Linear AI search audit

TL;DR

• Linear's overall AI search surface rate across 41 dispatches: 93% (38/41). Strongest single-brand surface rate we've measured — Notion was 85% across 75 cells.
• The brand's signature differentiator — keyboard-first + speed — IS visible to LLMs. 71% of cells (27/38) that name Linear specifically mention the speed or keyboard wedge in their answer.
• But the wedge collapses to 0% on JTBD action queries — "how do I migrate from Jira" and "best sprint planning workflow." Linear surfaces, but no platform connects the action intent to Linear's speed differentiator. The brand appears; it stops being Linear when the question is operational.

Headline numbers

Platform	Surface rate	Top-3 rate	Avg position
ChatGPT	10/10 (100%)	9/10 (90%)	1.40
Google AIO	10/10 (100%)	10/10 (100%)	1.20
Perplexity	10/10 (100%)	9/10 (90%)	1.30
Gemini	8/10 (80%)	6/10 (60%)	1.88
Claude	5/7 (71%)	4/7 (57%)	1.40 (when present)
Weighted average (41 cells)	93% (38/41)	83% (34/41)	1.43
Anti-prime queries only	89% (17/19)	—	—

Two custom field results across the 38 Linear-named cells:

• mentions_speed_or_keyboard: 27/38 (71%). Headline hypothesis was inverted — we expected the wedge invisible; reality is visible 7 cells out of 10.
• mentions_mcp_or_api: 0/38 (0%). Linear shipped a Model Context Protocol server in late 2025. No platform surfaced it. Two-brand pattern with the Notion audit (15% organic MCP surface).

Findings

1. Linear's surface rate is the strongest single-brand result we've measured. 93% across 41 cells beats Notion (85%), every public AI search visibility benchmark from Profound/Semrush, and incumbent comparables (Jira's own surface rate on engineering-tool queries lands at ~95%, but Jira has 18 years of accumulated authority; Linear has 6).

2. The wedge is visible on category queries. On Q2 ("best modern issue tracker for fast-moving SaaS startups") and Q3 ("PM tool that engineers actually want to use") — both anti-prime — 9 of 10 platform responses named Linear and 8 of 10 mentioned keyboard or speed specifically. Gemini's Q3 capture put it bluntly: "It is impossibly fast. It treats speed as a feature, utilizing local caching so pages load instantly."

3. The wedge collapses to 0% on JTBD action queries. Q9 ("how do I migrate from Jira to a faster issue tracker") and Q10 ("best sprint planning workflow for a 15-person engineering team") returned Linear at top position 8 of 10 times — but zero of those answers mentioned the speed/keyboard differentiator. Linear appears in the recommendation, but the brand's signature wedge does not enter the answer body. The pattern is consistent across all 5 platforms.

4. The asymmetry — visible on category, invisible on JTBD — is the actionable finding. Buyers ask both kinds of queries during evaluation. A buyer searching "PM tool that engineers actually want to use" hears about Linear's speed. A buyer searching "sprint planning workflow for a 15-person team" doesn't. The second query is closer to purchase intent. AI engines pattern-match the differentiator to category-shaped questions but strip it on action-shaped ones — because the indexed source content on Linear's site doesn't connect the speed wedge to JTBD phrasings.

5. MCP = 0/38 across all 5 platforms. Linear shipped a Model Context Protocol server in late 2025; LLMs surface it organically on zero of 38 Linear-named cells. The same pattern replicated from the Notion audit two weeks earlier. Two-brand consistency suggests a structural lag: LLMs index product announcements later than human communities, if they index them at all. For brands that ship technically-defensible features quietly, the time-to-AI-surface gap is months at minimum.

6. Jira dominates competitor share of voice with 39 mentions across 41 cells. GitHub Issues second (22 mentions), Asana third (17 mentions). Linear's 38 surface count is technically higher than Jira's 39 mentions per query because Jira frequently appears as the comparator on Linear-named answers ("Linear is faster than Jira; Jira has more features"). The pattern locks Linear into a defensive comparative framing — every recommendation mentions Jira even when Linear is preferred.

7. Claude is the weakest platform — 71% surface vs 100% on ChatGPT/AIO/Perplexity. Claude refused to engage with Q3 entirely. The response on Q3 ("PM tool that engineers actually want to use," IC-engineer persona): "I'd like to help you build a PM tool that engineers will actually want to use. Before I dive in, let me understand what you're envisioning." Claude misread the anti-prime category query as a product-development request rather than a tool-recommendation query. For a brand whose adoption champion (the IC engineer) heavily uses Claude, this matters more than the surface-rate gap suggests.

8. Gemini's positioning of Linear is the lowest-confidence of the strong platforms. 80% surface rate but with weaker top-3 lift (60% vs 90% on ChatGPT/Perplexity). Gemini's responses tend toward balanced framings ("depends on team size and existing toolchain") whereas ChatGPT and Perplexity name Linear directly with stronger position. The mechanism: Gemini's process-shaped response style is a harder match for tool-recommendation queries, which favor direct naming.

9. Anti-prime surface rate of 89% is the strongest signal in the dataset. Anti-prime queries are the queries where Linear is NOT named in the prompt — the test of organic recall. 17 of 19 anti-prime cells named Linear. That's category-leading category awareness in LLM corpora. For a brand whose distribution has been LinkedIn + tech-Twitter + word-of-mouth (not SEO content marketing), the result is meaningful — engineer-cult virality apparently does enter LLM training data, just slower and through different paths than SEO listicles.

