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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Tentang alat
AI Pencarian Readiness Laporan
module tidak memiliki dapat digunakan bukti karena ini needs lain masukan, exceeded budget, timed keluar, gagal untuk ambil, atau tidak apply. Ini adalah dikecualikan dari stage score dan lowers confidence daripada daripada becoming zero.
Home › Alat › AI Pencarian Readiness Laporan
Fitur
- Mengapa adalah di sana four stages alih-alih dari satu AI readiness score?
- Arti hasil Ambil — apakah jawaban system dapat discover dan ambil halaman. Gunakan — apakah tersedia halaman konten adalah dapat digunakan oleh dievaluasi modules. Cite — sinyal related untuk selecting dan attributing halaman sebagai sumber. Believe — sinyal related untuk identitas, mendukung, dan accurate representation. Confidence — bagaimana much dari provisional stage weight adalah sebenarnya dievaluasi. Tidak dievaluasi — tidak tersedia bukti, tidak pernah inferred lulus atau kesalahan.
- Consumer-produk konteks
- Tidak. laporan memeriksa dapat diamati halaman dan akses sinyal. Ini tidak dapat melihat setiap provider’s indeks, peringkat, dihasilkan jawaban, cache, atau sumber-pemilihan system.
- Bagaimana ini berfungsi protected jalankan menerima dibatasi permintaan budget, membandingkan browser dan GPTBot respons, mengambil robots.txt dan publik agen-penemuan berkas melalui SSRF-guarded endpoints, models retrieval chunks dari captured HTML, dan memeriksa tepat URL terhadap terbaru Umum Rayapi indexes. Ini juga evaluates jawaban structure, citations, entity fakta, dan parseable kebaruan bukti. Jika Anda supply merek, satu named Workers AI pengamatan adalah added dengan model dan kueri-waktu attribution. Setiap dievaluasi numeric module contributes diterbitkan-nya provisional weight; tidak tersedia bukti tetap alasan-coded tidak dievaluasi.
Cara kerja
Mengapa dapat terbaru perbaikan take waktu untuk muncul? publik jalankan memeriksa perayap dan edge akses, terbaru Umum Rayapi keberadaan, publik agen-penemuan sinyal, modeled chunk usability, jawaban dan citation bukti, kebaruan, entity fakta, dan—ketika Anda provide merek— dibatasi retrieval-off model pengamatan. tidak tersedia dependency tetap tidak dievaluasi. Tidak ada consumer AI produk adalah ditanyakan oleh ini laporan. Ini adalah dated qualitative priors, tidak disimulasikan hasil atau universal per-produk scores. Retrieval, gunakan, citation, dan accurate representation dapat setiap laporan kesalahan independently. Mempertahankan mereka terpisah prevents strong konten sinyal dari menyembunyikan akses kesalahan atau tidak tersedia module.
Batasan
- Apa modules melakukan saat ini publik jalankan evaluate?
- Metode catatan: stage weights adalah provisional dan terlihat. tidak tersedia periksa tidak pernah becomes lulus atau zero; ini lowers stage’s confidence alih-alih.
- Fitur Four-stage funnel alih-alih dari universal composite score. Terlihat per-module weights, temuan, dan perbaikan tautan. Per-perayap/model breakdown ketika module memiliki yang bukti. Alasan-spesifik tidak tersedia keadaan untuk ambil, render, AI, batas waktu, dan masukan batasan. Protected, dibatasi jaringan berjalan dan shareable URL keadaan.
- Apa tidak dievaluasi mean?
Pertanyaan umum
Gratis, tanpa pendaftaran. Five foundations—permission, extractability, renderability, operability, dan commerce readiness—feed four-stage explanation dari apakah AI system dapat ambil, gunakan, cite, dan accurately represent satu halaman. Ini stay independent daripada daripada becoming satu dibuat AI score.
Jalankan readiness laporan
Batasan Weights adalah provisional. chunk, kebaruan, dan entity-fakta memeriksa adalah deterministik approximations di atas captured HTML, sementara opsional merek periksa adalah satu retrieval-off Workers AI pengamatan. ambil dari ini service dapat berbeda dari provider’s geography, identitas, cache, atau indeks. laporan tidak kueri commercial jawaban produk, observe mereka privat retrieval systems, atau jaminan retrieval, gunakan, attribution, atau factual perlakuan.
Jawaban systems mungkin cache diambil halaman, retrieval indexes, atau dihasilkan respons. Re-rayapi dan regeneration timing varies oleh provider, so ini laporan dapat verifikasi saat ini publik halaman tanpa proving yang provider memiliki refreshed salin-nya.
Halaman profile
Melihat independent retrieval, gunakan, citation, dan belief sinyal yang shape apakah AI systems dapat tepat oleh halaman.
Melakukan strong laporan jaminan AI citations?
Publik halaman URL