Clean the source
Rewrite keyword-stuffed titles, fill missing attributes, and score completeness product by product.
Agents pick one answer.
AI agents don't browse a category page. They decide what to recommend.
Clean, structured product data makes your catalog legible enough to be the answer they choose.

Get the data wrong and you're not competing for visibility. You're invisible.
Agentic discovery starts before the feed. The catalog has to be clean, complete, and structured enough for an agent to understand it without guessing.
Rewrite keyword-stuffed titles, fill missing attributes, and score completeness product by product.
Turn one consistent catalog into records agents can retrieve, compare, and trust.
Generate the protocol surfaces agents already use instead of maintaining disconnected exports.
catalogGPT publishes the same enriched catalog to every protocol that matters. There are no separate exports to maintain and no mismatch between the data an agent sees and the data on your site.
Open the diagram to inspect each output.
Universal Commerce Protocol — real-time catalog search and lookup for agents
Agentic Commerce Protocol — a JSONL stream for agent shopping
A full Schema.org graph for direct LLM consumption
A Perplexity Merchant Program JSON envelope
Per-product JSON-LD injected through a theme extension
A storefront-served file for AI crawler discovery
Every product receives a readiness score, not a catalog-wide average. See which listings agents can represent well today and which missing fields keep the rest from being recommended.
Explore AI visibility analyticsAnswer 3 quick questions and see how well an AI agent would represent your store based on your current catalog data.
1. What platform are you on?
2. How many SKUs do you have?
3. How clean is your product data?
Answer all 3 questions above to see your score.
Make your catalog clean, readable, and ready to be recommended wherever shoppers ask.
Get Started →