A few months ago, Sephora launched a custom experience inside ChatGPT. Customers could describe their skin type, ask about ingredients, get personalized product recommendations, and make purchase decisions — all through a conversation. No browsing. No filtering. No ten-tab research sessions.

It worked. And it revealed something most ecommerce leaders had been wondering about: what happens when your product catalog actually lives inside AI?

The answer is straightforward. Customers shop differently. They shop better. And the brands that show up in that conversation win.

What Is a ChatGPT App?

A ChatGPT app — sometimes called a GPT or a plugin — is a custom experience that lives inside ChatGPT. For ecommerce, it means connecting your product catalog so that when a customer asks ChatGPT for a recommendation, the answer comes from your data.

This isn't a chatbot on your website. It's your brand, your products, and your merchandising expertise, embedded directly inside the world's most popular AI assistant.

When someone asks ChatGPT "What's a good moisturizer for dry skin under $40?", a brand with a ChatGPT app doesn't just hope to get mentioned. Their catalog is the source of the answer.

What Is MCP and Why Should You Care?

MCP stands for Model Context Protocol. It's an open standard that lets AI applications connect to external data sources and tools. Think of it as a universal adapter between your product data and AI platforms.

Here's why it matters for ecommerce: MCP isn't limited to ChatGPT. If you build your product catalog integration on MCP, your data becomes accessible to Claude, to future AI assistants from Google and Meta, and to any platform that adopts the protocol.

Building on MCP means you're not locked into a single AI platform. You're building for the entire AI shopping ecosystem.

What Sephora Got Right

Sephora's ChatGPT integration wasn't a gimmick. It was a strategic move that understood three things about where shopping is heading.

Customers Want Advice, Not Catalogs

Traditional ecommerce gives customers a search bar and a grid of products. Customers are expected to know what they're looking for, filter by attributes they may not understand, and compare options themselves.

Conversational shopping flips this. Customers describe a problem ("My skin is oily but I don't want a matte foundation") and get a curated recommendation. The AI handles the filtering, the comparison, and the selection. The customer just has to decide.

Product Data Is the Moat

Sephora has deep product data. Ingredients, skin type compatibility, application techniques, shade ranges, customer reviews. That data is what makes the ChatGPT experience useful.

Without rich, structured product data, a ChatGPT app is just a chatbot. With it, the app becomes a knowledgeable sales associate who's memorized every product in the store.

Early Movers Define the Category

When Sephora launched inside ChatGPT, they didn't just create a new sales channel. They set the expectation for how beauty shopping works in AI. Every beauty brand that follows will be compared to what Sephora built.

The same will be true in every product category. The first outdoor gear brand, the first luxury fashion house, the first home goods retailer to launch a ChatGPT app will define how those categories work inside AI.

Why Your PIM Is the Foundation

Your Product Information Management system — Shopify, Salesforce Commerce Cloud, Akeneo, Salsify, or whatever you use — is the foundation of a ChatGPT or MCP app.

Every attribute, every variant, every product description, every piece of inventory data in your PIM becomes queryable by AI. When a customer asks about sizing, compatibility, materials, or availability, the answer comes directly from your PIM.

This is why data quality matters more than ever. AI doesn't forgive incomplete product data the way a website might. If a customer asks about ingredient sourcing and your product data doesn't include that information, the AI can't fabricate an answer. It moves on to a competitor who has that data.

What Good PIM-to-AI Integration Looks Like

The connection between your PIM and AI platforms should be:

Real-time. When you update pricing or inventory, the AI app reflects those changes immediately. No batch updates, no stale data, no embarrassing "sorry, that's out of stock" moments.

Complete. Every SKU, every variant, every attribute. The AI should have access to your entire catalog, not a curated subset.

Normalized. AI models work best with clean, consistent, well-structured data. If your PIM has messy attributes or inconsistent naming, the integration layer needs to fix that.

Brand-aware. Your ChatGPT app should reflect your brand voice, your merchandising rules, and your expertise. A luxury fashion brand's app shouldn't sound like a discount retailer's.

