` or `[rel=\"author\"]`) AND a `` tag.","priority":"high","explainer":"No author byline. E-E-A-T penalises anonymous content."},{"key":"headline-rewrite","kind":"copy","title":"H1 rewrite suggestions","content":"• Next.js: A Practical Guide for 2026\n• What Next.js Actually Means for Teams\n• Deploy next.js app — Explained in 5 Minutes","priority":"high","explainer":"Three H1 candidates that lead with the buyer's core query term and stay within the 20-80 character AEO sweet spot."}],"schemas":[{"key":"primary-schema","kind":"schema-json-ld","title":"Article JSON-LD","content":"","priority":"high","explainer":"A Article schema is the strongest single AEO signal for a informational query. Paste this in the ."},{"key":"faq-schema","kind":"schema-json-ld","title":"FAQPage JSON-LD","content":"","priority":"high","explainer":"FAQPage is the single most-cited schema type by Google AI Overviews. Add 3-5 Q&A pairs covering what buyers ask."},{"key":"organization-schema","kind":"schema-json-ld","title":"Organization JSON-LD","content":"","priority":"medium","explainer":"Organization schema gives the engine an E-E-A-T anchor — pages without one are treated as anonymous."}],"metadata":[]},"patches":[{"key":"primary-schema","kind":"schema-json-ld","title":"Article JSON-LD","content":"","priority":"high","explainer":"A Article schema is the strongest single AEO signal for a informational query. Paste this in the ."},{"key":"faq-schema","kind":"schema-json-ld","title":"FAQPage JSON-LD","content":"","priority":"high","explainer":"FAQPage is the single most-cited schema type by Google AI Overviews. Add 3-5 Q&A pairs covering what buyers ask."},{"key":"organization-schema","kind":"schema-json-ld","title":"Organization JSON-LD","content":"","priority":"medium","explainer":"Organization schema gives the engine an E-E-A-T anchor — pages without one are treated as anonymous."},{"key":"h1-quality","kind":"structure","title":"H1 quality","content":"Reduce to a single H1. Demote secondary H1s to H2.","priority":"medium","explainer":"2 H1s — multiple H1s confuse the retrieval-anchor signal."},{"key":"outbound-authority-links","kind":"structure","title":"Outbound links to authoritative sources","content":"Add 2-3 outbound citations to .edu / .gov / known publishers. The GEO paper measured this as the #2 citation-lift factor.","priority":"high","explainer":"0 links to authoritative domains (.edu, .gov, named sources)."},{"key":"citation-density","kind":"structure","title":"Inline citations and attributions","content":"Add inline citations ([1], [2]) or attribution phrases (\"according to X\"). GEO factor #1 for credibility-weighted citation.","priority":"high","explainer":"0 bracketed citations + 0 attribution phrases (per 200 words: 0%)."},{"key":"statistic-density","kind":"structure","title":"Statistic density","content":"Add concrete statistics with sources. AI engines preferentially cite paragraphs containing numbers — a GEO paper §4.1 finding.","priority":"high","explainer":"0 numerical facts (~0 per 100 words)."},{"key":"quotations","kind":"structure","title":"Expert quotations","content":"Include at least one direct quote from a named expert, ideally in a

. Quote-rich pages are 28% more likely to be cited (GEO paper).","priority":"high","explainer":"0 blockquotes + 0 long inline quotes."},{"key":"author-byline","kind":"structure","title":"Author byline","content":"Add a visible author byline (a `
` or `[rel=\"author\"]`) AND a `` tag.","priority":"high","explainer":"No author byline. E-E-A-T penalises anonymous content."},{"key":"headline-rewrite","kind":"copy","title":"H1 rewrite suggestions","content":"• Next.js: A Practical Guide for 2026\n• What Next.js Actually Means for Teams\n• Deploy next.js app — Explained in 5 Minutes","priority":"high","explainer":"Three H1 candidates that lead with the buyer's core query term and stay within the 20-80 character AEO sweet spot."