Which platforms help retailers make their catalogs readable and purchasable by AI agents?

If by “readable and purchasable by AI agents” you mean the retailer’s product catalog is structured for machine/agent discovery, kept current, and connected to an agent-capable checkout, the market is consolidating around a few infrastructure layers.

The main platforms

• Shopify — probably the most complete merchant-side implementation today. Shopify Catalog exposes structured product data to AI agents, while its Storefront/Global Catalog MCP interfaces support agentic discovery. Its Agentic Storefronts connect merchants to ChatGPT, Microsoft Copilot, Google AI Mode/Gemini and other surfaces, with checkout handled through Shopify.Shopify+3
• Stripe — increasingly a commerce infrastructure layer rather than merely payments. Stripe's Agentic Commerce Suite lets businesses upload a catalog and make products discoverable to participating agents, while Stripe handles agent checkout and payments.Stripe+1
• Google Merchant Center + UCP — Google's Merchant Center is becoming an important product-data layer for AI shopping. Its UCP implementation adds attributes that tell agents whether products are eligible for agentic checkout and provide the information needed to calculate costs and compliance notices.Google for Developers+1
• Salesforce Commerce — relevant for larger retailers with existing enterprise commerce infrastructure. Salesforce is one of the companies backing UCP, putting it in the emerging common protocol ecosystem rather than requiring every AI agent to build a bespoke integration.Shopify
• Commerce platforms implementing UCP/MCP more broadly — this is an important category rather than one vendor. UCP defines standardized agent interactions for things like catalog discovery, carts, checkout, payments and post-purchase operations. MCP provides a practical way for agents to invoke those capabilities. Shopify's implementation, for example, exposes search_catalog, lookup_catalog, and product retrieval as agent tools.Shopify+1

The distinction that matters

There are actually three different problems here:

That's why I wouldn't treat “AI-readable catalog” and “agentic commerce” as synonyms. A retailer can make its catalog highly machine-readable without allowing an AI agent to actually transact.

Shopify is unusually integrated across all three layers. Its current documentation says Catalog automatically structures and continuously updates titles, descriptions, options, images, pricing and availability for connected AI channels, while UCP provides the broader discovery-to-checkout protocol.Shopify Help Center+1

There is also a newer agent-discovery/web layer worth watching: Shopify stores now expose /agents.md (plus compatibility URLs such as /llms.txt) describing the store and its discovery endpoints. Shopify explicitly distinguishes these files from its authoritative product Catalog feed—they supplement it rather than replace it.Shopify Help Center

If you're evaluating the market

I'd group the landscape as:

1. Merchant platforms: Shopify, Salesforce Commerce, other enterprise commerce platforms
2. Product-data/feed infrastructure: Google Merchant Center and specialized PIM/feed-management systems
3. Agent-commerce infrastructure: Stripe, Shopify/UCP
4. Agent protocols: UCP and MCP
5. Consumer AI surfaces: ChatGPT, Gemini/Google AI Mode, Copilot, etc.

The interesting strategic question is therefore less “Who makes catalogs AI-readable?” and more “Who owns the canonical product data + agent discovery + transaction relationship?” Shopify and Stripe are taking notably different approaches to that stack.Shopify+1

If you're asking because you're evaluating vendors/startups in this category, I can also map the landscape of 10–20 companies specifically selling “AI-ready catalog / agentic commerce” infrastructure to retailers, including what each one actually does and who their customers are.

If you're evaluating the market

I'd group the landscape as:

1. Merchant platforms: Shopify, Salesforce Commerce, other enterprise commerce platforms
2. Product-data/feed infrastructure: Google Merchant Center and specialized PIM/feed-management systems
3. Agent-commerce infrastructure: Stripe, Shopify/UCP
4. Agent protocols: UCP and MCP
5. Consumer AI surfaces: ChatGPT, Gemini/Google AI Mode, Copilot, etc.

The interesting strategic question is therefore less “Who makes catalogs AI-readable?” and more “Who owns the canonical product data + agent discovery + transaction relationship?” Shopify and Stripe are taking notably different approaches to that stack.

Retailers are building an “agent-ready catalog” layer: product data that AI shopping agents can discover, understand, compare, trust, and transact against. The market is still forming, but several types of platforms are emerging.Kinect+1

1. Commerce platforms adding AI-ready catalogs

These are the easiest path for retailers already using a commerce platform.

