.png)
.png)
AI shopping is reshaping how consumers discover and interact with products online. From ChatGPT and Perplexity to Google’s AI Overviews and Shopify’s smart assistants, large language models (LLMs) are becoming active participants in the shopping journey. And what’s at the heart of this evolution? Product feeds.
The way you structure, enrich, and distribute product data is now a competitive advantage. So, let’s take a look at how AI shopping works today, and how you can optimize your product feeds for LLMs.
AI shopping refers to the emerging consumer behavior of using large language models (LLMs), AI-powered chat assistants, and agentic search tools to discover, evaluate, and purchase products. Rather than relying on keyword-based searches like “best budget running shoes” or “Bluetooth headphones under $200,” AI shopping allows people to type or speak to an AI the way they’d talk to a real-life sales associate. For example:
These aren’t just search queries. They’re natural language prompts that include specific needs, preferences, and intent. And AI systems, whether it’s ChatGPT, Perplexity, Meta AI, or a browser assistant like Arc Search, now have the ability to interpret that intent and serve highly relevant product suggestions.
At the core of AI shopping is structured product data. Instead of pulling from web pages or relying solely on SEO metadata, today’s LLMs ingest product feeds in a machine-readable format. These feeds contain attributes like:
When someone enters a natural language query, the AI interprets that query into semantic filters, then ranks and retrieves products from these data sources—sometimes across multiple stores or platforms simultaneously.
In other words, what shows up is no longer based on ad spend or who optimized for a keyword. It’s based on who has the cleanest, most complete, and best-mapped product feed.
This shift levels the playing field, and also raises the bar. A brand with a niche product but a perfectly structured feed can now show up alongside big-box retailers in AI-powered results. But if your product feed is messy, incomplete, or inconsistent, you might get skipped over entirely, even if you have the best product for the job.
It’s not just about how your product looks. It’s about how your product data reads to the AI. The structure, context, and clarity of your feed determines your visibility in AI-powered commerce experiences.
When you interact with a shopping assistant powered by a LLM, you’re not triggering a traditional search engine. You’re initiating a real-time product match process powered by structured data.
The LLM takes your natural language input, interprets the underlying shopping intent, and then surfaces recommendations by parsing structured product feeds from connected merchants. That feed becomes your storefront in an AI-first commerce environment.
Here’s why the quality of your product feed makes or breaks your visibility:
Unlike traditional search crawlers that try to infer meaning from page copy and link signals, LLMs prefer certainty. They don’t want to guess what your product is, who it’s for, or how it’s used. They want that information explicitly defined in clean metadata: titles, descriptions, tags, attributes, pricing, availability, and enriched content like materials or certifications.
If you don’t give the model clear input, you don’t get surfaced.
AI systems are trained to understand nuance. A generic title like “Performance Tee” might fall flat. But a product described as “Moisture-wicking recycled polyester tee for trail runners in hot climates” has multiple points of semantic connection: activity type, material, weather relevance, sustainability angle.
LLMs use those details to match products with highly specific prompts like: “What’s the best eco-friendly running shirt for 90-degree weather?”
If your product feed lacks that richness, your listings simply won’t be chosen, even if you sell the ideal product.
In AI shopping, there’s no second page of results. If your product isn’t structured clearly, or if it lacks key details, the model doesn’t “rank it lower,” it skips it entirely.
It’s binary: you’re either matched to the intent, or you’re invisible.
This means missing or inconsistent data (e.g., no gender for apparel, no dimensions for furniture, no shipping speed for perishables) can quietly disqualify you from entire categories of queries. You may not even realize you’re losing traffic, because the traffic never arrives in the first place.
Let’s get into the meat and potatoes—because if you’re here, it’s probably not to hear another high-level trend piece. You want to know how to actually make your product feed work for AI.
Here’s the truth: optimizing for AI shopping isn’t about keyword stuffing, SEO gimmicks, or reordering your titles to hit character limits. It’s about feeding structured, unambiguous, and context-rich data to the systems that power tools like ChatGPT, Perplexity, Google Gemini, and Meta AI.
These models don’t crawl and guess like a search engine. They ingest and match based on what your feed tells them. The cleaner, richer, and more clearly structured that data is, the more often your products will show up in relevant AI shopping experiences.
LLMs need clear product identifiers that pack in useful context. Your titles should include the brand name, product type, and key differentiators, such as material, audience, or purpose.
Descriptions should reinforce this with intent-aware language. Think scannable, human-readable sentences that still expose machine-parsable attributes: “This lightweight shoe is designed for casual runners looking for breathable, eco-friendly comfort in warmer climates.”
