{"id":12042,"date":"2026-08-10T14:59:14","date_gmt":"2026-08-10T12:59:14","guid":{"rendered":"https:\/\/highstreet.io\/?p=12042"},"modified":"2026-08-10T15:15:30","modified_gmt":"2026-08-10T13:15:30","slug":"product-catalog-ai-assistants","status":"publish","type":"post","link":"https:\/\/highstreet.io\/en\/catalogo-prodotti-assistenti-ai\/","title":{"rendered":"How to make your product catalog discoverable by AI assistants (ChatGPT, Gemini)"},"content":{"rendered":"

AI assistants are becoming a place where people shop. A customer asks ChatGPT or Google's AI Mode to find a product, and the assistant returns real options \u2014 with prices, availability and a way to buy. The question every merchant now faces is simple: when an assistant looks for products like yours, does it find them?<\/strong><\/p>\n

The answer comes down to your product catalog. This is a practical guide to making it discoverable \u2014 and choosable \u2014 by AI assistants.<\/p>\n

Why AI assistants can (or can't) see your products<\/h2>\n

An AI shopping assistant doesn't crawl your website the way a shopper browses it. It reads a structured product feed<\/strong> \u2014 a machine-readable file describing each product \u2014 supplied to the platforms the assistant draws from. If that feed is missing, incomplete or stale, your products are effectively invisible to the assistant, no matter how strong your brand or how good the product.<\/p>\n

This is a bigger gap than most merchants realise. PayPal's Agentic Commerce Pulse Report (498 US merchants, February\u2013March 2026) found that nearly all merchants report at least partial visibility into AI-agent traffic, yet only about one in five have 80% or more of their catalog available as structured, machine-readable data. The traffic is arriving; the readiness usually isn't.<\/p>\n

The reassuring part: the requirement is not exotic. It's the same structured, high-quality feed that already powers Google Shopping, Performance Max and Meta \u2014 applied to a new set of consumers.<\/p>\n

The four things an AI assistant needs from your catalog<\/h2>\n

1. Descriptive, natural-language titles<\/h3>\n

Assistants match products to conversational requests like \"a waterproof jacket, size M, under \u20ac150.\" A title built for an internal SKU system (\"JKT-AW24-BLU-M\") gives the assistant nothing to match. A title that states product type, key attributes and defining features (\"Men's waterproof jacket, navy, size M, breathable\") maps directly onto how people ask. Write titles for the question, not for the warehouse.<\/p>\n

2. Complete, structured attributes<\/h3>\n

Colour, size, material, category, GTIN, brand \u2014 the more complete the structured data, the more confidently an assistant can match your product to a specific need and present it accurately. Missing attributes don't just lower relevance; they make the assistant less willing to surface an item it can't fully describe.<\/p>\n

3. Compliant, high-quality images<\/h3>\n

Visuals are part of how the assistant presents options to the shopper. Images need to meet the platform's requirements and show the product clearly. A poor or non-compliant image can get a product filtered out before it's ever shown.<\/p>\n

4. Accurate, fresh price and availability<\/h3>\n

An assistant that surfaces a product which is out of stock or wrongly priced erodes trust \u2014 in your catalog and in the assistant. Frequent synchronization of price and availability is what keeps you eligible to be recommended. Stale data is the fastest way to be dropped.<\/p>\n

Where the catalog goes: the surfaces and how they connect<\/h2>\n

Different assistants connect to catalogs through different routes, but the input is always the same structured feed:<\/p>\n