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Public Audit Teardown — linear.app

Linear AI Recommendation Audit

86% mention rate across 4 engines · 3 gaps confirmed · 3 fixes planned · DevTools / Project Management

This is a public diagnostic teardown based on publicly available website content. It does not claim implementation work, customer authorization, or measured business outcomes.

Audit Snapshot

86%

Mention Rate

6 of 7 prompts

4

Engines Tested

3

Gaps Confirmed

3

Fixes Planned

ComparisonEvidenceConsistency / Freshness

Executive Summary

Linear is partially visible to AI recommendation engines, but recommendation quality is limited by comparison gap issues. The highest-priority fix is to improve /compare/[product-vs-competitor] so AI engines can classify, compare, and recommend the product more confidently.

Key Findings

  • Across 7 prompt checks, the product was mentioned in 6 and absent from 1.
  • Mention rate by layer: discovery 3/3, comparison 1/2, purchase_intent 1/1, trust 1/1.
  • 2 high-severity gaps confirmed: Comparison Gap, Evidence Gap.
  • Jira dominates discovery queries across all 6 engines
  • Linear appears in 5/6 engines for discovery but only 3/6 for comparison
  • No engine can state Linear's exact starting price correctly

Linear — AI Recommendation Audit Case Study

Snapshot

Company Linear
Domain linear.app
Vertical DevTools / Project Management
Audit date March 20, 2026
Confirmed gaps 3
Top priority fix Comparison Gap

Before

Linear was partially visible to AI recommendation engines — mentioned in 6 of 7 prompt checks across major engines. However, comparison and evidence weaknesses limited how confidently engines could recommend the product. Jira dominates discovery queries across all 6 engines.

What We Found

Comparison Gap (High)
Comparison pages exist but lack structured verdict tables and per-feature fact rows. AI cannot easily extract Linear's differentiation in 'vs' queries.

Evidence Gap (High)
No named testimonials or case studies with measurable outcomes found on any public page. Trust signals rely on logo walls only.

Consistency / Freshness Gap (Low)
Pricing page and homepage both mention pricing but with slightly different framing. No last-updated dates on pricing or docs pages.

1. Comparison Gap

AI cannot easily compare the product against alternatives, reducing inclusion in shortlist and versus queries.

  • Create dedicated comparison pages → /compare/[product-vs-competitor] (M effort, High impact)
  • Add a 'How we compare' section to the homepage → Homepage (S effort, Medium impact)
  • State who the product is and is not for → Product / Features / Compare (S effort, Medium impact)

2. Evidence Gap

AI cannot find enough proof, testimonials, case studies, or verifiable claims to trust the product.

  • Add named testimonials with company and role → Homepage / About (S effort, High impact)
  • Link to external reviews or listings → Homepage footer / About (S effort, Medium impact)
  • Publish one customer case study with before/after outcomes → Case study / Homepage proof block (M effort, High impact)

3. Consistency / Freshness Gap

Public product information is outdated, contradictory, or not consistently repeated across key pages.

  • Audit key public pages for factual consistency → Homepage / About / Pricing / Docs (M effort, Medium impact)
  • Add visible last-updated dates on fact-heavy pages → Pricing / Docs / Compare (S effort, Low impact)
  • Archive or update stale blog posts that contradict positioning → Blog archive (M effort, Low impact)

What We'd Recheck Next

Once key fixes are published, we'd verify improvement by:

  • Re-run the same prompt set 7 days after key fixes are published.
  • Track whether AI engines cite updated pricing, trust, and comparison surfaces.
  • Compare recommendation quality before and after fixes.

Attribution Limits

AI engine outputs are non-deterministic and vary by session, region, and time. Static crawl findings may miss JavaScript-rendered content. Recommendation improvement cannot be attributed to a single page change without repeat checks.

Case study generated by EurekaNav on March 20, 2026.

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