Permission
Is the crawler allowed?
AI Search Readiness Report
Free, no signup. Five foundations—permission, extractability, renderability, operability, and commerce readiness—feed a four-stage explanation of whether an AI system can retrieve, use, cite, and accurately represent one page. These stay independent rather than becoming one invented AI score.
Five separate tests · registry 2026-07-29
Crawler access is only the first layer
Extractability
Are useful facts present in raw HTML?
Renderability
Does JavaScript successfully expose them?
Operability
Can an agent identify and use semantic controls?
Commerce readiness
Do page, schema, feed and checkout facts agree?
HTML remains primary. llms.txt, UCP, and other protocol files are optional distribution layers and cannot compensate for inaccessible or unextractable HTML.
Validate a protocol response
Upload verified-bot log evidence
Expected columns: crawler, timestamp, url, status, verification. A user-agent string alone is never treated as verified identity.
No log evidence uploaded.
Method note: stage weights are provisional and visible. An unavailable check never becomes a pass or a zero; it lowers the stage’s confidence instead. This observation describes the named consumer product at the recorded query time. A model API with search is an execution surface, not a proxy for the provider’s consumer product. Results can correlate, but they are not interchangeable.
General preserves the base readiness run. Named profiles add required page-signal checks; unavailable schema guidance and weights are shown as not evaluated.
Add observed answer evidence and retrieval-path inputs
When supplied, this also enables one bounded retrieval-off brand observation. It is not a query of ChatGPT, Gemini, or another consumer search product.
Checks run from our server; we fetch the URL you enter and don't keep the results. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.
Sample report Honest output shape
Suppose the public files and page HTML are fetchable, while rendered-page evidence and the optional AI-probe budget are unavailable. A faithful report does not fill those gaps with zeros:
Retrieve · scored from the crawler evidence that was evaluated · reduced confidence
Use · not evaluated where rendering or additional inputs are required
Cite · evaluated modules shown individually; unavailable modules excluded
Believe · not evaluated when the current run has no supporting module evidence
This is a static explanation of state handling, not a captured score for a named site. Live numeric results depend on the entered URL and the modules available during that run.
How to use it
- Enter the complete URL of one public page and choose its profile.
- Complete the anti-abuse check and select Run readiness report.
- Start with the four-stage summary, then expand each stage to see evaluated and unavailable modules.
- Fix explicit access or content findings first. Re-run after publishing, while allowing for provider-specific cache lag.
+ saves the current site or page. Use ☆ beside any saved site, page, or list to favorite it. Recent check history appears below.
Create a named list
Target filled from your local choices.
Site passport Local context for this saved site
Local data
Saved targets, named lists, and recent check summaries remain only in this browser.
Consumer-product context
No consumer AI product is queried by this report. These are dated qualitative priors, not simulated results or universal per-product scores.
ChatGPT consumer search: not evaluated — no product-specific query occurred. Contextual prior: citable source, quotability, information gain, and corroboration; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Gemini app: not evaluated — no product-specific query occurred. Contextual prior: parametric brand footprint; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Google AI Overviews: not evaluated — no product-specific query occurred. Contextual prior: classic SEO retrieval and ranking; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Google AI Mode: not evaluated — no product-specific query occurred. Contextual prior: extractable passage coverage across fan-out queries; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Perplexity consumer search: not evaluated — no product-specific query occurred. Contextual prior: freshness, indexing speed, and citable passage volume; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Claude consumer search: not evaluated — no product-specific query occurred. Contextual prior: broad authority and Brave-indexed presence; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Microsoft Copilot: not evaluated — no product-specific query occurred. Contextual prior: Bing indexation; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Grok consumer search: not evaluated — no product-specific query occurred. Contextual prior: parametric brand footprint and current community presence; provenance: Source unavailable — editorial prior without a cited primary or study source, as of 2026-07-11, low confidence.
Answer-caching lag: AI answers may continue to reflect cached or previously retrieved material for days to weeks after a fix. Recheck over time before treating an unchanged answer as proof that the fix failed.
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What the results mean
- Retrieve — whether an answer system can discover and fetch the page.
- Use — whether the available page content is usable by the evaluated modules.
- Cite — signals related to selecting and attributing a page as a source.
- Believe — signals related to identity, support, and accurate representation.
- Confidence — how much of the provisional stage weight was actually evaluated.
- Not evaluated — unavailable evidence, never an inferred pass or error.
How it works
A protected run receives a bounded request budget, compares browser and GPTBot responses, fetches robots.txt and public agent-discovery files through SSRF-guarded endpoints, models retrieval chunks from the captured HTML, and checks the exact URL against recent Common Crawl indexes. It also evaluates answer structure, citations, entity facts, and parseable freshness evidence. If you supply a brand, one named Workers AI observation is added with model and query-time attribution. Each evaluated numeric module contributes its published provisional weight; unavailable evidence remains reason-coded not evaluated.
Features
- Four-stage funnel instead of a universal composite score.
- Visible per-module weights, findings, and fix links.
- Per-crawler/model breakdown when a module has that evidence.
- Reason-specific unavailable states for fetch, render, AI, timeout, and input limits.
- Protected, bounded network runs and shareable URL state.
Limitations
Weights are provisional. The chunk, freshness, and entity-fact checks are deterministic approximations over captured HTML, while the optional brand check is one retrieval-off Workers AI observation. A fetch from this service can differ from a provider’s geography, identity, cache, or index. The report does not query commercial answer products, observe their private retrieval systems, or guarantee retrieval, use, attribution, or factual treatment.
Frequently asked questions
Why are there four stages instead of one AI readiness score?
