Cross-Signal Contradiction Check
Compares visible dates, JSON-LD, Last-Modified, and sitemap lastmod against each other and flags any that disagree by more than a month.
AI Content Freshness Auditor for Date Signal Consistency
DIATAS;DR: AI Content Freshness Auditor checks cross-check visible dates, json-ld, last-modified, and sitemap lastmod for stale or conflicting freshness signals. so you can confirm the current issue, understand when it matters, and move directly into the next fix without leaving the browser.
Cross-check every freshness signal on a page and flag stale content before it loses AI citations.
Diperbarui
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Visible published/updated date extraction JSON-LD datePublished and dateModified parsing Last-Modified header and sitemap lastmod cross-check Contradiction detection between signals Staleness scoring tuned for AI citation
What does AI Content Freshness Auditor do?
AI Content Freshness Auditor reads every freshness signal a single page exposes and checks whether they agree. It collects the visible "published" and "updated" dates in your copy, the datePublished and dateModified values in JSON-LD, the Last-Modified response header, and the matching lastmod entry from your XML sitemap, then compares them all against today.
Why freshness matters for AI SEO: Answer engines like Google AI Overviews, Perplexity, and ChatGPT browse weigh recency heavily when choosing what to cite. A page whose signals disagree — copy that says "updated this week" while the Last-Modified header and sitemap lastmod are two years stale — reads as untrustworthy, and retrieval systems quietly pass it over.
Catch silent contradictions: Mismatched date signals are easy to ship and hard to spot. A republish that updates the visible byline but not the JSON-LD, or a CDN that resets Last-Modified on every deploy, can leave conflicting timestamps that undercut your recency story.
Find stale pages that bleed citations: The auditor flags content past the 12-to-18-month staleness line that suppresses AI citation, so you can prioritize refreshes by impact instead of guessing.
Editorial and content-ops QA: Run it before a refresh to confirm the page actually looks stale to machines, and after a refresh to verify every signal was synchronized to the same true last-update date.
When should you use AI Content Freshness Auditor?
AI Content Freshness Auditor is most useful when you need a direct answer on a live URL or draft before you change templates, ship content, or rerun a wider audit.
Cara Menggunakan Alat Ini
- 1 Enter the URL of a published page or article.
- 2 Run the freshness audit.
- 3 Review the signal table, contradictions, staleness verdict, and coverage gaps.
- 4 Sync your date signals or refresh the content, then rerun to confirm.
What should you do next?
Pair freshness checks with citation readiness and answer extractability audits to keep refreshed pages eligible for AI citation. Then move to the related checks below to confirm the fix on the live canonical page.
Apa yang Anda Dapatkan
Citation-Tuned Staleness Score
Grades the page against the 12-to-18-month freshness window AI answer engines favor, so you know which content to refresh first.
Signal Coverage Map
Shows which of the four freshness signal sources are present and which are missing, with fixes to strengthen weak spots.
Frequently Asked Questions
Jawaban tentang AI Content Freshness Auditor
Run the page URL through the AI Content Freshness Auditor and read the staleness grade. It cross-checks the visible date in your copy, JSON-LD datePublished/dateModified, the Last-Modified header, and your sitemap lastmod, then flags the page if the freshest verifiable signal is past the 12-to-18-month window that AI answer engines treat as stale. Pages with no machine-readable date at all are flagged hardest, because retrieval systems cannot confirm recency.
Because answer engines distrust pages whose freshness signals disagree. When your visible byline says one date, your JSON-LD says another, and the Last-Modified header or sitemap lastmod says a third, an AI system cannot establish a reliable last-update date, so it discounts the page's recency and is less likely to cite it. The auditor surfaces every pair of signals that conflict by more than 30 days and ranks the worst contradictions first.
It checks four sources: visible dates, structured data, headers, and the sitemap. Specifically, it extracts labelled "published" and "updated" dates from the visible copy, parses datePublished and dateModified (plus article:published_time and article:modified_time) from JSON-LD, reads the Last-Modified response header, and looks up the page's lastmod entry in your XML sitemap. It then normalizes all of them to compare ages and detect mismatches.
There is no fixed cutoff, but content older than 12 to 18 months tends to lose citation share. AI Overviews and tools like Perplexity favor recently updated, signal-consistent pages for time-sensitive queries. This auditor treats roughly a year as a caution threshold and 18 months as the point where a page reads as stale, so you can refresh high-value pages before they fall out of answer results.