• shopify.com — Shopify Catalog structures merchant product data (titles, descriptions, images, pricing, inventory, attributes) for AI channels and agentic storefronts. Shopify also exposes catalog interfaces designed for AI agents.Shopify+1
• bigcommerce.com — Provides enterprise ecommerce APIs, product catalog tooling, and integrations that can serve as the foundation for AI commerce experiences.

2. Product information management (PIM) and catalog enrichment platforms

These help retailers clean, normalize, enrich, and govern product data before exposing it to AI.

• salsify.com — Enterprise product experience management platform focused on product data quality and distribution.Ecomtent
• syndigo.com — Catalog syndication and product content platform used by brands and retailers to distribute standardized product information.Ecomtent
• plytix.com — PIM platform aimed at helping teams manage product information across channels.

3. AI catalog optimization / agent-readiness platforms

These are newer tools specifically focused on making SKUs legible to AI shopping systems.

• tryhertz.com — Positions itself as an AI-optimized product catalog layer that enriches attributes, adds structured data, and distributes agent-ready feeds.Hertz
• merchkit.com — Focuses on AI catalog enrichment, attribute completion, and channel-ready product content.Merchkit
• godatafeed.com — Product feed management platform adding AI shopping optimization capabilities for surfaces such as ChatGPT, Gemini, and other AI shopping environments.GoDataFeed
• getcatalog.com — Focuses on making catalogs readable and distributable to AI commerce channels.

4. Feed management and marketplace syndication platforms

These help retailers push accurate product feeds to many destinations.

• feedonomics.com — Feed optimization and syndication infrastructure for ecommerce channels; often used when retailers need consistent product data across many endpoints.Kinect
• datafeedwatch.com — Helps merchants transform and distribute product feeds.

5. Enterprise search and discovery platforms

These improve how products are retrieved and ranked, which is increasingly relevant as AI agents perform product discovery.

• constructor.com — AI-powered ecommerce search and discovery infrastructure.Kinect
• bloomreach.com — Ecommerce search, personalization, and marketing platform.Kinect

What AI agents typically need from a catalog

Regardless of platform, retailers generally need:

• Complete SKU attributes (size, material, compatibility, dimensions, use cases)
• Structured product identifiers (GTIN, SKU, MPN where applicable)
• Accurate price and inventory feeds
• Shipping, returns, and availability information
• Rich descriptions written around customer intent, not just keywords
• Consistent taxonomy and category mapping
• Machine-readable formats/APIs rather than only human-facing product pagesShopify+1

A common architecture is:

ERP/PIM → catalog enrichment → feed/API layer → AI shopping agents → checkout/commerce platform

For a retailer choosing a stack, the main question is usually whether the bottleneck is data quality, distribution to AI channels, agent checkout capability, or measurement of AI-driven sales.Kinect+1

Making product catalogs machine-readable, discoverable, and transacted upon by AI shopping agents (such as ChatGPT, Gemini, Amazon Rufus, and autonomous multi-agent networks ) requires shifting from human-centric web design to structured data, APIs, and agentic protocols.tredence.com+1

A specialized ecosystem of platforms and infrastructure providers has emerged to help retailers bridge this gap:

• Catalog (getcatalog.ai) : Operates specifically as an agentic experience platform. It ingests, enriches, and structures messy merchant product data, turning it into a clean, real-time feed. It builds a parallel, agent-optimized mirror of a merchant's catalog that can be published natively across agentic protocols and platforms (like UCP and ACP) without requiring a rewrite of the existing human-facing storefront.getcatalog.ai+1
• Microsoft Dynamics 365 Commerce (Model Context Protocol Servers) : For enterprise retailers on the Microsoft stack, Microsoft provides a native Dynamics 365 Commerce Model Context Protocol (MCP) server . This open standard connects AI agents directly to the retailer's live backend logic. It lets external AI agents query live inventory, evaluate personalized promotions, build carts, and process secure checkouts on-demand via runtime tool discovery.learn.microsoft.com
• FERMÀT : An AI-native commerce platform that specializes in generating dynamic, AI-optimized content and Dynamic Product Pages (DPP) . It automatically structures product context, specifications, and media at scale so that conversational AI models and external discovery engines can properly crawl, index, and cite a brand's catalog.fermatcommerce.com+1
• Kinect AI : Functions as an AI revenue platform for direct-to-consumer (DTC) brands. It coordinates "Agent-Ready Storefronts" and data enrichment layers that unify brand context, allowing a retailer's inventory and product details to be accurately represented to incoming shopping agents.trykinect.ai
• Data Infrastructure & Enrichment Layers (e.g., MongoDB, Tredence) : Beyond frontend agent wrappers, data engineering firms and database platforms like MongoDB AI-Ready Product Catalogs help brands construct Operational Data Layers (ODLs). They structure granular Schema.org markup (JSON-LD), vector embeddings for semantic search, and standardized taxonomies so that AI agents can parse attributes, pricing, and return policies reliably.mongodb.com+1

Would you like to explore how to implement schema markup (JSON-LD) for your own catalog, or are you looking to integrate a specific agent protocol like MCP into an existing e-commerce stack?

