Your store's AI recommends items, answers sizing questions, and writes product copy. Ecommerce AI evaluation grades those outputs against your real catalog, so no customer buys based on a feature that does not exist.
We grade outputs. We grade your store AI against your real catalog and policies. We do not train models, run fine-tuning, or sell experts we cannot verify.
E-commerce AI evaluation is the grading of a store's AI outputs, product recommendations, shopper Q and A, generated product copy, against your real catalog data and store policies. Retail AI teams need it because the failure mode hits revenue directly: a shopper who buys on a wrong detail returns the item and remembers the store. Good looks like recommendations that exist in your catalog, specs that match the product page, and honest "not in stock" answers instead of invented alternatives. When the catalog data comes through retrieval, a RAG evaluation covers the retrieval side of the same problem.
Store AI fails at the moment of purchase intent. These are the patterns we grade for.
The AI describes a waterproof rating, a size option, or a compatibility that the product page never claimed. The shopper buys on that detail. We grade every product claim in the output against your actual catalog data.
Recommendation engines love to suggest the perfect item, which sometimes is not in your catalog, is discontinued, or is out of stock with no restock date. We check that every recommendation exists, is purchasable, and matches the shopper's stated need.
Asked about delivery times or return windows, the AI improvises instead of quoting policy. A promised two-day delivery that takes a week is a support ticket and a bad review. We grade policy answers against your real shipping, returns, and warranty terms.
AI-written product copy drifts toward superlatives and sometimes past the truth: "best in class" for a budget item, "all day battery" for six hours. We grade generated copy against the spec sheet, flagging every claim the specs do not support.
You send 20 to 50 AI outputs: recommendations, Q and A answers, generated copy, plus access to your catalog data and store policies, or we help you build a rubric from them.
Trained reviewers grade every sample against your catalog and policies, backed by automated checks. Invented product details are P0s.
Sample-level grades, where the catalog mismatches cluster, recommended prompt and data fixes, and a walkthrough call.
Every product claim in the output gets checked against your catalog data: specs, prices, availability, variants. When the AI says a jacket is waterproof and the spec sheet says water-resistant, that is a flagged mismatch with both texts shown side by side. The report reads like a diff between what the AI claimed and what you actually sell.
A recommendation is not just a product mention; it is your store telling a shopper what to buy. We grade whether the recommended item exists, is purchasable, fits the stated need, and is described accurately. A perfect-fit recommendation for a discontinued item fails, because the shopper cannot buy it. A great recommendation with wrong specs fails too, because the purchase happens on false premises.
We also grade how the AI handles the unknown. When a shopper asks about a product detail your catalog does not cover, the honest answer is to say so and offer an alternative path, not to improvise a confident answer. The sample set includes unanswerable questions on purpose, because improvisation at the moment of purchase is exactly what causes returns and bad reviews. A store AI that says it does not know earns more trust than one that guesses right nine times and wrong the tenth. The tenth guess is the one the customer remembers.
You give us access to the catalog data your AI should be using: a feed export, product pages, or API access to the same source the AI reads. Reviewers verify each product claim in the sample against that data. If your AI reads a stale feed, the grading will show it.
Yes. Recommendations get graded on three things: the item exists and is purchasable, it matches the shopper's stated need, and the description of it is accurate. A recommendation that nails the need but describes the wrong specs still loses points.
We grade those against the spec sheet: every claim in the copy must be supported by the specs. Overselling is the common failure, and it is exactly what causes returns. We flag each unsupported claim with the spec it contradicts.
No, as long as we grade against the catalog as it stood when the output was generated. We agree on a snapshot date per batch. If you want ongoing checks as the catalog moves, that is what continuous monitoring is for.
Yes, as a secondary check. Accuracy comes first: a friendly wrong answer is still wrong. But we also grade whether the tone fits your brand, especially in complaint and return conversations where the shopper is already unhappy.
A standard pilot uses 20 to 50 outputs covering your main flows: product questions, recommendations, policy questions, and generated copy. If you run seasonal catalogs, include samples from a category change so we can grade how the AI handles new inventory.
Send 20 to 50 outputs with your catalog. We will grade them all.
Not sure where to start? Talk to us and we will point you at the right evaluation.