Be the Brand AI Recommends: A Practical Guide for Merchants

Try this experiment: open ChatGPT or Gemini right now and ask it to recommend a product in your category. Not your brand by name, just the category. See if you show up.

A growing number of our clients have run this exact test on themselves and gotten an uncomfortable answer. Their competitor showed up. They didn't. Same price point, similar product quality, sometimes even worse reviews, and the AI still picked the other brand. That's not a fluke, and it's not something a bigger ad budget fixes. It's a data and structure problem, and it's fixable once you understand what's actually happening under the hood.

This is the practical version of that explanation: what makes an AI system recommend one product over another, what you can control, and what we'd actually do first if this were our own store.

Why Your Best Product Sometimes Loses to a Worse One

Traditional search ranks pages. AI recommendation works differently: the model has to actually understand your product well enough to compare it, explain it, and stand behind the suggestion. If it can't confidently answer “why this one,” it defaults to whatever product gave it the clearest, most complete answer, which is very often not the best product. It's the best-described one.

This is the uncomfortable truth behind most AI visibility gaps we find during an audit. It's rarely a quality problem. It's a clarity problem. A product page written for a human who already knows what they're looking for doesn't give an AI model the structured reasoning it needs to recommend that product to someone who doesn't.

What AI Models Actually Look For

When an AI system evaluates products to recommend, it's effectively trying to answer three questions: what is this, who is it for, and why this one over the alternatives. Getting recommended consistently means answering all three clearly and consistently everywhere your product data lives, not just on the page a human happens to land on.

In practice, that comes down to a handful of concrete things:

• Structured attributes, not just adjectives. “Waterproof up to 10 meters” gives a model something to compare. “Great for the outdoors” doesn't.
• Explicit use cases and constraints. “Fits kitchens under 150 square feet” is something an AI can match to a real question. A lifestyle photo is not.
• Consistency across sources. If your website, your Shopify Catalog listing, your marketplace listings, and your reviews describe the product differently, the model has conflicting signals and tends to trust none of them fully.
• Clean, current data. Stock status, pricing, and shipping windows that are actually accurate in real time. An AI shopping agent checks this before recommending, and a stale feed gets quietly skipped in favor of a competitor with a reliable one.

GEO and Agent-Readiness Are Two Different Jobs

We treat these as related but distinct workstreams, because they solve different problems.

Generative Engine Optimization, GEO, is about whether an AI system understands your brand and products well enough to describe and recommend them accurately when someone asks a general question in ChatGPT, Gemini, or an AI Overview. It's a content and structured-data problem. We go deeper on exactly what that involves in our GEO strategy guide.

Agent-readiness, tied to standards like Shopify and Google's Universal Commerce Protocol, is about whether an AI shopping agent can actually complete a transaction with you, checkout logic, live inventory, tax and shipping rules, without a human stepping in. That's an operations and systems problem more than a content one. We cover the mechanics of that in our breakdown of how UCP actually works.

Most merchants need both eventually. Very few need to solve both at once, and trying to usually means neither gets done well.

A Simple Way to Diagnose Where You Stand

Before investing in either workstream, run a basic version of the test we opened with, but structured. Pick your ten highest-revenue products. Ask three different AI tools, ChatGPT, Gemini, and Perplexity are a reasonable set, to recommend a product for the specific use case each one solves. Note whether you're mentioned, how accurately you're described when you are, and who beats you when you're not.

This takes about an hour and tells you more about your actual AI visibility gap than any generic checklist, because it's your real products against your real competitors, not a hypothetical.

What Good Looks Like Once You've Done the Work

Merchants who've genuinely closed this gap tend to see a few consistent things: their products get cited by name with accurate specifics rather than vague summaries, they show up in comparison-style answers (“X vs Y for small kitchens”) rather than only direct-name searches, and their agent-facing data, inventory, pricing, shipping, stays accurate enough that an AI agent doesn't have to hedge or skip them.

None of this happens from one optimization pass. It's closer to how technical SEO works: get the foundation right, then maintain it as your catalog and the AI systems evolve. The brands that treated GEO like a checkbox in early 2025 are, by our audits, mostly worse off now than the ones who treated it as an ongoing discipline.

Where to Start If This Feels Like a Lot

You don't need to fix your entire catalog at once. Start with the products that drive the most revenue and the most competitive pressure, the ones where losing an AI recommendation to a competitor actually costs you something. Rewrite those with explicit attributes, use cases, and constraints. Check that your inventory and pricing feeds are accurate and current, and validate your structured data against Google's Rich Results Test.

That's a two-to-three-week project for a focused team, not a quarter-long initiative, and it's usually where we start with new clients before scoping anything larger.

Frequently Asked Questions

Want to Know Exactly Where Your Catalog Stands?

Our AI Catalog & GEO Audit gives you a concrete visibility diagnosis, an attribute coverage map, and a prioritized 30/60/90-day roadmap, not a generic checklist. Book your AI visibility audit →