How to Make Your Product Catalog Visible to AI Agents

ChatGPT Shopping, Perplexity, Google Gemini, and Amazon Rufus are recommending products right now. This is not the future. It is the present.

And most e-commerce catalogs are simply not ready for it.

According to McKinsey, e-commerce companies lose up to 23% of revenue due to incomplete or inconsistent product data [1]. That number was already alarming before AI agents. Now, with Agentic Commerce, the impact multiplies: a product with poor data doesn't lose visibility on one channel. It loses on all of them. Simultaneously.

I wrote about this in a previous article: most brands still optimize their catalogs for human eyes. Beautiful banners, persuasive copy, attractive images. But AI agents don't "see" any of that. They process structured data, compare attributes, validate consistency, and decide based on what they can interpret.

No emotion. No second chance.

This article is a practical guide. I'll cover exactly what you need to fix in your catalog to be discovered, recommended, and chosen by AI agents — from ChatGPT Shopping to Amazon Rufus.

Agentic Commerce: why your catalog needs to change

Agentic Commerce is the model where autonomous AI agents research, compare, and purchase products on behalf of consumers. Unlike traditional e-commerce where humans browse product pages, agents process structured data to make purchasing decisions based on objective attributes [3].

OpenAI's documentation is direct: integration starts with a structured product feed [2]. That feed is what allows indexing products, understanding core attributes, and presenting correct information.

No structured feed, no visibility. Simple as that.

Brands with incomplete or poorly formatted data are systematically ignored by these agents — much like unoptimized websites lost visibility in the early days of Google. History repeats itself. Only faster.

The 5 problems that make your catalog invisible

After analyzing thousands of catalogs, I see the same patterns repeating. And they all lead to the same place: exclusion.

• •Missing or incomplete attributes: Dimensions, weight, materials, technical specifications. Every empty field is a reason for the agent to skip your product. 43% of listings contain critical errors of this type.
• •Generic or duplicated descriptions: Vague text that doesn't differentiate one product from another. The agent needs specificity to compare. If it doesn't find it, it moves on.
• •Cross-channel inconsistency: The same product with different information on Amazon, Shopify, and Mercado Livre. When the agent cross-references data and finds contradictions, it discards. No hesitation. Discards.
• •Confusing taxonomy: Incorrect or ambiguous categorization prevents the agent from finding your product when the consumer describes what they need.
• •Images without structured context: Beautiful photos without alt text, without metadata, without correspondence to product attributes. The agent doesn't "see" the image — it reads the data associated with it.

GEO and AEO: you know SEO. Now you need to know these two.

AEO (Answer Engine Optimization) is the practice of structuring content so that AI-powered answer engines — like Google AI Overviews, voice assistants, and featured snippets — can understand, reference, and recommend your brand. Unlike traditional SEO that focuses on ranking links, AEO focuses on making your content the direct answer [4].

GEO (Generative Engine Optimization) is a subset of AEO focused specifically on generative AI platforms like ChatGPT, Perplexity, and Google Gemini. These systems don't just retrieve links — they generate original responses that recommend specific products [4].

For e-commerce, what does this mean in practice? Structured product data, clear specifications, and authoritative content that AI systems can confidently cite.

SEO got you on the list. GEO gets you in the answer.

What each AI agent needs from your catalog

Each platform has specific requirements. But all share a common foundation: structured, consistent, and complete data.

The 4-step pipeline

After optimizing over 500,000 products, we arrived at a 4-step pipeline that covers the entire product content lifecycle. This isn't theory. It's what works.

• •Step 1 — Audit: Scan your entire catalog to identify missing data, generic descriptions, SEO and GEO gaps, and compliance issues. Each product receives a quality score from 0 to 100.
• •Step 2 — Fix: Correct factual errors, fill missing fields, and enrich descriptions automatically. Every data point is verified against real product specifications. Zero hallucination.
• •Step 3 — Optimize: Transform basic listings into conversion-optimized content. SEO-ready titles, GEO-ready descriptions, optimized images, and marketplace-specific formatting.
• •Step 4 — Distribute: Publish optimized content to all channels simultaneously. One source of truth, every channel updated.

Real results

Numbers matter more than promises:

• •Up to 40% increase in catalog team productivity
• •60% faster time-to-market for new products
• •Up to 23% increase in sales from improved data quality
• •Significant reduction in suppressed or rejected listings
• •Improved visibility in both traditional search and AI recommendations

And the advantage is cumulative. The more correct data circulates, the better the algorithm learns, the better the catalog performs. It's a compounding effect. Quiet, but powerful.

Spreadsheets, freelancers, and ChatGPT won't cut it

For operations with fewer than 100 SKUs on a single marketplace, manual processes may still work. Beyond that, complexity scales exponentially.

Each marketplace has different rules. Each product needs adapted content. Continuous maintenance is manually unfeasible.

Using ChatGPT directly to generate descriptions seems tempting. But it creates new problems: hallucinations in technical specifications, inconsistency between products, and no integration with sales channels. The result? More manual work, not less.

What works is an automated pipeline that audits, fixes, optimizes, and distributes — with real data verification and native integration with each channel.

Where to start

You don't need to change your entire catalog at once. The most efficient path:

• •Start with an audit to identify the most critical gaps
• •Prioritize categories with the greatest commercial impact
• •Implement improvements incrementally
• •Automate distribution to maintain cross-channel consistency
• •Monitor and iterate continuously based on real data

At GLOBALD, this is exactly the kind of problem that interests us: structuring catalogs for a commerce where visibility, discovery, and conversion increasingly depend on algorithms' ability to understand your products with precision.

Because in Agentic Commerce, content still matters. But content without structure becomes noise. And a catalog without consistency becomes invisibility.