AI Content Detector
Estimate AI-likelihood using transparent heuristics. This result is probabilistic, not definitive.
How teams use AI-content detection as an editorial quality gate
This detector is built for triage, not certainty. In real publishing workflows, the biggest problem is usually not “AI vs human” labeling—it is quality drift: repetitive rhythm, generic transitions, and low-information phrasing that weakens trust and discoverability. This tool helps you catch those patterns early with transparent signals (variance, lexical diversity proxy, repetition rate) so editors can decide whether a draft needs another pass before publication. It is especially useful when teams produce many pages quickly and human review time is limited.
A practical operating pattern is to run this check at the end of each draft cycle. Start with text mode for unpublished copy. If a page is already live, use URL mode to inspect rendered content instead of trusting source docs. Treat high-probability bands as a prompt for improvement, not an automatic rejection. Fix one issue at a time—often sentence rhythm or repeated terms—then rerun and compare movement. This keeps revisions focused and prevents heavy rewrites that accidentally remove useful detail.
For weekly freshness cadence, audit 3-5 priority pages: one newly published page, one older high-traffic page, and one underperforming support article. Pair this with Semantic Similarity Comparator to ensure edits preserve intent and Content Gap Extractor to add missing practical depth where copy still feels thin. The result is a repeatable quality loop that improves readability without breaking route stability.
Practical FAQ
Can this tool prove a text was AI-generated?
No. It provides probabilistic language-pattern signals. Use it for editorial triage, not definitive authorship claims.
What should I fix first when risk is high?
Usually start with repetition and rhythm: reduce repeated terms, vary sentence length, and add concrete specifics tied to real outcomes.
How long should input text be for reliable signals?
Longer samples (multiple paragraphs) are more reliable than short snippets. Very short content naturally lowers confidence.
How to use
- Choose Text input or URL extraction.
- Run analysis on at least one solid paragraph (preferably several).
- Review the probability band and signal breakdown.
- Apply one rewrite fix, rerun, then publish.
Next-step workflow
- Semantic Similarity Comparator to verify rewrites kept original meaning.
- SERP Snippet Optimizer to improve click intent after copy cleanup.
- Technical SEO Health Scorecard to prioritize quality work alongside technical blockers.