Reviews help, but not in the simple more-is-better way people assume. To an AI assistant, reviews are a corroboration signal — evidence that you are a real, trusted business and a source of concrete, quotable detail about what you are good at. That means three things matter more than raw count. Quality and specificity: reviews that mention the actual service, outcome and location give the model language it can reuse (on-time roof repair after a storm is far more useful than great service). Recency and steadiness: a consistent flow of recent reviews signals an active, current business, while a pile of old reviews suggests a stale one. And distribution: reviews across the platforms models read, tied to a consistent business name and location, reinforce your identity rather than fragmenting it. A huge review count on one platform, full of generic praise, does less for AI visibility than a steady stream of specific, recent reviews across a few trusted sources. Reviews also cannot rescue a site an assistant cannot read in the first place — they are one leg of the stool, powerful alongside readable content and consistent listings, not a substitute for them.
Most businesses score under 40/100 and have no idea. Find your score in 15 seconds, then fix it.
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