Similar names are not enough.
Two listings can look similar while describing different sizes, packs, or variants. A wrong match makes the price comparison misleading.
Pricogni uses barcode, brand, size, and other product evidence to assess the relationship between listings.
Make uncertainty visible.
The matching pipeline separates confident results from uncertain ones. The review path preserves the evidence behind a proposed match.
This makes an AI decision inspectable. A reviewer can check the product details instead of accepting an unexplained score.
Evaluate against the actual queue.
I tested an AI judge against a real backlog of uncertain matches. The published account records the sample, cost, and limitations.
A later barcode check provided another way to assess the results. The decision to use the judge followed that evidence.
The case does not establish a general accuracy claim. It shows how a specific evaluation informed a product decision.
Keep the history useful.
A current price is only one observation. Pricogni keeps price history and records when observations become stale.
Matching quality and data freshness belong in the same workflow. Both affect whether a comparison deserves attention.