Would you read 1,000 reviews to save $80? We would.

Review Intelligence reads every review a product has, clusters what people actually say, and returns a one-line answer: buy, wait, or skip. Here's how.

The trick: Star ratings lie by omission. A 4.6★ product with 900 reviews about the wrong problem is worse than a 4.1★ product with 200 reviews about your problem. Volume + sentiment aren't the same as fit.

The 3-step flow

1

Paste the product URL

Amazon, Shopify, DTC — any product page with reviews. Pick your depth (fast vs deep, 1.5 years back).

2

We fan out across sources

The store's own reviews, third-party review sites, YouTube unboxings, Reddit threads, and social mentions. Deduped and clustered by theme.

3

You get the verdict

Top praises, top complaints, sentiment trend over time, verified discount codes, top-3 alternatives side by side, and a plain-English recommendation.

What "should you buy?" actually looks like

BUY — high fit, few red flags
WAIT — good, but a deal is coming
SKIP — better alternative exists

The signal we extract

Store reviews Social mentions 3rd-party sites Cluster + dedup Top complaints Buy verdict Top 3 alternatives

Common misconceptions

Myth: "Average rating is enough."
Reality: A 4.6★ average hides the 12% of buyers saying "battery died in 2 months." That's the review you need to read.
Myth: "The brand's own reviews are enough."
Reality: ~30% of DTC reviews are gated. Third-party and social sources are where the honest signal lives.
Myth: "AI just summarizes."
Reality: Summary without clustering flattens signal. We cluster by complaint type first, then summarize each cluster. The 12% who said "battery" show up as a bucket, not a footnote.

The takeaway

Star ratings answer "did people like it?" The real question is "will you like it?" Review Intelligence gets you to that answer in five minutes instead of two hours of tab-hopping.

Analyze a product now