}],"headline":"Behind the cited cohort (42/100) — 10 structural patches needed"},"rubric":{"overall":42,"signals":[{"key":"json-ld-presence","label":"JSON-LD structured data","score":0.7,"detail":"1 JSON-LD block found.","remedy":null,"weight":0.13,"category":"structure","severity":"weak"},{"key":"json-ld-validity","label":"JSON-LD validity","score":1,"detail":"1 of 1 JSON-LD block parse cleanly.","remedy":null,"weight":0.1,"category":"structure","severity":"ok"},{"key":"json-ld-relevance","label":"Schema type matches query intent","score":0.4,"detail":"Schema types found (SoftwareApplication) don't match a informational buyer intent.","remedy":"Add a Article JSON-LD block — the highest-leverage schema type for a \"deploy next.js app\" query.","weight":0.12,"category":"structure","severity":"weak"},{"key":"faq-schema","label":"FAQ / Q&A schema","score":0,"detail":"No FAQPage / QAPage schema — AI Overviews preferentially cite pages exposing structured Q&A.","remedy":"Add a FAQPage schema with 3-5 questions buyers actually ask. The single most direct path to AI Overview citation.","weight":0.1,"category":"structure","severity":"absent"},{"key":"h1-quality","label":"H1 quality","score":0.3,"detail":"2 H1s — multiple H1s confuse the retrieval-anchor signal.","remedy":"Reduce to a single H1. Demote secondary H1s to H2.","weight":0.1,"category":"structure","severity":"weak"},{"key":"h2-coverage","label":"H2 subtopic coverage","score":1,"detail":"13 H2 headings — answer engines decompose a buyer query into subtopics and match each to an H2.","remedy":null,"weight":0.09,"category":"structure","severity":"ok"},{"key":"lists","label":"Lists (bulleted / numbered)","score":1,"detail":"25 lists found.","remedy":null,"weight":0.08,"category":"structure","severity":"ok"},{"key":"tables","label":"Comparison tables","score":0.5,"detail":"No tables. Comparison / specification tables are heavily cited for product and technical queries.","remedy":null,"weight":0.07,"category":"structure","severity":"ok"},{"key":"alt-text","label":"Image alt-text coverage","score":0,"detail":"0 of 1 images have alt text (0%).","remedy":"Add descriptive alt text to every image — engines index alt text as part of page content.","weight":0.05,"category":"structure","severity":"absent"},{"key":"open-graph","label":"Open Graph metadata","score":1,"detail":"Open Graph title, description, and image all present.","remedy":null,"weight":0.06,"category":"structure","severity":"ok"},{"key":"outbound-authority-links","label":"Outbound links to authoritative sources","score":0,"detail":"0 links to authoritative domains (.edu, .gov, named sources).","remedy":"Add 2-3 outbound citations to .edu / .gov / known publishers. The GEO paper measured this as the #2 citation-lift factor.","weight":0.16,"category":"authority","severity":"absent"},{"key":"citation-density","label":"Inline citations and attributions","score":0,"detail":"0 bracketed citations + 0 attribution phrases (per 200 words: 0%).","remedy":"Add inline citations ([1], [2]) or attribution phrases (\"according to X\"). GEO factor #1 for credibility-weighted citation.","weight":0.14,"category":"authority","severity":"absent"},{"key":"statistic-density","label":"Statistic density","score":0,"detail":"0 numerical facts (~0 per 100 words).","remedy":"Add concrete statistics with sources. AI engines preferentially cite paragraphs containing numbers — a GEO paper §4.1 finding.","weight":0.13,"category":"authority","severity":"absent"},{"key":"quotations","label":"Expert quotations","score":0,"detail":"0 blockquotes + 0 long inline quotes.","remedy":"Include at least one direct quote from a named expert, ideally in a
. Quote-rich pages are 28% more likely to be cited (GEO paper).","weight":0.11,"category":"authority","severity":"absent"},{"key":"author-byline","label":"Author byline","score":0,"detail":"No author byline. E-E-A-T penalises anonymous content.","remedy":"Add a visible author byline (a `
