Millions of merchants. Billions of products.
Agents pick one answer.

AI agents don't browse — they pick. The only way to be the answer they pick is data clean and structured enough for an agent to trust, published everywhere agents look.

Get that wrong and you're not competing for visibility. You're invisible.

An AI answer agent finding the one matching product in a massive pile of similar catalog items — the needle in the agentic haystack

Agentic Discovery

Get found where shoppers are already asking.

Clean, enriched, scored product data — distributed as six agentic feeds AI agents actually read.

Enrichment, cleaning & scoring

Feeds are only as good as the data behind them. cleanerGPT rewrites keyword-stuffed titles, fills missing attributes, and scores completeness product-by-product — catalogGPT then uses that same scoring to flag exactly which products AI agents can't understand yet, before it ever reaches a feed.

Six agentic feeds, one catalog

catalogGPT publishes the same clean catalog to every protocol that matters — no separate exports to maintain, no feeds drifting out of sync with each other.

UCP catalog

Universal Commerce Protocol — real-time catalog search & lookup for agents

ACP feed

Agentic Commerce Protocol — JSONL stream for OpenAI / agent shopping

JSON-LD @graph

Full Schema.org @graph document for direct LLM consumption

Perplexity feed

Perplexity Merchant Program JSON envelope

Storefront JSON-LD

Per-product JSON-LD injected via theme extension

llms.txt

Storefront-served llms.txt for AI crawler discovery

Visibility scoring

Every product gets a readiness score — not a catalog-wide average — so you know exactly which listings AI agents can represent well today, and which fields are holding the rest back.