How ReviewPond works
ReviewPond compares products on real review data and nothing else. There is no editorial scoring, no pay-to-rank, and no invented numbers. This page explains exactly how every figure on the site is produced.
Every number traces to a real fetched review
Each rating, review count, and theme on ReviewPond comes from a real row fetched from a named source (App Store, Steam, Product Hunt, TMDB). Nothing is seeded, estimated, or rounded up to look bigger. If we cannot fetch it, we do not show it.
Sources are shown whole, never blended
Steam measures the share of players who recommend a game. The App Store measures a mean out of five. TMDB measures a mean out of ten. These are different things in different units, so we never merge them into one invented composite. Each source is shown in its own units, side by side, attributed.
The AI only summarizes real reviews
When a product has real review text, a language model reads only that text and writes a short verdict with pros and cons. It is never asked to invent a score, a count, or a quote. The counts next to each theme are re-derived against the real reviews, not taken from the model. No review text means no verdict, and we say so.
We refresh on demand, and we date it
A product is re-fetched when someone actually looks at it, not on a blind nightly loop. Every page shows the date its data was last computed, so you can see how fresh the numbers are.
What we never do
- Invent or seed a rating, review count, or "X+ reviews" badge
- Blend different sources into a single made-up score
- Publish an aggregate star rating we did not earn from visible, attributed reviews
- Let a model write a verdict that the real reviews do not support
Why it is built this way
Most review sites inflate counts and blend scores because it looks authoritative. We think the opposite is the durable advantage: a number you can trace is worth more than a big number you cannot. The honest version is also the one search engines and readers can trust over time.