` or `[rel=\"author\"]`) AND a `` tag.","weight":0.1,"category":"authority","severity":"absent"},{"key":"date-markup","label":"Publish + update date markup","score":0,"detail":"No structured publish date. Freshness ranking can't be applied.","remedy":"Add `` for published date AND a `` for updates.","weight":0.09,"category":"authority","severity":"absent"},{"key":"freshness","label":"Content freshness","score":0,"detail":"No parseable date — engines treat undated content as stale.","remedy":"Add a parseable date — `` or ``.","weight":0.13,"category":"authority","severity":"absent"},{"key":"technical-terms","label":"Technical / domain vocabulary","score":1,"detail":"32 multi-syllable words (~32.0% of total).","remedy":null,"weight":0.14,"category":"authority","severity":"ok"},{"key":"direct-answer","label":"Direct answer in the lead","score":0,"detail":"0 of 4 query terms appear in the first paragraph.","remedy":"Open the page with a direct, complete answer to the buyer query — engines extract their citation snippet from the lead paragraph.","weight":0.18,"category":"content","severity":"absent"},{"key":"query-coverage","label":"Query-term coverage","score":0.25,"detail":"25% of query terms appear somewhere on the page.","remedy":"Cover every term in the buyer query at least once. Missing terms make embeddings drift and competitor pages win retrieval.","weight":0.15,"category":"content","severity":"absent"},{"key":"entity-coverage","label":"Named-entity coverage","score":1,"detail":"12 distinct proper-noun entities mentioned.","remedy":null,"weight":0.14,"category":"content","severity":"ok"},{"key":"readability","label":"Readability (Flesch Reading Ease)","score":0.2,"detail":"Reading-ease score: 27.2 (target 60-70).","remedy":"Page reads too academic — shorter sentences and simpler vocabulary lift citation rate by ~12% (GEO §4.3).","weight":0.11,"category":"content","severity":"absent"},{"key":"length","label":"Content depth","score":0,"detail":"100 words. Target 300-2500 — engines bias toward focused depth.","remedy":"Page is too short. Expand to at least 500 words covering the query in depth.","weight":0.1,"category":"content","severity":"absent"},{"key":"info-density","label":"Information density","score":1,"detail":"Unique-word ratio: 56.0% (target 40-60%).","remedy":null,"weight":0.1,"category":"content","severity":"ok"},{"key":"definitions","label":"Definitional sentences","score":0,"detail":"0 definitional sentences.","remedy":"Include definitional sentences (\"X is...\", \"X refers to...\"). Engines specifically extract definitions for AI Overview snippets.","weight":0.1,"category":"content","severity":"absent"},{"key":"https","label":"HTTPS","score":1,"detail":"Page served over HTTPS.","remedy":null,"weight":0.25,"category":"trust","severity":"ok"},{"key":"canonical","label":"Canonical URL","score":1,"detail":"Canonical: https://vercel.com","remedy":null,"weight":0.15,"category":"trust","severity":"ok"},{"key":"viewport","label":"Mobile-friendly viewport","score":1,"detail":"Viewport meta tag present.","remedy":null,"weight":0.15,"category":"trust","severity":"ok"},{"key":"twitter-card","label":"Twitter Card","score":1,"detail":"Twitter Card metadata present.","remedy":null,"weight":0.1,"category":"trust","severity":"ok"},{"key":"meta-description","label":"Meta description","score":1,"detail":"Meta description: 111 chars (target 50-160).","remedy":null,"weight":0.35,"category":"trust","severity":"ok"}],"summary":{"ok":13,"weak":3,"total":30,"absent":14},"categories":[{"score":59,"signals":[{"key":"json-ld-presence","label":"JSON-LD structured data","score":0.7,"detail":"1 JSON-LD block found.","remedy":null,"weight":0.13,"category":"structure","severity":"weak"},{"key":"json-ld-validity","label":"JSON-LD validity","score":1,"detail":"1 of 1 JSON-LD block parse cleanly.","remedy":null,"weight":0.1,"category":"structure","severity":"ok"},{"key":"json-ld-relevance","label":"Schema type matches query intent","score":0.4,"detail":"Schema types found (SoftwareApplication) don't match a informational buyer intent.","remedy":"Add a Article JSON-LD block — the highest-leverage schema type for a \"deploy next.js app\" query.","weight":0.12,"category":"structure","severity":"weak"},{"key":"faq-schema","label":"FAQ / Q&A schema","score":0,"detail":"No FAQPage / QAPage schema — AI Overviews preferentially cite pages exposing structured Q&A.","remedy":"Add a FAQPage schema with 3-5 questions buyers actually ask. The single most direct path to AI Overview citation.","weight":0.1,"category":"structure","severity":"absent"},{"key":"h1-quality","label":"H1 quality","score":0.3,"detail":"2 H1s — multiple H1s confuse the retrieval-anchor signal.","remedy":"Reduce to a single H1. Demote secondary H1s to H2.","weight":0.1,"category":"structure","severity":"weak"},{"key":"h2-coverage","label":"H2 subtopic coverage","score":1,"detail":"13 H2 headings — answer engines decompose a buyer query into subtopics and match each to an H2.","remedy":null,"weight":0.09,"category":"structure","severity":"ok"},{"key":"lists","label":"Lists (bulleted / numbered)","score":1,"detail":"25 lists found.","remedy":null,"weight":0.08,"category":"structure","severity":"ok"},{"key":"tables","label":"Comparison tables","score":0.5,"detail":"No tables. Comparison / specification tables are heavily cited for product and technical queries.","remedy":null,"weight":0.07,"category":"structure","severity":"ok"},{"key":"alt-text","label":"Image alt-text coverage","score":0,"detail":"0 of 1 images have alt text (0%).","remedy":"Add descriptive alt text to every image — engines index alt text as part of page content.","weight":0.05,"category":"structure","severity":"absent"},{"key":"open-graph","label":"Open Graph metadata","score":1,"detail":"Open Graph title, description, and image all present.","remedy":null,"weight":0.06,"category":"structure","severity":"ok"}],"category":"structure"},{"score":14,"signals":[{"key":"outbound-authority-links","label":"Outbound links to authoritative sources","score":0,"detail":"0 links to authoritative domains (.edu, .gov, named sources).","remedy":"Add 2-3 outbound citations to .edu / .gov / known publishers. The GEO paper measured this as the #2 citation-lift factor.","weight":0.16,"category":"authority","severity":"absent"},{"key":"citation-density","label":"Inline citations and attributions","score":0,"detail":"0 bracketed citations + 0 attribution phrases (per 200 words: 0%).","remedy":"Add inline citations ([1], [2]) or attribution phrases (\"according to X\"). GEO factor #1 for credibility-weighted citation.","weight":0.14,"category":"authority","severity":"absent"},{"key":"statistic-density","label":"Statistic density","score":0,"detail":"0 numerical facts (~0 per 100 words).","remedy":"Add concrete statistics with sources. AI engines preferentially cite paragraphs containing numbers — a GEO paper §4.1 finding.","weight":0.13,"category":"authority","severity":"absent"},{"key":"quotations","label":"Expert quotations","score":0,"detail":"0 blockquotes + 0 long inline quotes.","remedy":"Include at least one direct quote from a named expert, ideally in a
. Quote-rich pages are 28% more likely to be cited (GEO paper).","weight":0.11,"category":"authority","severity":"absent"},{"key":"author-byline","label":"Author byline","score":0,"detail":"No author byline. E-E-A-T penalises anonymous content.","remedy":"Add a visible author byline (a `
` or `[rel=\"author\"]`) AND a `` tag.","weight":0.1,"category":"authority","severity":"absent"},{"key":"date-markup","label":"Publish + update date markup","score":0,"detail":"No structured publish date. Freshness ranking can't be applied.","remedy":"Add `` for published date AND a `` for updates.","weight":0.09,"category":"authority","severity":"absent"},{"key":"freshness","label":"Content freshness","score":0,"detail":"No parseable date — engines treat undated content as stale.","remedy":"Add a parseable date — `` or ``.","weight":0.13,"category":"authority","severity":"absent"},{"key":"technical-terms","label":"Technical / domain vocabulary","score":1,"detail":"32 multi-syllable words (~32.0% of total).","remedy":null,"weight":0.14,"category":"authority","severity":"ok"}],"category":"authority"},{"score":34,"signals":[{"key":"direct-answer","label":"Direct answer in the lead","score":0,"detail":"0 of 4 query terms appear in the