A hands-on look at AnswerEng
Meet Cascade Supply, our example outdoor store. Its wide-fit hiking boots aren’t showing up in a set of AI answers. Let’s find a detail worth improving.
Watch the 42-second walkthroughNo signup. No changes to a real store.
01 / See the picture
How often AI recommends this store’s products when we ask shopping questions.
168 of 400 sampled answers
recommended this store.
AI can answer the same question differently. We repeat questions and show a 95% Wilson confidence interval to express uncertainty around each measured rate. This is a sample of answers, not a prediction of sales.
Recommendation frequency for each example brand.
One answer can recommend several brands, so rates don’t add to 100%.
The same store, measured independently.
Ranges show the 95% confidence interval for each rate.
02 / Follow the evidence
Look at the answer, then compare it with the information in your catalog.
“What are the best waterproof hiking boots for wide feet?”
“For wet trails and a wider fit, consider the Northwind Trail Wide. Its product description explicitly lists a wide fit and waterproof construction. Check the size guide before choosing your pair.”
Cascade Ridge Mid isn’t in this answer.
This is one fictional excerpt from the example sample set.
“A dependable boot for long trail days. Built for comfort, season after season.”
The buyer asked about fit and waterproofing. Both are in the catalog, but neither is explicit in the description. Making them clearer is a change worth testing; it doesn’t guarantee a recommendation.
Next: review a draft grounded in those existing product facts.
03 / You make the call
Compare the copy, check the product facts, and try reviewing a suggestion.
Walkthrough complete
You approved an example draft. Nothing was published or sent to a store.
In a real workflow, you review and apply changes, then use later measurements to look for a trend. The demo score stays the same.