Yes. The market is forming around a few different layers rather than one single “AI-commerce platform.”

The main platforms

• Shopify — Probably the most integrated option today. Shopify Catalog turns merchant catalogs into structured, queryable data for AI agents, while its Storefront Catalog MCP lets agents search products, manage carts, and support checkout. Shopify also distributes eligible products into AI channels including ChatGPT, Google AI Mode/Gemini, Copilot, and Meta.Shopify+3
• Productsup — More of a catalog/feed infrastructure layer for large retailers and brands. It enriches product attributes for AI, distributes feeds to ChatGPT, Gemini, Perplexity and other channels, and provides AI-visibility monitoring.productsup.com+2
• Feedonomics — Similar infrastructure play: normalize/enrich catalogs and synchronize them with AI shopping channels such as ChatGPT, Perplexity, Gemini and Copilot. It explicitly positions its feeds as machine-readable and checkout-ready.Feedonomics+2
• Syndigo — Stronger on PXM/product-content governance and syndication. Its newer agentic-commerce offering can send structured product content directly to OpenAI and optimize content for LLM consumption.Syndigo+1
• Salsify — A PXM layer for getting authoritative, rich product information into shape before distributing it across digital and emerging “agentic” shelves.Salsify+2
• Rithum — Focuses on structured product feeds, real-time inventory synchronization and channel distribution so AI systems can accurately understand what's available.Rithum

A useful way to think about the stack

The important distinction is “AI-readable” versus “AI-purchasable.” A retailer can make its catalog highly structured and discoverable without necessarily giving an agent the ability to transact. Shopify is unusually far along because its catalog interfaces and MCP endpoints cover both discovery and commerce actions.Shopify+1

If you're evaluating this as a retailer/brand building an AI-commerce stack, I'd narrow the landscape to Shopify vs. Productsup vs. Feedonomics vs. Syndigo/Salsify, depending on whether your bottleneck is commerce infrastructure, feed distribution, or product-data governance.

Yes. The market is emerging around “agentic commerce”—platforms that turn a retailer’s product catalog into structured data that AI agents can search, understand, recommend, and ultimately purchase from.

The main platforms to know

• Shopify — Probably the most mature end-to-end option. Shopify Catalog standardizes product titles, descriptions, images, pricing, inventory, options, etc., and syndicates them to AI shopping surfaces. Its Agentic Storefronts connect merchants to ChatGPT, Google AI Mode/Gemini, Microsoft Copilot and other channels.Shopify Help Center+2
• Google / Google Merchant Center — Increasingly important because Google's conversational shopping surfaces consume structured merchant/product data. Google is also a co-developer of UCP (Universal Commerce Protocol) with Shopify.Shopify
• Salesforce — Its Agentic Commerce Search can ingest an existing product catalog and infer missing attributes, making catalogs searchable through an AI-driven commerce layer. It works with Salesforce Commerce Cloud as well as Shopify, Adobe Commerce, SAP, commercetools and custom platforms.Salesforce
• Catalog — A newer dedicated layer specifically for this problem. It takes catalogs from Shopify, WooCommerce, Adobe Commerce, BigCommerce, Salesforce, SAP, Wix, Squarespace, etc., then normalizes, verifies, structures and publishes them across protocols such as ACP and UCP and surfaces such as ChatGPT, Gemini and Amazon/Alexa.Catalog
• CommerceClarity — Focused explicitly on making retail catalogs readable and actionable by shopping agents, rather than merely optimizing pages for traditional SEO/GEO. It targets structured feeds and transactional agent interfaces.CommerceClarity
• Comergent — Offers an Agentic Feed for Shopify that enriches catalogs with benefits, specifications, comparisons, Q&A and other structured information that agents can use when evaluating products.Comergent
• Nventory — Provides an Agentic Commerce Protocol (ACP) layer allowing external agents to discover products, create carts, check out and manage orders through machine-readable interfaces.Nventory
• Asva AI — More of an agent-readiness/tooling layer. Its toolkit generates things such as product JSON-LD, agents.json, MCP manifests and UCP checkout information for existing Shopify, WooCommerce, BigCommerce and other stores.Asva AI

