You enriched your catalog.
Now find out if it's working.
Ranker scores how your catalog actually performs inside ChatGPT, Perplexity, and Google AI — retrieval, match confidence, recommendation accuracy.
The buyer already moved.
Your catalog measurement hasn't.
More product discovery now happens inside AI agents than on a results page — a buyer asks, gets three products, and there's no page two. The catalog that wins is the one with the structured data the model can answer from. Ranker measures whether yours does.
Six signals across every
AI shopping surface
Ranker doesn't grade your content marketing. It measures whether AI agents can find your product, match it to a query, and recommend it with confidence.
AI Retrieval Score
- How often does your SKU appear when an AI shopping agent queries your category?
- Score before and after enrichment to see the direct lift from Catalog Agents.
Match Confidence
- When an agent tries to match a buyer's query to your product, how confident is the match?
- Low confidence means the agent skips you — or recommends a competitor instead.
Recommendation Accuracy
- Does the AI agent recommend your product for the right query?
- Ranker surfaces attribute gaps that cause mismatches and wrong placements.
Comparison Readiness
- AI agents compare products side-by-side. Incomplete attributes lose the comparison.
- Not because the product is worse — because the data is thinner.
Answer Inclusion Rate
- For ChatGPT and Perplexity, track how often your products appear in AI-generated answers.
- The products with complete structured data dominate these surfaces.
Enrichment Impact Score
- Which Catalog Agent enrichment runs moved your Ranker score?
- Close the feedback loop so the next enrichment cycle targets the right attributes.
Every major AI shopping surface
Ranker tracks your catalog performance across the AI discovery surfaces where buyers are going — with more coming as the landscape evolves.
Enrichment that knows
if it worked.
Without Ranker, catalog enrichment is a one-way process: you improve the data, hope it performs better, and find out three months later in a quarterly review. With Ranker, every enrichment cycle gets scored.
If Catalog Agents added a missing multipack signal and your Ranker score for that SKU went up in Amazon Rufus, you know. The next cycle targets the next gap. Every enrichment run is more targeted than the last.
Score your catalog in
AI shopping agents.
Find out how your Paladio-enriched products perform in ChatGPT, Perplexity, and Google AI — and which attribute gaps to close next.