VectorCiteVectorCite
AEO audit · May 31, 2026

linear.app

on the queryissue tracking software for startups
54AEO score
Behind the cited cohort
Not cited at top 0·simulated retrieval against 0 candidates
⚡ This Week — top 5 fixes📈 Predict score⬇ CSV⬇ JSON View page

The 30-signal rubric

17 ok · 2 weak · 11 absent

Every signal is measurable from your page. Hover for the detail and a remedy.

Structure

50/100
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 a Article JSON-LD block — the highest-leverage schema type for a "issue tracking software for startups" 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 quality60
H1: "The product development system for teams and agentsThe produ…" (152 chars).
Fix: Adjust H1 length to 20-80 chars — shorter than 20 lacks context, longer than 80 loses retrieval focus.
H2 subtopic coverage100
8 H2 headings — answer engines decompose a buyer query into subtopics and match each to an H2.
Lists (bulleted / numbered)100
12 lists found.
Comparison tables50
No tables. Comparison / specification tables are heavily cited for product and technical queries.
Image alt-text coverage45
14 of 31 images have alt text (45%).
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

35/100
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
29 numerical facts (~2.9 per 100 words).
Expert quotations75
3 blockquotes + 0 long inline quotes.
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
319 multi-syllable words (~31.8% of total).

Content

62/100
Direct answer in the lead20
1 of 5 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 coverage100
100% of query terms appear somewhere on the page.
Named-entity coverage100
56 distinct proper-noun entities mentioned.
Readability (Flesch Reading Ease)20
Reading-ease score: 21.7 (target 60-70).
Fix: Page reads too academic — shorter sentences and simpler vocabulary lift citation rate by ~12% (GEO §4.3).
Content depth100
1003 words. Target 300-2500 — engines bias toward focused depth.
Information density100
Unique-word ratio: 52.9% (target 40-60%).
Definitional sentences0
0 definitional sentences.
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://linear.app
Mobile-friendly viewport100
Viewport meta tag present.
Twitter Card100
Twitter Card metadata present.
Meta description100
Meta description: 64 chars (target 50-160).

The Fix Kit

Behind the cited cohort (54/100) — 10 structural patches needed

Ready-to-paste patches. Schema.org JSON-LD, meta tags, copy guidance. The first three below are unlocked; the rest are gated behind a free account.

Article JSON-LD

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": "The product development system for teams and agentsThe product developmentsystem for teams and agentsThe product development system for teams and agents",
  "description": "Purpose-built for planning and building products with AI agents.",
  "url": "https://linear.app/",
  "datePublished": "2026-05-31",
  "author": {
    "@type": "Person",
    "name": "Add author name"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Linear",
    "logo": {
      "@type": "ImageObject",
      "url": "https://linear.app/logo.png"
    }
  }
}
</script>

FAQPage JSON-LD

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 A new species of product tool. Purpose-built for modern teams with AI workflows at its core, Linear sets a new standard for planning and building products?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Make product \noperations self-driving?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Define the \nproduct direction?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    },
    {
      "@type": "Question",
      "name": "What is Move work forward \nacross teams and agents?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add a 1-2 sentence answer here, mentioning the key entity / number / claim that buyers searching \"issue tracking software for startups\" would expect."
      }
    }
  ]
}
</script>

Organization JSON-LD

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": "Linear",
  "url": "https://linear.app",
  "logo": "https://linear.app/logo.png",
  "sameAs": [
    "https://twitter.com/your-handle",
    "https://linkedin.com/company/your-org"
  ]
}
</script>
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