The MCP Advantage

Building on the Model Context Protocol isn't just a technical choice. It's a business decision about reach and portability.

One Integration, Every AI Platform

MCP is an open standard. When you build an MCP-compatible product catalog integration, you get access to ChatGPT, Claude, and every future AI platform that supports the protocol. You're not building separate integrations for each platform.

For brands with limited engineering resources — which is most ecommerce brands — this is a significant advantage. Build once, deploy everywhere.

Future-Proof Architecture

Nobody knows which AI platforms will dominate in two years. ChatGPT is the leader today, but Claude is growing fast. Google's AI assistant is improving. Meta AI is reaching billions of users through WhatsApp and Instagram.

MCP gives you a standard interface that works across all of them. When a new AI shopping platform launches, your catalog is ready.

Composability

MCP apps aren't monolithic. They're modular tools that AI can combine. Your product catalog MCP app can work alongside a reviews app, a sizing app, or a styling app. The AI orchestrates these tools to deliver a complete shopping experience.

What This Means for Different Types of Brands

DTC Brands

For direct-to-consumer brands, a ChatGPT app is a new customer acquisition channel that doesn't require paid media spend. Customers who discover your products through AI conversation are high-intent and well-informed. They've already been qualified by the conversation.

The data advantage matters here too. DTC brands often have richer product data than marketplace sellers because they control the entire product narrative. That translates directly into better AI shopping experiences.

Enterprise and Multi-Brand Retailers

For larger retailers with hundreds or thousands of SKUs, a ChatGPT app can be transformative for product discovery. Customers who would never navigate a 10,000-SKU catalog on a website can describe what they need in natural language and get precise recommendations.

The PIM integration is more complex at this scale, but the payoff is proportionally larger. Every SKU in your PIM becomes discoverable through conversation.

Marketplace Sellers

If you sell on Amazon, Walmart, or other marketplaces, you already know the importance of product data optimization. A ChatGPT app gives you a direct relationship with customers that marketplaces don't. When a customer discovers your products through your branded ChatGPT app, that's your relationship, not Amazon's.

The Practical Path to Launching

You don't need a massive engineering team to launch a ChatGPT or MCP app. Here's what the process actually looks like.

Step 1: Connect Your PIM

The starting point is connecting your product data source. Whether it's Shopify, SFCC, Akeneo, Salsify, or a custom system, the integration pulls your catalog into a format that AI can query effectively.

This isn't a one-time export. It's a live connection that stays in sync with your PIM. When you launch a new product or update pricing, the AI app reflects that automatically.

Step 2: Configure the Experience

This is where merchandising meets AI. You define how the app represents your brand, which products to emphasize, how to handle questions about categories you're strong in, and how to deal with edge cases.

Think of it as training a knowledgeable sales associate — except this one can handle thousands of conversations simultaneously.

Step 3: Deploy Across AI Platforms

Once the app is built, it launches inside ChatGPT and across MCP-supported AI platforms. Customers can immediately start discovering and interacting with your products through conversation.

The ongoing work is minimal. Your PIM stays in sync automatically. You can adjust merchandising rules, update brand voice guidelines, and add new product categories as your catalog evolves.

The Window Is Open

AI shopping is new. Most brands haven't launched inside ChatGPT. Most haven't heard of MCP. The brands that move now will define how their category works inside AI — the same way early SEO adopters dominated Google's first page.

Sephora showed what's possible. The question isn't whether other brands will follow. The question is whether your brand will be the one that defines your category — or the one that catches up later.

The customers are already there. Millions of people use ChatGPT every day to research products and make purchase decisions. Right now, most of those conversations don't include your catalog.

That's the opportunity. And it's available to any brand willing to connect their products to AI.

Wildcard helps ecommerce brands launch custom ChatGPT and MCP apps. We connect directly to your PIM, handle any custom integrations, and get your products discoverable inside AI. Book a demo to get started.