Surface-level attributes like price, size, and color are no longer enough. To match natural language queries, your feed needs deeper context. That means tagging products with:
These attributes give AI systems the semantic hooks they need to match conversational prompts with high precision.
Your internal product categories should map to familiar shopping hierarchies. LLMs often use ontologies derived from sources like Google’s product taxonomy or Shopify’s Catalog API structure to understand product relationships.
If you’re using overly niche or brand-internal category names (e.g., “FlowCore Techwear”), add standard mappings so the system knows it falls under “Men’s Athletic Outerwear” or “Breathable Jackets.”
Multimodal models (like GPT-4o and Gemini) don’t just “read” text—they can analyze image data, too. That means image quality matters, and so does how you describe it.
Every image should include alt text that combines product name, type, color, and key features:
This helps with accessibility, search, and multimodal model interpretation all at once.
AI shopping thrives on immediacy. If your product shows up in a recommendation but is sold out or mispriced, that consumer journey ends before it begins.
This is why real-time or near-real-time syncing matters. Platforms like Shopify’s new Catalog API are designed for this exact use case—allowing trusted partners like ChatGPT or Perplexity to access live inventory, pricing, and metadata directly from merchant feeds.
The more up-to-date your feed, the more trusted and performant your brand becomes in AI-driven experiences.
AI shopping is already happening across the platforms your customers use every day. If you’ve asked ChatGPT for a product recommendation or seen an AI-generated product list on Google, you’ve experienced it firsthand.
OpenAI’s ChatGPT, especially with the browser tool or plugin ecosystem enabled, has quickly become an AI-powered shopping assistant. Users can type natural language prompts like:
“Find me a minimalist backpack for under $150 that fits a 16-inch laptop”
…and get actual product recommendations pulled from live data sources. With Shopify integrations, ChatGPT can deliver direct product links, show updated pricing and availability, and even surface curated collections. This isn’t a chatbot novelty—it’s a new shopping front-end powered by product feeds.
Perplexity has emerged as one of the most advanced LLM-native search tools—and it’s already using Shopify’s new Catalog API. That means it can ingest structured product data (title, price, stock, attributes) in real time and incorporate it directly into its answers.
When a user searches something like:
“What are the best everyday sneakers under $100?”
Perplexity might respond with a short list of live product listings—including links, product descriptions, and merchant data—all sourced directly from product feeds, not scraped sites or affiliate blogs.
Google’s AI Overviews (part of the Search Generative Experience, or SGE) are slowly rolling out to more users. These overviews are already pulling in shopping content—especially when the query has commercial intent.
Example:
“Best office chairs for back pain under $300”
SGE may return an AI-generated summary followed by structured product listings pulled from Shopping Graph data. If your feed is plugged into Google Merchant Center with clean GTINs, taxonomy, and rich attributes, you’ve got a shot at appearing in that generative layer.
Amazon’s AI shopping assistant, Rufus, launched in early 2024 and is built natively into the app experience. When users ask questions like:
“What’s a good vacuum for pet hair on hardwood floors?”
Rufus parses the query, taps into Amazon’s internal product feed infrastructure, and recommends items based on attributes like surface type, noise level, and customer reviews. Amazon’s advantage? They’ve already been structuring product data at scale for over a decade, so their LLMs have rich inputs to work with.
For years, visibility in commerce was all about building the right pages—product pages, landing pages, SEO-optimized collections. The goal was to rank on Page 1 and win the click.
In the AI era, that’s shifting. Visibility is no longer about who ranks—it’s about who feeds.
LLMs don’t crawl pages the way search engines do. They ingest structured data. That means you’re not just competing for keywords anymore—you’re competing to be the cleanest, clearest, and most context-rich product in the feed.
Brands that treat product data as a strategic asset—not a backend task—will win the AI shelf space.
It also changes how creative gets built. Platforms like Marpipe help brands generate a range of catalog ad variations and connect creative elements to feed data. The result? Ads that are more adaptable, more relevant to specific placements, and more likely to appear in AI-driven discovery surfaces across Meta, Google, and beyond.
Search is becoming more conversational, more contextual, and more visual. And the language AI understands best? Structured product data. If your feed is clean, complete, and context-rich, you’re in the mix. If it’s messy or missing key details, you may already be invisible.
Now’s the time to revisit your taxonomy, tighten your metadata, and connect performance creative to a smarter feed. Because in the world of AI shopping, your feed is your storefront, and the front door is wide open.