Retrieval, use, citation, and accurate representation can each report errors independently. Keeping them separate prevents a strong content signal from hiding an access error or an unavailable module.
What does not evaluated mean?
The module had no usable evidence because it needs another input, exceeded a budget, timed out, failed to fetch, or does not apply. It is excluded from the stage score and lowers confidence rather than becoming zero.
Does a strong report guarantee AI citations?
No. The report checks observable page and access signals. It cannot see every provider’s index, ranking, generated answer, cache, or source-selection system.
Why can a recent fix take time to appear?
Answer systems may cache fetched pages, retrieval indexes, or generated responses. Re-crawl and regeneration timing varies by provider, so this report can verify the current public page without proving that a provider has refreshed its copy.
What modules does the current public run evaluate?
The public run checks crawler and edge access, recent Common Crawl presence, public agent-discovery signals, modeled chunk usability, answer and citation evidence, freshness, entity facts, and—when you provide a brand—a bounded retrieval-off model observation. An unavailable dependency remains not evaluated.
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حول الأداة
انظر ال مستقل الاسترجاع, استخدم, citation, و belief إشارات ذلك شكل ما إذا AI أنظمة يمكن يفعل الصحيح بواسطة واحد صفحة.
مجاني, من دون تسجيل. خمسة foundations—إذن, قابلية الاستخراج, renderability, operability, و تجارة جاهزية—موجز واحد four-stage شرح من ما إذا واحد AI نظام يمكن يسترجع, استخدم, استشهد, و بدقة يمثل واحد صفحة. هذه يبقى مستقل بدلًا من يصبح واحد مُختلَق AI درجة.
الميزات
- Four-stage قمع بدلًا من واحد عالمي مركّب درجة.
- مرئي per-module أوزان, نتائج, و أصلح روابط.
- Per-crawler/نموذج breakdown عندما واحد وحدة يحتوي ذلك دليل.
- Reason-specific غير متاح حالات من أجل جلب, تصيير, AI, مهلة, و إدخال حدود.
- محمي, محدود شبكة تشغيل و قابل للمشاركة عنوان URL حالة.
كيفية العمل
واحد محمي شغّل receives واحد محدود طلب ميزانية, يقارن متصفح و GPTBot استجابات, يجلب Robots.txt و عام agent-discovery ملفات من خلال SSRF-guarded endpoints, نماذج الاسترجاع أجزاء من ال مُلتقَط HTML, و تحقّقات ال دقيق عنوان URL مقابل الأخيرة شائع زحف فهارس. إنه أيضًا يقيّم إجابة بنية, citations, كيان حقائق, و قابل للتحليل حداثة دليل. إذا أنت تقديم واحد brand, واحد مسمّى Workers AI ملاحظة هو مضاف مع نموذج و query-time إسناد. كل مُقيَّم رقمي وحدة contributes الخاص به published provisional وزن; غير متاح دليل يبقى reason-coded ليس مُقيَّم.
القيود
- أوزان هي provisional. ال جزء, حداثة, و entity-fact تحقّقات هي حتمي approximations فوق مُلتقَط HTML, بينما ال اختياري brand تحقّق هو واحد الاسترجاع-off Workers AI ملاحظة. واحد جلب من هذا خدمة يمكن يختلف من واحد provider’s geography, هوية, ذاكرة مؤقتة, أو فهرس. ال تقرير يفعل ليس استعلام تجاري إجابة منتجات, راقب الخاص بهم خاص الاسترجاع أنظمة, أو يضمن الاسترجاع, استخدم, إسناد, أو factual treatment.
الأسئلة الشائعة
لماذا هي هناك أربعة stages بدلًا من واحد AI جاهزية درجة?
الاسترجاع, استخدم, citation, و دقيق تمثيل يمكن كل تقرير أخطاء بصورة مستقلة. احتفاظ هم منفصل يمنع واحد strong محتوى إشارة من hiding واحد وصول خطأ أو واحد غير متاح وحدة.
ما يفعل ليس مُقيَّم يعني?
ال وحدة كان لديه لا قابل للاستخدام دليل لأن إنه يحتاج آخر إدخال, exceeded واحد ميزانية, موقّت خارج, فشل إلى جلب, أو يفعل ليس تطبيق. إنه هو مستبعد من ال stage درجة و lowers ثقة بدلًا من يصبح صفر.
يفعل واحد strong تقرير يضمن AI citations?
لا. ال تقرير تحقّقات قابل للرصد صفحة و وصول إشارات. إنه لا يمكن انظر كل provider’s فهرس, ترتيب, منشأ إجابة, ذاكرة مؤقتة, أو source-selection نظام.
لماذا يمكن واحد الأخيرة أصلح يأخذ وقت إلى يظهر?
إجابة أنظمة قد ذاكرة مؤقتة مجلوب صفحات, الاسترجاع فهارس, أو منشأ استجابات. Re-crawl و regeneration توقيت varies بواسطة مزود, لذلك هذا تقرير يمكن تحقّق ال حالي عام صفحة من دون proving ذلك واحد مزود يحتوي محدّث الخاص به نسخ.
ما وحدات يفعل ال حالي عام شغّل يقيّم?
ال عام شغّل تحقّقات زاحف و حافة وصول, الأخيرة شائع زحف وجود, عام agent-discovery إشارات, مُمثَّل جزء usability, إجابة و citation دليل, حداثة, كيان حقائق, و—عندما أنت وفّر واحد brand—واحد محدود الاسترجاع-off نموذج ملاحظة. واحد غير متاح اعتمادية يبقى ليس مُقيَّم.