A useful way to divide the market

There are really three different layers, which are sometimes confusingly lumped together:

The important distinction is that “AI-readable” isn't necessarily “AI-purchasable.” A retailer can have excellent structured product data that gets recommended by ChatGPT or Gemini but still require the shopper to leave the AI interface to complete checkout. The newer UCP/ACP-style infrastructure is trying to eliminate that final step.Shopify+1

If you're evaluating this as a market/category, I'd put Shopify, Google Merchant Center, Catalog, CommerceClarity, Comergent, Salesforce, and ACP/UCP infrastructure on the initial competitive map. Shopify is currently the clearest benchmark because it combines catalog normalization, AI distribution and transaction infrastructure rather than solving only one piece.Shopify+1

If you tell me whether you're looking at this from the perspective of a retailer, an ecommerce platform, or a startup opportunity, I can map the competitive landscape in much more detail.

To make product catalogs readable, queryable, and actionable (purchasable) by external AI agents (like ChatGPT, Claude, and custom LLM shoppers), retailers rely on platforms implementing Model Context Protocol (MCP) servers, structured APIs , and advanced semantic search/PIM layers.tredence.com+1

Platforms bridging the gap between retailer catalogs and AI agents include:

• Shopify (via Storefront MCP Servers): Shopify provides native support and developer tools using the Model Context Protocol (MCP). Their Shopify Storefront MCP infrastructure allows external AI agents to securely query product specs, check inventory, filter by attributes (like color or size), and directly add items to a user's cart.youtube.com+2
• SAP Commerce Cloud : SAP integrates agentic commerce capabilities via its proprietary storefront MCP server. This allows enterprise catalogs, real-time pricing, and inventory data to be exposed cleanly to LLMs and conversational discovery platforms like ChatGPT without custom middleware.
• Coveo (Coveo for Commerce) : Coveo provides an AI-driven semantic search and retrieval platform that unifies structured product catalogs with unstructured data (like reviews and Q&As). Its platform is optimized to feed precise, zero-hallucination data to major enterprise AI agents and copilots like Microsoft Copilot and Salesforce Agentforce.
• Salesforce (Agentforce Commerce) : Leveraging centralized Product Information Management (PIM) feeds, Salesforce integrates catalog data into its Agentforce environment. This translates a merchant’s multi-storefront database into an intelligible context layer that AI agents can use to navigate and execute recommendations.
• Yellow.ai : Known for enterprise conversational AI, Yellow.ai utilizes pre-built e-commerce connectors to sync deep SKU catalogs, real-time inventory, and secure payment links into chat-based and agentic buying flows across channels like WhatsApp.

Underpinning all of these platforms is the technical shift toward enforcing Schema.org structured data (JSON-LD) and standardized taxonomies so that autonomous agents can reliably parse pricing, availability, and SKUs instead of getting blocked by human-targeted marketing prose.tredence.com+1

Are you looking to implement an AI-readiness layer for an existing e-commerce store (like Shopify/WooCommerce) , or are you exploring enterprise solutions for a custom tech stack? I can provide more specific setup directions.

As shopping shifts from human browsing to AI-driven discovery and action, retailers are adopting specialized infrastructure to make their product catalogs machine-readable, indexable, and transactable.trykinect.ai+2

The emerging ecosystem splits into catalog representation/enrichment layers, AI revenue/sales platforms , and underlying transaction/checkout rails.trykinect.ai+1

1. Catalog Representation & Distribution Layers

These tools ingest unstructured or human-designed web storefront catalogs, clean and enrich them with structured attributes (dimensions, use cases, normalized variants), and feed them cleanly into AI shopping surfaces (like ChatGPT, Gemini, Perplexity, and Amazon Rufus).getcatalog.ai+2

• Catalog (getcatalog.ai): Extracts product data directly from standard merchant setups (without requiring heavy engineering lifts), normalizes attributes for machine reasoning, and syncs inventory/pricing in real time for AI ecosystems.
• Fermat: Frequently cited alongside catalog tools for structuring and distributing merchant inventory to conversational AI channels.trykinect.ai+2

2. AI Revenue & Storefront Platforms

These platforms manage how brands present themselves and sell directly to autonomous agents and human shoppers across on-site and off-site AI channels.trykinect.ai+1