first paragraph.","remedy":"Open the page with a direct, complete answer to the buyer query — engines extract their citation snippet from the lead paragraph.","weight":0.18,"category":"content","severity":"absent"},{"key":"query-coverage","label":"Query-term coverage","score":0.25,"detail":"25% of query terms appear somewhere on the page.","remedy":"Cover every term in the buyer query at least once. Missing terms make embeddings drift and competitor pages win retrieval.","weight":0.15,"category":"content","severity":"absent"},{"key":"entity-coverage","label":"Named-entity coverage","score":1,"detail":"12 distinct proper-noun entities mentioned.","remedy":null,"weight":0.14,"category":"content","severity":"ok"},{"key":"readability","label":"Readability (Flesch Reading Ease)","score":0.2,"detail":"Reading-ease score: 27.2 (target 60-70).","remedy":"Page reads too academic — shorter sentences and simpler vocabulary lift citation rate by ~12% (GEO §4.3).","weight":0.11,"category":"content","severity":"absent"},{"key":"length","label":"Content depth","score":0,"detail":"100 words. Target 300-2500 — engines bias toward focused depth.","remedy":"Page is too short. Expand to at least 500 words covering the query in depth.","weight":0.1,"category":"content","severity":"absent"},{"key":"info-density","label":"Information density","score":1,"detail":"Unique-word ratio: 56.0% (target 40-60%).","remedy":null,"weight":0.1,"category":"content","severity":"ok"},{"key":"definitions","label":"Definitional sentences","score":0,"detail":"0 definitional sentences.","remedy":"Include definitional sentences (\"X is...\", \"X refers to...\"). Engines specifically extract definitions for AI Overview snippets.","weight":0.1,"category":"content","severity":"absent"}],"category":"content"},{"score":100,"signals":[{"key":"https","label":"HTTPS","score":1,"detail":"Page served over HTTPS.","remedy":null,"weight":0.25,"category":"trust","severity":"ok"},{"key":"canonical","label":"Canonical URL","score":1,"detail":"Canonical: https://vercel.com","remedy":null,"weight":0.15,"category":"trust","severity":"ok"},{"key":"viewport","label":"Mobile-friendly viewport","score":1,"detail":"Viewport meta tag present.","remedy":null,"weight":0.15,"category":"trust","severity":"ok"},{"key":"twitter-card","label":"Twitter Card","score":1,"detail":"Twitter Card metadata present.","remedy":null,"weight":0.1,"category":"trust","severity":"ok"},{"key":"meta-description","label":"Meta description","score":1,"detail":"Meta description: 111 chars (target 50-160).","remedy":null,"weight":0.35,"category":"trust","severity":"ok"}],"category":"trust"}]},"diagnostics":{"serp":{"ddg":false,"wikipedia":false},"embeddingsLoaded":false,"competitorsFetched":0},"serpContext":{"query":"deploy next.js app","results":[],"degraded":{"ddg":false,"wikipedia":false},"entities":[{"qid":"Q56062435","url":"https://en.wikipedia.org/wiki/Next.js","title":"Next.js","description":"Next.js is an open-source full-stack web development framework created by the private company Vercel providing React-based web applications with server-side rendering and static rendering."},{"qid":"Q17054505","url":"https://en.wikipedia.org/wiki/Google_Cloud_Platform","title":"Google Cloud Platform","description":"Google Cloud is a suite of cloud computing services offered by Google that provides a series of modular cloud services including computing, data storage, data analytics, and machine learning, alongside a set of management tools. It runs on the same infrastructure that Google uses"},{"qid":"Q117230892","url":"https://en.wikipedia.org/wiki/CodeSandbox","title":"CodeSandbox","description":"CodeSandbox is a cloud-based online integrated development environment (IDE) focused on web application development. It supports popular web technologies such as JavaScript, TypeScript, React, Vue.js, and Node.js. CodeSandbox allows users to create, edit, and deploy web applicati"}]},"semanticVoid":[],"centroidShift":null,"citationVerdict":{"rank":null,"cited":false,"cohort":0,"margin":0},"retrievalRanking":[]}}