How we review
Useful evidence before confident claims.
Our goal is not to crown a universal winner. It is to help readers understand fit, tradeoffs, and the next question to ask.
01. Start with the job
We evaluate a tool against the work it claims to help with. A coding agent and an academic search engine need different standards, even when both use AI.
02. Check primary sources
We use official product pages, help centers, documentation, pricing pages, and policies to establish supported features and limits. We avoid repeating unsupported aggregator claims.
03. Write a decision-oriented note
Each entry answers four questions: What is it? Who is it for? What does it do well? What should a buyer understand before committing?
04. Timestamp every review
AI products change quickly. Every field note shows a verification date and sends readers to the official product site for current pricing and terms.
05. Correct transparently
Material corrections update the field note and its verification date. Vendors can point out factual errors, but they cannot approve, purchase, or suppress our verdict.