notion.so

Record Ki57l3f2E · May 31, 2026

for “team collaboration tool”

This audit was run before live answers existed, so it has no observed verdict.

47/100 readiness

Readiness needs work

How ready the page is to be cited, across 30 signals. Readiness, not a citation count.

Comparison not run. This audit ran without any competing pages to compare against, so there is no ranking to show.

This week’s top fixesPredict the score CSV JSON View the page

The live answer

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This audit doesn't include a live answer from Claude. The score, comparison and fixes below are complete.

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How your page compares

No comparison pages were available for this audit.

Readiness, signal by signal

30 signals, each measured from the page itself: 15 pass, 4 weak, 11 missing. Open any signal for the detail and the fix. This record was scored on an earlier, 30-signal version of the rubric; it has 47 signals today. Every signal is documented in the rubric.

Structure

54/100

JSON-LD structured data0
No JSON-LD blocks found. Answer engines key off schema.org markup to ground citations.
Fix: Add at least one schema.org JSON-LD block: Article, Product, FAQPage, or HowTo are the highest-lift types.
JSON-LD validity100
No JSON-LD blocks to validate (the presence signal covers that).
Schema type matches query intent0
No schema types: for a informational query, add one of: Article, BlogPosting, FAQPage, HowTo.
Fix: Add an Article JSON-LD block: the highest-leverage schema type for a "team collaboration tool" query.
FAQ / Q&A schema0
No FAQPage / QAPage schema: AI Overviews preferentially cite pages exposing structured Q&A.
Fix: Add a FAQPage schema with 3-5 questions buyers actually ask. The single most direct path to AI Overview citation.
H1 quality100
H1: "Meet the night shift." (21 chars).
H2 subtopic coverage100
12 H2 headings: answer engines decompose a buyer query into subtopics and match each to an H2.
Lists (bulleted / numbered)100
20 lists found.
Comparison tables50
No tables. Comparison / specification tables are heavily cited for product and technical queries.
Image alt-text coverage48
32 of 66 images have alt text (48%).
Fix: Add descriptive alt text to every image: engines index alt text as part of page content.
Open Graph metadata100
Open Graph title, description, and image all present.

Authority

30/100

Outbound links to authoritative sources0
0 links to authoritative domains (.edu, .gov, named sources).
Fix: Add 2-3 outbound citations to .edu / .gov / known publishers. The GEO paper measured this as the #2 citation-lift factor.
Inline citations and attributions0
0 bracketed citations + 0 attribution phrases (per 200 words: 0%).
Fix: Add inline citations ([1], [2]) or attribution phrases ("according to X"). GEO factor #1 for credibility-weighted citation.
Statistic density100
33 numerical facts (~11.2 per 100 words).
Expert quotations25
1 blockquote + 0 long inline quotes.
Fix: Include at least one direct quote from a named expert, ideally in a <blockquote>. Quote-rich pages are 28% more likely to be cited (GEO paper).
Author byline0
No author byline. E-E-A-T penalises anonymous content.
Fix: Add a visible author byline (a `<div class="byline">` or `[rel="author"]`) AND a `<meta name="author">` tag.
Publish + update date markup0
No structured publish date. Freshness ranking can't be applied.
Fix: Add `<time datetime="...">` for published date AND a `<meta property="article:modified_time">` for updates.
Content freshness0
No parseable date: engines treat undated content as stale.
Fix: Add a parseable date: `<time datetime="2026-01-15">` or `<meta property="article:modified_time">`.
Technical / domain vocabulary100
89 multi-syllable words (~30.3% of total).

Content

40/100

Direct answer in the lead0
0 of 3 query terms appear in the first paragraph.
Fix: Open the page with a direct, complete answer to the buyer query: engines extract their citation snippet from the lead paragraph.
Query-term coverage33
33% of query terms appear somewhere on the page.
Fix: Cover every term in the buyer query at least once. Missing terms make embeddings drift and competitor pages win retrieval.
Named-entity coverage100
36 distinct proper-noun entities mentioned.
Readability (Flesch Reading Ease)20
Reading-ease score: 34.6 (target 60-70).
Fix: Page reads too academic: shorter sentences and simpler vocabulary lift citation rate by ~12% (GEO §4.3).
Content depth40
294 words. Target 300-2500: engines bias toward focused depth.
Fix: Page is too short. Expand to at least 500 words covering the query in depth.
Information density70
Unique-word ratio: 60.5% (target 40-60%).
Definitional sentences33
1 definitional sentence.
Fix: Include definitional sentences ("X is...", "X refers to..."). Engines specifically extract definitions for AI Overview snippets.

Trust

100/100

HTTPS100
Page served over HTTPS.
Canonical URL100
Canonical: https://www.notion.com/
Mobile-friendly viewport100
Viewport meta tag present.
Twitter Card100
Twitter Card metadata present.
Meta description100
Meta description: 124 chars (target 50-160).

The Fix Kit

Readiness needs work (47/100): 9 structural patches needed. Paste-ready patches, filled from what your page already states. Anything in [brackets] is yours to fill in before publishing.

Article JSON-LD

Schema markup · High priority

A Article schema is the strongest single AEO signal for a informational query. Paste this in the <head>.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Meet the night shift.",
  "description": "Build Custom Agents, search across all your apps, and automate busywork. The AI workspace where teams get more done, faster.",
  "url": "https://www.notion.so/",
  "datePublished": "2026-05-31",
  "author": {
    "@type": "Person",
    "name": "Add author name"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Notion",
    "logo": {
      "@type": "ImageObject",
      "url": "https://www.notion.so/logo.png"
    }
  }
}
</script>

FAQPage JSON-LD

Schema markup · High priority

FAQPage is the single most-cited schema type by Google AI Overviews. Add 3-5 Q&A pairs covering what buyers ask.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is Keep work moving 24/7?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"team collaboration tool\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Automate repetitive work for your team?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"team collaboration tool\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Ask your on-demand assistants?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"team collaboration tool\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is You assign the tasks. Notion Agent does the work?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"team collaboration tool\" would expect."
      }
    }
  ]
}
</script>

Organization JSON-LD

Schema markup · Medium priority

Organization schema gives the engine an E-E-A-T anchor: pages without one are treated as anonymous.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Notion",
  "url": "https://www.notion.so",
  "logo": "https://www.notion.so/logo.png",
  "sameAs": [
    "https://twitter.com/your-handle",
    "https://linkedin.com/company/your-org"
  ]
}
</script>