Sample report
This is a curated illustration of a full report, annotated so you know what each part means. It is not a live audit. Figures below are fixed examples, not measurements of a real store.
Illustrated store: Maple and Main (fictional candle shop). Score: 64 / 100, Grade C.
What AI models say (illustrated)
2 of 3 buyer questions mentioned the store, 0 recommended it. One answer named "Glow Supply Co" instead. Annotation: this is the section that decides whether the score matters. Mentions without recommendations usually point at weak reviews.
Who AI recommends instead of you (illustrated)
Glow Supply Co scores 81 against this store's 64. Annotation: a side by side score is what makes the gap concrete. The merchant's next step was to compare review counts, not markup.
Fix these first (illustrated)
- highAI shoppers pick reviewed stores firstFew or no review signals
Fix: Install a reviews app and show star ratings on product pages.
Effort: About 1 hour, app install
- mediumAI shopping surfaces cannot list your productsNo product feed found
Fix: Install the Google and YouTube app on Shopify and connect Merchant Center.
Effort: About 30 minutes, app install
Annotation: each fix carries an effort label so a non technical merchant knows what to do first. The merchant did the reviews fix first because it was high severity and one hour.
Category breakdown (illustrated)
- Product schema depth: 70 / 100 (weight 30%)
- Bot accessibility: 100 / 100 (weight 20%)
- Policy completeness: 67 / 100 (weight 10%)
- Transactability signals: 50 / 100 (weight 10%)
- Basics: 100 / 100 (weight 15%)
- Reviews and authority: 25 / 100 (weight 15%)
Annotation: the lowest weighted category (reviews) dragged the result. That matches how assistants behave: they quote reviewed stores.
What the merchant did next
Week 1: installed a reviews app and asked past buyers for reviews. Week 2: connected the product feed to Merchant Center. Week 3: re-ran the audit. This sequence is typical, not a promise of a specific score change.