Your Products Need to Be Found by AI Search, Not Just Google — Here's the Catalog Work That Gets You There
For two decades, getting found meant ranking on Google. In 2026, a growing share of product discovery happens somewhere else entirely: a shopper asks ChatGPT, Perplexity, or Google's AI Mode for a recommendation, and an AI decides which products to surface. Your website design, your clever copy, your ad budget — the AI ignores most of it. It reads your product data.
The website is becoming a secondary asset
This is the shift sellers underestimate. An AI recommending products can't admire your photography or your brand voice. It relies primarily on structured product data — identifiers like GTINs, category mappings, and attributes like material, dimensions, and compatibility — to match products to a shopper's query. Complete, structured data is what makes a product discoverable in the AI era; missing data makes it invisible, even when it's the better product.
There's an upside hidden in this. AI-driven discovery levels a field that paid ads and web design used to tilt toward big budgets. A smaller seller with better data can be surfaced ahead of a recognized brand with worse data. The catalog, not the marketing spend, becomes the competitive asset.
What AI search actually needs from your catalog
The requirements are strikingly consistent across platforms — Perplexity, ChatGPT, and Google AI Mode all want the same things. They want product identifiers filled in (GTINs and EANs, MPNs) so the AI can match your product confidently. They want every required and recommended attribute populated — category, material, dimensions, color (the exact name, "navy blue" not just "blue"), and compatibility. They want structured fields, not free text, so the engine knows that $89 is a price and 4.7 out of 5 is a rating, and that a dimension is a dimension. And they want fresh availability data, because stale "in stock" flags cause failed transactions and lost recommendations.
If that list looks familiar, it should: it's nearly identical to what gets a product published on a marketplace and recommended by Amazon's AI assistant. The same clean, structured, complete catalog wins in all three places.
One catalog, two destinations
This is the core idea behind how SYNAPS produces its output. From minimal input, it enriches your catalog into complete, normalized, validated listings — and those listings are built to be dual-output by design: ready to publish across marketplaces and readable by AI search engines and buying agents.
You don't run a marketplace-listing project and then a separate "AI optimization" project. The work is the same work. Get your product data complete, structured, and current once, and you're simultaneously clearing marketplace validation, satisfying Amazon's AI layer, and becoming visible to ChatGPT and Perplexity.
Where to start
Begin with your highest-revenue SKUs — the products people search for by specific attributes — and make their structured data complete and consistent. Then automate the long tail so the rest of the catalog catches up and stays current. The cost of waiting isn't a penalty notice; it's a competitor with cleaner data quietly getting recommended in your place.
Want your catalog ready for marketplaces and AI search at once? Book a 30-minute demo at cal.com/hugo-benloulou/30min.