AI Review Analyzer That Flags Fake Ratings on Amazon
An AI layer on Amazon product pages that scores review authenticity and gives a straight buy-or-pass call
✳The problem they went after
Fake and inflated reviews run through Amazon's marketplace, and shoppers carry the cost. Seventy percent of users name unclear or suspicious reviews as their biggest shopping pain point, and they spend triple the time trying to verify authenticity by hand. What follows is decision paralysis, abandoned carts, and slow erosion of trust in the platform's seller ecosystem.
✳What they actually did
The AI Review Authenticity Analyzer sits above the existing reviews section on any product page. It combines rule-based checks with LLM analysis to spot suspicious patterns, filters for verified genuine reviews, then shows a Trust Score from 0 to 100, a synthesised pros and cons summary, and a buy or pass recommendation. Flagged reviews stay visible in their own section with AI-written explanations of why they look inauthentic. A working prototype was built in Lovable with sample data and live LLM calls.
✳What you can take from it
Worth stealing
- Trust Score 0-100 plus a plain buy-or-pass verdict
- Rule-based checks paired with LLM pattern detection
- Flagged reviews shown with reasons, not hidden
- Lovable prototype mirroring host design language, live LLM data
Read this if
You are designing an AI trust layer over user-generated content
The transferable bitThe feature borrows Amazon's own design language so there is no learning curve, and it shows flagged reviews with reasons instead of quietly hiding them, which leaves the judgement with the shopper. The roadmap earns its keep too: pilot on live review feeds, then a scaled ML model, then a seller Review Health Dashboard.
✳The submission itself
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