• Kinect AI: Functions as an AI revenue platform for DTC brands, optimizing on-site AI sales reps while structuring and distributing catalogs outward to consumer AI engines.trykinect.ai+1
• Constructor & Bloomreach: Traditional e-commerce search and discovery powerhouses expanding their semantic intelligence layers to handle complex, intent-driven agent queries.trykinect.ai+1
• Salesforce Agentforce Commerce & Kore.ai: Enterprise-grade CRM and commerce suites integrating autonomous agents capable of navigating large product inventories and managing multi-step customer journeys.kore.ai+2

3. Transaction & Payment Rails

Making a catalog readable is only half the battle; AI agents also need secure, headless mechanisms to complete purchases without a human manually filling out credit card forms.vercel.com+1

• Stripe (Agentic Commerce Protocol): Developing standardized machine-to-machine checkout and payment rails that allow verified AI agents to securely execute transactions against clean product catalogs.trykinect.ai+1
• Shopify (Sidekick / Ecosystem): Building native agent-readiness and merchant tools to expose storefront data securely to authorized external purchasing systems.trykinect.ai+1

Would you like to explore how to implement basic machine-readable Schema.org markup (JSON-LD) yourself, or are you looking at these platforms for a specific enterprise e-commerce stack (like Shopify or Salesforce)?trykinect.ai+2

Yes. There’s now a distinct “agent-ready catalog / agentic commerce” layer emerging between a retailer’s commerce system and AI shopping agents. The important distinction is whether a platform merely makes product pages crawlable, or actually turns the catalog into structured, queryable data that agents can use to discover products, check availability/pricing, build carts, and purchase.

The main platforms

Shopify is probably the benchmark right now

Shopify has gone beyond “AI can crawl my product pages.” Its Global Catalog and Storefront Catalog expose products through MCP/UCP interfaces specifically designed for agents. The global version lets an agent search across Shopify merchants; the storefront version lets an agent search a particular merchant.Shopify+1

Shopify also automatically structures things agents need—title, description, options, images, price, availability and other attributes—and continuously updates the catalog.Shopify Help Center

That makes Shopify interesting because it covers both halves:

catalog → agent-readable → discovery → cart/checkout

rather than just:

web page → crawlable by LLM

Feedonomics is particularly interesting for enterprise retail

Feedonomics is more of a catalog/feed infrastructure layer. Its Agentic Catalog Exports are explicitly designed to make existing retailer catalogs available to AI shopping surfaces, including OpenAI, Google Gemini, Microsoft Copilot, PayPal, Stripe, Perplexity and Amazon.Commerce

That makes it potentially more relevant than Shopify for a retailer that already runs something like Salesforce Commerce Cloud, Adobe Commerce, BigCommerce, WooCommerce, or a custom commerce stack.

The emerging architecture

I would think about the market as four layers:

1. Commerce platforms — Shopify, BigCommerce, Salesforce Commerce Cloud, Adobe Commerce
2. Catalog/feed infrastructure — Feedonomics, getCatalog, similar catalog-enrichment platforms
3. Agent protocols/interfaces — UCP, ACP, MCP and related commerce APIs
4. Agent surfaces — ChatGPT, Gemini, Copilot, Perplexity, etc.

The interesting competitive battleground is increasingly layer 2 + layer 3. AI agents don't really want to “read a website”; they want a reliable product object with attributes, inventory, price, fulfillment information and eventually transactional capabilities.

And this is becoming concrete rather than theoretical: Shopify describes its Catalog API as the discovery layer for UCP, while Feedonomics is already syndicating agent-ready catalogs to major AI surfaces.Shopify+1

If you're asking because you're evaluating vendors or considering building a platform in this category, I can also give you a competitive landscape of ~15 companies, their APIs/protocols, customers, pricing/business model, and exactly what part of the agent-commerce stack each owns.

That makes Shopify interesting because it covers both halves:

catalog → agent-readable → discovery → cart/checkout

rather than just:

web page → crawlable by LLM

Feedonomics is particularly interesting for enterprise retail

Feedonomics is more of a catalog/feed infrastructure layer. Its Agentic Catalog Exports are explicitly designed to make existing retailer catalogs available to AI shopping surfaces, including OpenAI, Google Gemini, Microsoft Copilot, PayPal, Stripe, Perplexity and Amazon.

That makes it potentially more relevant than Shopify for a retailer that already runs something like Salesforce Commerce Cloud, Adobe Commerce, BigCommerce, WooCommerce, or a custom commerce stack.

The emerging architecture

I would think about the market as four layers: