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

Retailers adapting to agentic commerce —where AI assistants, chatbots, and autonomous shopping agents browse and buy products on behalf of consumers—rely on emerging open protocols, structured data layers, and e-commerce platform integrations.

Making a catalog "readable and purchasable" requires two components: discoverability/readability (structured data, semantics, and protocols) and actionability (secure cart-building, authentication, and checkout integration).

1. Open Standards & Protocols for Agentic Commerce

• Universal Commerce Protocol (UCP): Co-developed by industry heavyweights including Google and Shopify , alongside retail partners like Walmart, Target, Etsy, and Wayfair . UCP (ucp.dev ) is an open standard designed specifically to standardize how AI agents discover merchant inventories, negotiate attributes, handle user identity, build carts, and trigger checkouts. It natively integrates with frameworks like the Model Context Protocol (MCP) and Agent2Agent (A2A) protocols.
• Google Merchant Center & Business Agents: Google provides structured data attribute frameworks and manifest specifications through Merchant Center that allow a retailer's product catalog to plug directly into conversational surfaces like the Gemini app and AI-driven search modes.

2. Major E-Commerce Platforms

• Shopify: As a co-developer of UCP and deep ecosystem partner for agentic frameworks, Shopify automatically translates merchant inventory into structured, machine-readable formats. Their infrastructure handles the backend plumbing so AI systems can query store inventories and seamlessly route transactions without breaking merchant-of-record status.
• Enterprise Cloud & Conversational Middleware (AWS, Kore.ai, Sierra AI):
  • Amazon Web Services (AWS) offers infrastructure toolkits that let brands build custom, catalog-aware conversational shopping agents capable of navigating live product inventory.
  • Enterprise Agent Platforms like Kore.ai and Sierra AI integrate deeply with enterprise product catalogs, using orchestration layers to bridge unstructured user intent with structured product inventory databases.

3. Catalog Data Infrastructure & Search Optimization

• Algolia (NeuralSearch): Algolia provides hybrid semantic-keyword search and API layers that allow external LLMs and AI agents to query product catalogs with high contextual accuracy, translating natural language queries into exact catalog matches.
• Data Enrichment & Validation Specialists (e.g., Tredence): For large enterprise inventories, data engineering firms build real-time validation pipelines and unified data layers. They automatically enrich messy product attributes (sizing, specifications, compatibility) into clean, machine-readable states that AI search models require.

Are you looking to optimize a specific e-commerce platform (like Shopify or a custom stack) for AI agents, or exploring the technical implementation of protocols like UCP?

Yes. The market is emerging around “agentic commerce”—making a retailer’s product catalog structured, discoverable, understandable, and ultimately purchasable by AI agents.

The main platforms to know

• Shopify — Probably the most comprehensive option today. Shopify Catalog standardizes product data and exposes it to AI channels, while Agentic Storefronts connects merchants to ChatGPT, Google AI Mode/Gemini, Microsoft Copilot, and Meta. Shopify also offers the Catalog API and Universal Commerce Protocol (UCP) for developers.
• Stripe — More focused on the transaction/checkout layer than catalog enrichment. Its Agentic Commerce Protocol (ACP) lets AI agents discover products and transact with merchants, with Stripe handling payment infrastructure.
• Google / Google Merchant Center — Still an important product-data foundation for Google's AI shopping surfaces. Shopify's current integration, for example, uses the Google & YouTube channel to expose products to Google AI Mode and Gemini.
• getcatalog.ai — A newer, purpose-built AI product-data layer. It enriches retail catalogs and supports agent protocols including ACP and UCP, with integrations around Shopify and WooCommerce.
• Mercentia — An emerging AI-native commerce platform that provides a canonical product feed, catalog AI-readiness scoring, MCP access, and syndication to multiple AI shopping surfaces.
• CockpitAI — Focuses specifically on Agentic Commerce Optimization (ACO): enriching titles, attributes, structured data, and feed signals so AI agents can understand and recommend SKUs.
• Asva AI — More of an agent-readiness toolkit: generates things like product JSON-LD, agent manifests, MCP manifests, and checkout information for existing Shopify, WooCommerce, BigCommerce, Squarespace, or custom stores.

A useful way to segment the landscape

The important distinction is that “readable by AI” and “purchasable by AI” are becoming separate infrastructure problems. A retailer can have excellent structured product data but still lack an agent-compatible cart/checkout, while a payment protocol alone doesn't solve poor catalog data.

If you're evaluating this as a market/category, I'd put Shopify, Stripe, Google, getcatalog.ai, and the emerging UCP/ACP ecosystem at the center of the landscape right now. Shopify is particularly notable because it is trying to own both sides: the catalog/discovery layer and the transaction layer.

Yes. The market is emerging around “agentic commerce” / “AI-readable catalogs”—platforms that turn a retailer’s product data into structured feeds/APIs that AI shopping agents can search, compare, and increasingly purchase from.

The main platforms to know

There are really two different layers

This distinction is important.

1. Catalog/discovery layer: Makes SKUs understandable to AI: titles, attributes, variants, GTINs, images, price, inventory, shipping, returns, etc. Shopify Catalog, Mirakl, Catalog.ai and similar products operate here. AI agents increasingly consume structured product feeds rather than relying solely on scraping human-oriented webpages.

2. Transaction/agent-commerce layer: Lets an agent actually perform actions—search, create a cart, check availability, initiate checkout and potentially complete a purchase. This is where protocols such as MCP, ACP and UCP come in. Shopify's current Storefront Catalog MCP, for example, is explicitly designed for an AI agent to search and discover products from a merchant's catalog.

There's also an important distinction between merchant infrastructure and product-discovery networks. For example, BuyWhere is an agent-native API aggregating millions of products across retailers; that's primarily a data source for shopping agents, rather than software a retailer installs to make its own catalog agent-ready.

If you're evaluating this as a market

I'd divide the competitive landscape into:

• Ecommerce platforms: Shopify
• Enterprise catalog/marketplace platforms: Mirakl
• Agentic catalog middleware: Catalog.ai, Mercentia, Comergent
• Agentic order/fulfillment infrastructure: Directify and similar platforms
• Agent-optimized catalog/GEO tooling: CockpitAI and emerging ACO vendors
• Open protocols: MCP, UCP, ACP, plus emerging open standards such as OCP
• Aggregated product-data APIs: BuyWhere and similar networks

The particularly interesting strategic question is whether the winning product is a catalog optimizer, a protocol adapter, or the network that becomes the default product-data source for agents. Those are quite different businesses. Shopify is attempting to cover several layers simultaneously.

If you're asking because you're mapping competitors for a startup/product in this space, I can also give you a more rigorous landscape of ~20 companies, with funding, customers, protocols supported, Shopify/WooCommerce compatibility, pricing, and where each sits in the stack.

1. Catalog/discovery layer: Makes SKUs understandable to AI: titles, attributes, variants, GTINs, images, price, inventory, shipping, returns, etc. Shopify Catalog, Mirakl, Catalog.ai and similar products operate here. AI agents increasingly consume structured product feeds rather than relying solely on scraping human-oriented webpages.

2. Transaction/agent-commerce layer: Lets an agent actually perform actions—search, create a cart, check availability, initiate checkout and potentially complete a purchase. This is where protocols such as MCP, ACP and UCP come in. Shopify's current Storefront Catalog MCP, for example, is explicitly designed for an AI agent to search and discover products from a merchant's catalog.

There's also an important distinction between merchant infrastructure and product-discovery networks. For example, BuyWhere is an agent-native API aggregating millions of products across retailers; that's primarily a data source for shopping agents, rather than software a retailer installs to make its own catalog agent-ready.

As shopping shifts from human browsing to agentic commerce —where AI assistants like ChatGPT, Perplexity, and Google Gemini discover, compare, and buy products directly inside chat interfaces—traditional website layouts are no longer enough. AI agents require clean, structured, and machine-readable product data.

A new category of specialized infrastructure and e-commerce platforms has emerged to make retail catalogs fully readable and transacting-ready for AI agents:

• • Norg : Specifically built as AI infrastructure for agentic commerce, Norg connects to a brand's full product catalog (including technical data, reviews, and specs) and simultaneously publishes it across all formats that AI systems consume—including optimized HTML structured data, direct agentic product feeds, and LLM inference-time retrieval directories.
• • Shopify : As a dominant e-commerce backbone, Shopify has integrated directly with protocols enabling agentic checkouts (such as OpenAI's and Google's agent e-commerce ecosystems), allowing merchants to expose their standardized inventory feeds for in-chat discovery and direct purchases.
• • Rep AI : An agentic commerce platform built deeply into ecosystems like Shopify Plus. It trains an on-site behavioral AI agent directly on a brand's product catalog, structuring shopper intent and conversation loops so that AI can surface accurate inventory details and drive in-chat add-to-carts.
• • Google Cloud / Google Merchant Center : Google provides open protocols and cloud infrastructure (supporting massive catalog rollouts like Wayfair's 30+ million SKUs) to feed merchant-to-merchant and consumer-to-merchant AI agent interactions safely.
• • Perplexity Merchant Program : While functioning as a discovery engine rather than a backend platform, Perplexity’s merchant integrations and API programs allow brands to structure, feed, and optimize their product catalogs specifically for recommendation inside Perplexity’s AI-powered shopping assistant.

Would you like to explore how to structure a standard product feed for LLMs , or are you looking to integrate agent-ready checkout protocols for a specific e-commerce platform like Shopify?

Yes. The market is converging around “agentic commerce”: platforms that turn a retailer’s product/catalog data into structured, machine-readable inventory and expose search, pricing, cart, checkout, and order capabilities to AI agents.

The main players I’d look at are:

The particularly interesting development: Shopify Catalog

If by “make their catalogs readable and purchasable by AI agents” you mean a platform that sits between retailers and many different AI agents, Shopify is arguably the clearest example.

Shopify now has two catalog interfaces:

• Global Catalog — an AI agent can search across products from Shopify merchants.
• Storefront Catalog — an agent can search a particular merchant's catalog.

Both use UCP's catalog capability and MCP, with structured product information rather than forcing the agent to scrape HTML.

And Shopify's agentic stack goes beyond discovery: its UCP/MCP tooling can move from product → variant → cart → checkout → order tracking.

Importantly, Shopify announced an Agentic plan for non-Shopify merchants in January 2026. That is particularly relevant to your wording: retailers don't necessarily have to replatform their commerce stack to get their catalog into Shopify's AI distribution network.

There are really three layers emerging

I'd separate the market into:

1. Catalog enrichment / “make my SKUs understandable”
Tools ingest messy PIM/ecommerce feeds, normalize attributes, infer missing information, and produce agent-friendly structured data. Salesforce's Agentic Commerce Search is an example of this approach.

2. Agent-accessible commerce infrastructure
MCP/UCP APIs let an agent actually query inventory, prices, variants, promotions, carts and orders. Shopify and commercetools are strong examples.

3. Distribution into consumer AI surfaces
This is where the retailer gets discovered inside ChatGPT, Gemini, Google Search/AI Mode, etc., rather than relying on an agent to crawl the retailer's website. Shopify and Google are particularly aggressive here.

So if you're researching this as a competitive landscape, I'd put Shopify, commercetools, Salesforce, Google, Adobe, and BigCommerce on the core platform map—but I'd also look closely at the emerging specialist “agent-readiness/catalog optimization” middleware companies. That's a distinct category from the commerce platforms themselves.

If you tell me whether you're looking at this as a retailer, an AI-agent builder, or as a potential startup/product opportunity, I can map the companies much more deeply—including who owns the catalog layer, who owns checkout, APIs/protocols used, pricing, and where there are still gaps.

