Brands read reviews for customer service. AI engines read them as evidence. The gap between what you claim and what customers say is the finding. Brands read reviews to handle complaints. Engines read them as evidence about whether your product is good for a specific use case — in language customers chose, about problems you may never have named.
4 min read
We pull verified reviews from the open web — Reddit, Google, wherever customers actually talk — not only from the review platform a brand happens to have connected. That distinction matters more than it sounds. The reviews inside your own reviews app are the ones you have already seen. The ones shaping what a model believes are usually somewhere else.
This is our own build, and pulling from the open web is the reason it exists.
The average rating is close to useless. A 4.6 tells you nothing about what a model is learning. The gap is what matters: the distance between what a brand says about itself and what customers consistently say back.
A brand marketing on durability whose reviews all praise the fit has a positioning problem and a citation problem at the same time — and the second one is invisible until someone looks.
Rewrite product copy in the customers' own language, because that is the language the queries are phrased in
Feed real use cases into the content plan — usually the best article ideas in the account
Flag consistent negatives early, before they harden into the default thing engines say about you
The pull is built and running. How the gaps get categorised after the pull is still in build, and it may well stay partly manual — some of that judgement does not automate well, and we would rather say so than publish a method we have not finished.
That is the direction of everything we make: do the work well by hand first, then build the instrument.
Your free AI Perception Report includes what your reviews are teaching AI about you.
