How it works

What breaks, what we fix, what we watch.

AI assistants are becoming the front door to shopping. When a buyer asks one for a product, it doesn't browse your store — it reads your data and recommends what it can confidently understand. If yours can't be read, it can't be recommended, and you never see the sale that went elsewhere.

Why it matters

The shift from searching and scrolling to asking an assistant is happening fast — across ChatGPT, Gemini and the rest at once. Two things follow. Visibility is now binary: the assistant returns a short list, often one pick, so you're either in the answer or you're invisible — there's no page two to climb. And the upside compounds: the stores that get readable get recommended again and again, which turns into visibility and sales you're currently leaving on the table. Large retailers were plugged into AI shopping automatically. This is the rare channel where specific, well-described inventory can out-rank a big budget — if the product data is right.

The scale is already real: traffic to U.S. retail sites from AI assistants grew more than 1,200% year over year in 2025 and kept climbing, roughly half of U.S. shoppers used AI to shop that year, and ChatGPT alone reaches 800M+ people weekly. — Adobe Analytics; PartnerCentric; OpenAI, 2025

What breaks

An AI assistant reads a store the way a machine does — not the way a shopper does. It fails silently when:

• Crawlers are blocked. If robots.txt turns away GPTBot or PerplexityBot, the assistant can't read a single product.
• There's no structured data. Without JSON-LD on product pages, a listing can't be parsed reliably.
• Identifiers are missing. No GTIN or part number means the assistant can't confirm what an item actually is.
• Attributes are thin. Size, material, compatibility — the fields assistants filter on — aren't there.
• Titles are vague. Missing the brand, model, or the key detail a buyer types.

What we fix

The enrichment engine completes identifiers where they're derivable, normalizes attributes to a per-vertical schema, rewrites titles and descriptions into the language buyers use, and publishes complete structured data. Two rules never bend: we never invent a fact, and every change is reversible per product or per batch.

What we watch

Surfaces drift. We re-scan on a cadence and track where you rank, who beat you, and what opened up — so the fixes hold as ChatGPT, Gemini and the rest change how they read catalogs.