To make product catalogs readable, discoverable, and purchasable by AI agents (a shift known as agentic commerce ), retailers rely on a new stack of infrastructure platforms, data layers, and protocols . Instead of relying on traditional web page layouts or human-targeted SEO, these platforms structure data via APIs, metadata standards, and direct transaction rails.

Platforms facilitating this transition fit into four primary categories:

• Specialized AI Data & Readability Layers
  • Catalog (getcatalog.ai) : Transforms raw e-commerce product fields (from Shopify, WooCommerce, etc.) into structured, machine-readable data designed specifically for AI channels.
  • Norg : A publishing platform that takes existing inventories (like Google Merchant Center feeds) and simultaneously formats them into HTML with structured data, AI discovery files, and knowledge graphs.
  • Logicbroker : Offers enterprise-grade product content syndication that leverages protocols like Model Context Protocol (MCP) to make complex B2B and retail catalogs instantly readable by large language models (LLMs).
• E-Commerce Giants & Native Protocol Bridges
  • Shopify : Integrates deeply into agentic shopping channels by auto-syndicating merchant catalogs into major AI environments using standardized MCP servers and Universal Cart frameworks while keeping the merchant of record.
  • Microsoft Dynamics 365 Commerce : Exposes core retail business logic—including catalogs, live inventory, pricing, promotions, and carts—as MCP-enabled capabilities so AI agents can query and execute workflows directly.
  • Salesforce Agentforce Commerce : Unifies digital, point-of-sale, and product attributes into a structured data backbone that feeds conversational shopping across both owned and external AI agents.
• Transactional & Payment Infrastructure
  • Stripe Agentic Commerce Suite : Bridges AI agents (like ChatGPT Shopping) directly with merchant backends, handling product catalog syndication, secure tokenized payments, and fraud detection.
  • Google Merchant Center & Universal Commerce Protocol (UCP) : Uses an open standard that allows AI agents (like Gemini) to access real-time inventory and product details from participating retailers to execute direct "Buy for Me" transactions.
• Enterprise Product Information Management (PIM/PXM)
  • Syndigo : Enriches multidimensional product data (Master Data Management attributes, Generative Engine Optimization tags, and social proof) to ensure AI agents have the complete product context required to compare and recommend items accurately.
  • Tredence : Automates attribute enrichment and real-time feed validation to maintain a clean, fragmented-free live layer of catalog and inventory data for AI platforms.

If you are preparing a retail catalog for AI agents, would you like to explore implementing a specific protocol like MCP/UCP or focus on enriching your product data attributes first?

To make product catalogs readable, queryable, and purchasable by autonomous AI shopping agents (such as ChatGPT, Google Gemini/AI Mode, Perplexity, and Microsoft Copilot), retailers rely on a new wave of agentic commerce infrastructure, feed management platforms, and headless e-commerce stacks.

AI agents do not browse human-centric web pages; they ingest structured data feeds, Model Context Protocol (MCP) servers, and machine-parseable manifests.

The primary platforms and tools helping retailers bridge this gap include:

• Productsup (Agentic Commerce Solutions): A leading feed management and syndication platform that audits, transforms, and enriches product catalogs so they comply with emerging LLM discovery formats and agentic purchasing protocols.
• Norg: A specialized visibility platform that takes existing product feeds and simultaneously publishes them across all formats consumed by AI systems—including embedded HTML structured data, LLM inference-time retrieval files, and structured API endpoints.
• Mirakl (Agentic Activation): Enterprise marketplace and dropship software that features dedicated agentic infrastructure, enabling large-scale retailers and brands to clean, structure, and syndicate massive multi-vendor catalogs for AI discovery.
• Feedoptimise: A technical integration and feed engine that hooks directly into cloud storage, FTP, or APIs to continuously format and refresh retail product data to match changing AI shopping protocols.
• Shopify (Agentic Storefronts & MCP Catalogs): For the direct-to-consumer space, Shopify natively wires merchant catalogs into major AI discovery surfaces via standardized product data layers, Model Context Protocol (MCP) servers, and Universal Cart checkout integrations.
• commercetools (Agent Gateway): An API-first, headless commerce platform designed for enterprise retailers. It uses an Agent Gateway layer to expose complex, multi-region product and inventory data with the full semantic context that AI agents require.
• Salesforce (Agentforce & MCP-First Architecture): Salesforce is re-architecting its commerce cloud to be agent-readable from the ground up, allowing external AI systems to securely read from and write transactions directly to the system of record.
• Stripe (Agent Toolkit & Catalog Feeds): Providing the transactional backbone, Stripe's infrastructure and catalog feed syndication allow AI agents to safely parse real-time inventory/pricing and execute autonomous, tokenized payments.

