We fixed reviews
Normalized review scores for things you love
Rankquant is better: we aggregate a product's rating based on each users relative score.
See our ratings
The process
Four deterministic steps from review to rank
Per-reviewer z-score
Subtract the reviewer's personal mean, divide by their personal stddev. Every reviewer now speaks the same dimensionless language.
Three lenses
R1 unweighted (headline). R2 source-weighted by credibility. R3 broadens to constant-rater reviewers via imputed σ.
90% CI floor
Ẑ − 1.645·SE penalizes thin samples. A 4-reviewer +2.1 mean loses to an 80-reviewer +1.6 mean.
ECDF percentile
Rank floor against the universe → 0–100. Cohort percentile re-ranks within same-category, ±20%-price peers.
Why us
What we do that Google and ChatGPT can't
Search ranks by links and click behavior. We rank by what bias-corrected reviewers actually thought. The most-clicked wine isn't the highest-quality wine.
AI summarizes whatever it scraped — usually flat averages or vibes. Our methodology is published, deterministic, and reproducible. Every score has a paper trail.
Native platforms have rating clusters: 4.5 means nothing when every product gets a 4.5. We strip the cluster effect by re-centering each reviewer on their own scale.