Choosely / Trust
Corrections
AI changes quickly. We correct material mistakes, identify substantive updates and preserve dated reporting as a record of its time.
Report an error
Send factual corrections or relevant primary-source updates to info@choosely.ai. Include the page URL, the specific passage, what you believe is wrong or has changed, and the source that supports your note. Readers, researchers and companies are all welcome to submit evidence.
We evaluate the claim and its evidence. A source-page change is a review signal, not automatic proof that the original article was wrong.
How we respond
Material factual corrections are made transparently and proportionately to their effect on the reader's understanding or decision. When appropriate, the article will explain what changed. Better evidence may also require a changed conclusion.
A product changing after publication is different from an error in the original reporting. Dated coverage may remain as historical record, receive an update, or point to newer work. We do not silently rewrite historical reporting to make it look current.
What the labels mean
Correction
A material factual statement was wrong or unsupported when published. We fix the claim and make the correction visible where appropriate.
Update
New facts or product changes emerged after publication. We add or revise information so the page remains useful, without implying that the original reporting was necessarily wrong.
Superseded
Newer coverage has become the current decision surface. An older page may remain available for historical value and direct readers to the newer work.
Citation repair
A supporting link breaks, moves or needs a more precise reference. We repair the citation and check whether the underlying claim still has adequate support. A repaired link alone is not a new verification of every claim in the article.
Evidence and accountability
Claims of hands-on testing should be backed by actual use and documentation. If such a claim cannot be substantiated, it should be corrected. The same standard applies when a comparison or recommendation depends on a material fact that no longer holds.
Our AI Radar lifecycle distinguishes CURRENT and EVERGREEN coverage on the active truth surface from HISTORICAL and SUPERSEDED records. Ongoing review helps identify what needs attention. It does not mean every archived claim is perpetually current. Read more in our Editorial Standards.