If you're currently preparing a catalog, let me know:

• What e-commerce platform (Shopify, custom headless, Magento, etc.) are you using?
• Are you looking primarily for discovery visibility (getting recommended by ChatGPT/Perplexity) or autonomous checkout?

I can recommend the most direct integration path for your tech stack.

Yes. There’s a new-ish category forming around “agent-ready commerce”: platforms that turn a retailer’s product catalog into structured, enriched data that AI shopping agents can understand, discover, compare, and ultimately transact against.

The main players I’d look at are:

The important distinction

I’d divide the market into three layers rather than treating all of these as competitors:

1. Commerce platforms / infrastructure
Shopify and Google are building the underlying pipes. Shopify's Catalog API is explicitly designed to make millions of products structured and queryable by AI agents, while UCP standardizes agent ↔ merchant interactions from discovery through checkout.

2. Catalog enrichment / “AI readability” layers
Companies such as Catalog and ReFiBuy are attacking the problem that a normal PIM/feed often has technically valid but semantically thin data: missing materials, use cases, fit, compatibility, variant relationships, etc. Catalog, for example, explicitly describes transforming a conventional catalog into the richer product data AI weighs when making recommendations.

3. Agent-facing distribution and checkout
The other half is making that data available where agents actually shop. Shopify's Agentic Storefronts already expose merchants to ChatGPT, Copilot, Google AI Mode and Gemini, while Google is rolling out UCP-based agentic checkout.

Google is particularly interesting

Google has started adding “conversational attributes” specifically so AI systems can understand product nuances. These include things such as Q&A, related products, documents and item-group information.

And its new UCP infrastructure is explicitly intended to let agents communicate programmatically with merchant backends for product discovery, buying and post-purchase workflows.

If you're asking because you're evaluating the market…

I'd frame the competitive landscape as:

PIM/feed → AI enrichment → agent-readable catalog → AI discovery → agent checkout

The interesting white space is arguably the middle: taking an existing retailer catalog and continuously making every SKU semantically legible to multiple agents, rather than merely exporting a conventional Google/Meta feed.

That's why platforms like Catalog and ReFiBuy are worth watching alongside the much larger Shopify/Google ecosystems.

If you want, I can also give you a competitive landscape of 15–20 companies in this space, including funding, customers, pricing, APIs/MCP support, and which ones actually modify/enrich the underlying SKU data versus simply distribute it.

Yes. The market is starting to split into three layers: commerce platforms that natively expose catalogs to agents, protocols/infrastructure that make catalogs transactible, and newer catalog-enrichment/“AI visibility” vendors.

The main platforms to know

Shopify is currently the most vertically integrated example: its Catalog is explicitly designed to make products machine-readable, while its agentic storefronts connect that catalog to AI surfaces. Shopify says its Catalog continuously updates product information such as price and inventory.

An important distinction

There are really two different problems here:

1. “Can an AI understand and recommend my products?”
This requires clean structured catalog data: attributes, variants, prices, availability, identifiers, shipping/returns, reviews, use cases, etc.

2. “Can an AI actually buy my products?”
That requires an agent-facing commerce interface/protocol—catalog discovery plus cart, checkout, payment authorization, order status, returns, etc. UCP is increasingly positioning itself as the standard layer for this.

