Check a claim before it travels.
FactSentinel helps reviewers inspect a specific claim with source links, confidence, reasoning, caveats, and model agreement before it is shared, edited, cited, taught, or published.
A useful claim check starts with the wording in front of you.
The goal is not to ask whether a broad topic is true. The goal is to keep the exact claim, evidence burden, caveats, and source trail visible long enough for a human reviewer to challenge the result.
Preserve the claim
Check the wording as written, including numbers, dates, named sources, quotes, and scope limits that change what evidence is needed.
Inspect the evidence
Look for source links, whether those sources support the exact wording, and where the claim depends on context that may be missing.
Use disagreement
When models disagree or caveats appear, treat that as a review signal. Slow down before the claim moves into higher-trust work.
Use it when a claim needs receipts.
The claim-checking workflow is built for moments where a single sentence can change a reader's understanding and the source trail needs to be inspectable.
AI-assisted drafts
- Generated summaries
- Draft article assertions
- Claims copied from assistant output
News and source pages
- Highlighted article claims
- Newsletter statistics
- Claims with unclear attribution
Research and education
- Teaching examples
- Student source trails
- Research-note citations
Run a first pass, then verify manually where it matters.
FactSentinel is designed to surface the review trail: verdict, confidence, reasoning, caveats, model agreement, and sources. It does not replace editorial, academic, or professional judgment.
Checking a news article?
Use the news fact checker when a headline, statistic, quote, or attributed claim needs a visible first-pass review before it travels.
Need to inspect citations?
Use the citation checker when the claim depends on reference titles, journals, reports, policy documents, author names, or source dates.
Need to review AI hallucination risk?
Use the hallucination checker when AI-generated text includes confident wording, invented-looking sources, unsupported numbers, or a thin evidence trail.
Need to interpret model disagreement?
A split result can be the most useful part of the review. Use the model-disagreement field note when the next step is deciding what a human should inspect.
Need a real source-failure example?
The South Africa AI policy case study shows why a polished reference list still needs claim-level and citation-level verification before publication.
Common questions
What is an AI claim checker?
It is a first-pass workflow for checking whether a specific claim has enough source support, context, caveats, and model agreement before anyone relies on it.
Can FactSentinel make the final truth decision?
No. FactSentinel helps surface evidence, reasoning, confidence, caveats, and model disagreement. A human still decides what needs manual verification and what is ready to use.
What should I check first?
Start with claims that include numbers, quotes, dates, source names, policy or legal references, medical context, citations, or anything that will be shared, edited, taught, filed, or published.
Should I use the web checker or Chrome extension?
Use the web checker for pasted claims and drafts. Use the Chrome extension when you want to check selected text directly from an article, source page, document, or research note.
Put a claim through a visible review trail.
Use FactSentinel when a sentence has enough consequence that the sources, caveats, and disagreement signals should be visible before it moves forward.