use to make confident recommendations.

The Competitive Landscape: Who Else Is Playing?

Feedonomics is not the only player recognizing the importance of agent-ready product data, but ACE is one of the most comprehensive offerings to date. Other players in the space include:

Feedonomics' differentiation is its explicit focus on "agent-ready" data as a distinct category, rather than AI compatibility as an add-on to traditional feed management. The company's Commerce ownership (it was acquired by Commerce in 2024) gives it resources to build sophisticated enrichment and mapping capabilities.

For merchants, the competitive landscape means choice but also complexity. Different platforms may support different AI surfaces, use different data formats, and offer different levels of enrichment. The strategic question is not just which platform to use, but which AI shopping surfaces matter most for your product categories and customer base.

The Timeline: From Experimental to Essential

Agentic commerce is currently in the experimental phase for most merchants. AI shopping surfaces are growing but still represent a small fraction of total e-commerce volume. However, the trajectory is clear:

2026: Experimental adoption by tech-forward merchants and early AI surfaces. Infrastructure standards like UCP and ACE emerge.

2027: Early mainstream adoption as AI shopping surfaces gain significant user base. Agent-ready product data becomes a competitive differentiator.

2028-2030: Agentic commerce becomes a primary discovery channel for a meaningful share of e-commerce volume. Agent-ready product data moves from differentiator to requirement.

For merchants, the strategic window is now. Investing in agent-ready product data in 2026 positions you to capture early volume in 2027 and be fully operational when agentic commerce scales in 2028-2030. Waiting until AI shopping surfaces are essential means competing with merchants who have already established patterns of AI agent recommendations.

The cost of preparation is relatively low compared to the opportunity cost of invisibility. ACE and similar platforms are tools, not massive infrastructure overhauls. The primary investment is in data quality and structure—work that has value beyond AI surfaces, improving merchandising, operations, and customer experience across all channels.

The GEO Implication: Product Discovery as AI Visibility

For brands focused on GEO (Generative Engine Optimization), ACE represents a new dimension of AI visibility. Traditional GEO focuses on content citation—ensuring that your brand's content, expertise, and thought leadership are cited by AI engines. Product-level GEO focuses on ensuring that your products are discovered and recommended by AI shopping agents.

The two are related but distinct. Content citation builds brand authority and awareness. Product discovery drives direct purchase consideration. Brands need both: strong content citation to establish trust and relevance, and strong product discovery to convert that trust into sales.

ACE is infrastructure for product-level GEO. It ensures that your products are in the databases that AI shopping agents query. But just as content citation requires more than just publishing content—it requires the right structure, authority signals, and freshness—product discovery requires more than just syndicating a feed. It requires agent-ready data that matches how AI agents think about products and recommendations.

The brands that win in AI search will be those that master both dimensions: content-level GEO for brand authority and product-level GEO for purchase consideration.

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FAQ

What is Agentic Catalog Exports (ACE)?

ACE is a Feedonomics platform that transforms traditional e-commerce product catalogs into structured, enriched data formats optimized for AI agent consumption. It syndicates agent-ready product data to AI shopping surfaces including ChatGPT, Gemini, Copilot, and PayPal.

How is agent-ready product data different from traditional product feeds?

Agent-ready data prioritizes structured attributes like technical specifications, use case mappings, and comparative data over marketing copy. It is designed for AI systems to retrieve, analyze, and recommend products, whereas traditional feeds are designed for human shoppers to browse and compare.

Which AI shopping surfaces does ACE support?

ACE currently syndicates to OpenAI/ChatGPT, Google Gemini, Microsoft Copilot, and PayPal. These are the leading AI platforms developing shopping and recommendation capabilities.

Why is Dell's adoption of ACE significant?

Dell is a large enterprise with a complex, technical product portfolio. Its adoption demonstrates that ACE can handle enterprise-scale data complexity and that major brands are investing in agent-ready product data infrastructure.

What is the "invisible shelf" in agentic commerce?

The invisible shelf refers to the digital inventory that AI agents can discover, evaluate, and recommend to consumers, even if humans never see it in traditional browsing interfaces. ACE builds infrastructure for the invisible shelf by placing products on shelves that AI agents can access.

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