That's why I wouldn't put ordinary product-feed tools (Google Merchant Center, traditional PIMs, XML feeds) in exactly the same category. They're useful inputs, but they're not necessarily agent-native commerce infrastructure.

If you're evaluating the competitive landscape

I'd group the ecosystem like this:

• Commerce platforms: Shopify, WooCommerce, BigCommerce, Adobe Commerce
• Agentic commerce standards: UCP, MCP-based commerce interfaces
• Catalog/agent-readiness layer: Comergent and emerging AI-catalog optimization vendors
• AI shopping destinations: ChatGPT, Gemini/Google AI Mode, Microsoft Copilot, Perplexity
• Underlying catalog/PIM infrastructure: Akeneo, Salsify, Syndigo, Plytix, etc.

The interesting emerging category is “agentic catalog infrastructure”: a layer that takes a retailer's messy PIM/commerce data and turns it into a continuously updated, semantically rich catalog that agents can discover → evaluate → compare → purchase.

That category is still early. Shopify is moving fastest toward owning the whole stack, while UCP is trying to make the transaction layer interoperable across retailers and agents.

If you're asking because you're mapping startups/competitors in this space, I can also give you a 20–30 company landscape, categorized by catalog enrichment, AI visibility, agent protocols, checkout, and agent shopping, including which ones are actually live versus mostly marketing.

Making product catalogs readable, recommendable, and purchasable by AI shopping agents (ChatGPT, Perplexity, Gemini, Claude) relies on a shift from traditional HTML-heavy storefronts to machine-readable product pipelines, structured data feeds, and agentic protocols.

Several platforms and emerging categories help retailers bridge this gap:

• E-Commerce Giants with Native Agent Protocols (Shopify, Salesforce, WooCommerce)
  • Shopify integrates agentic storefront capabilities that syndicate merchant catalogs into AI channels via Model Context Protocol (MCP) and Universal Commerce Protocol (UCP) standards while keeping the merchant as the record holder.
  • Salesforce (Agentforce Commerce) unifies enterprise digital, POS, and inventory data, feeding structured attributes directly into its Atlas Reasoning Engine so agents can securely query and execute transactions.
  • WooCommerce utilizes dedicated MCP extensions and the WordPress Abilities API to expose raw product attributes and inventory fields to external AI systems.
• Agentic Visibility and Optimization Platforms (Ranketta, DeepLumen, Swap, Catalog.ai)
  • Ranketta tracks product-level visibility across various AI engines, enriches feeds via AI attribute proposals, and exposes catalog data directly through an MCP server.
  • DeepLumen auto-generates AI-readable "Agentic Pages" (mirrored, structured representations of a storefront) enabling external AI agents to precisely read, filter, and recommend inventory without altering the human-facing site.
  • Swap runs an Agentic Commerce Universal Catalog pipeline that transforms unstructured merchant content into clean semantic vectors and machine-parsable facts for AI buyers.
• Marketplace and Product Information Management (PIM) Enrichment (Mirakl, Syndigo)
  • Mirakl (Catalog Transformer) uses vision-based and text AI enrichment natively embedded in marketplace workflows to standardize disparate seller data, taxonomy, and attributes so large multi-vendor catalogs show up cleanly in AI searches.
  • Syndigo provides MDM-grade (Master Data Management) attribute depth and structured data hygiene, ensuring off-site AI agents receive unified product specs rather than fragmented marketing text.
• Checkout and Discovery Infrastructure (Stripe Agentic Commerce Suite / ACP Ecosystem)
  • Stripe provides the payment and checkout rails for the Agentic Commerce Protocol (ACP), letting merchants on platforms like BigCommere, WooCommerce, and Squarespace plug in standardized cart and transaction capabilities so AI agents can complete a purchase directly inside a chat window.

If you are looking to prepare a specific store, let me know what e-commerce platform you are currently using (e.g., Shopify, custom, Magento) so I can outline the best implementation path for your catalog.