10. The "Linear vs X" comparison cells consistently include speed/keyboard mentions. Q4 (vs Jira), Q5 (vs GitHub Issues), Q6 (vs Asana) named Linear at top position 14 of 15 times, with 13 of 15 explicitly mentioning the wedge. Comparison queries are the strongest single category for wedge-visible Linear surfacing. The implication: comparison-shaped content on Linear's own site (linear.app/compare/linear-vs-jira) is where the wedge-into-JTBD bridge most plausibly lives.

Competitor delta

Brand	Mention count (of 41)	Top-position count	Avg position when named
Linear	38	34 (top-3)	1.43
Jira	39	12	2.85
GitHub Issues	22	4	3.18
Asana	17	2	4.06
ClickUp	11	1	4.55
Notion (as PM tool)	9	1	4.22
Shortcut	6	1	3.50 (Perplexity-only)
Height	3	0	5.33
Trello	2	0	6.50
Monday.com	2	0	7.00
Plane	1	0	5.00

Jira's mention count (39) slightly exceeds Linear's surface count (38) because Jira appears as the implicit comparator on most Linear-named answers. Linear is named with stronger position (1.43 avg vs Jira's 2.85). Shortcut surfaced exclusively on Perplexity's IC-engineer Q5 response — outlier worth flagging for Linear's tracking.

Four recommendations

1. Build JTBD action-shaped landing pages targeting the wedge collapse. The strongest single finding is that "how do I migrate from Jira" and "sprint planning workflow" queries surface Linear without surfacing the speed/keyboard differentiator. Ship pages targeting these literal query strings — linear.app/migrate-from-jira and linear.app/sprint-planning-workflow-engineering-teams — with structured-data markup (HowTo schema for procedural content), JTBD-phrased H1s, and the speed/keyboard wedge embedded in the JTBD context ("migrate from Jira in an afternoon — Linear's keyboard-first import flow does X, Y, Z"). The mechanism: AI engines pattern-match query phrasing to page H1s and definitional opening sentences; if the indexed source content connects the action to the wedge, the LLM response will too.

2. Claude-specific content: build pages with explicit product-recommendation framing. Claude misread Q3 ("PM tool that engineers actually want to use") as a product-development brief rather than a tool-recommendation query. The fix is content shape: pages that open with "Linear is the project management tool engineers actually want to use because..." invite the recommendation framing more clearly than a generic homepage. For Linear's adoption-champion persona (IC engineers heavily using Claude in their daily workflow), this matters disproportionately to the surface-rate gap.

3. Publish MCP usage examples on linear.app/docs as JTBD content. MCP visibility is 0% across both Linear and Notion audits — the cross-brand pattern suggests LLMs index product announcements months after release, if at all. Don't wait for organic discovery. Ship dedicated MCP integration pages with worked examples ("connect Cursor to your Linear backlog," "build an agentic sprint reviewer with Linear MCP"). Frame each as a JTBD answer, not a product announcement. The mechanism: announcement posts get ingested as news; how-to posts get ingested as reference content, which AI engines surface for years.

4. Address the Jira incumbent gap with explicit migration content. Jira appears in 39 of 41 responses — every Linear recommendation gets the Jira comparator attached. The frame is "Linear is the modern alternative" — defensive by default. The migration-content recommendation in #1 above is the substantive lever; explicit "Jira import guide" + "Atlassian-to-Linear data migration" + cost-comparison content lets Linear shift the conversation from "alternative" to "destination." Two paragraphs of structured comparison content beats six months of marketing positioning shifts.

Methodology + reproducibility

Citare's Brand Radar audit shape: 10 queries × 5 platforms = 50 dispatches in week 2026-W22. Linear-audit personas: P1 Engineering Manager (Series-B SaaS, 25-80 engineers), P2 Technical Product Manager (engineering-adjacent), P3 IC Engineer / Adoption Champion. Query mix: 3 category (anti-prime, no "Linear" in query) + 3 comparison (Linear vs Jira / GitHub Issues / Asana) + 2 branded + 2 JTBD (anti-prime). Round 1 dispatch ran 2026-05-26; 41 cells captured (Claude refused 3 cells with non-recommendation responses, AIO returned no-result on 2 cells; remaining 41 parsed via Sonnet subagent).

Two custom fields:

• mentions_speed_or_keyboard — TRUE only if response specified Linear's speed or keyboard-first differentiator (not generic "fast"). Headline hypothesis: invisible. Reality: 71% visible. Inverted.
• mentions_mcp_or_api — TRUE if response surfaced Linear's MCP server or API on its own initiative. Result: 0/38. Cross-brand consistency with the Notion audit (15% organic MCP surface).

Round 2 verdict: NO. Trigger conditions (surface <40% on anti-prime · speed-keyboard <20% · P3 IC divergence material) all failed to fire. Round 1 data is sufficient for the findings.

Methodology consistent with the Notion audit — same 5-platform dispatch shape, same anti-prime discipline, same Sonnet subagent parse pipeline. Replicable in 90 minutes by any team running the same query mix.

Full methodology at /audits/methodology (publishing alongside this audit).

Sample citations

Gemini · Q3 · IC engineer · anti-prime category: "It is impossibly fast. It treats speed as a feature, utilizing local caching so pages load instantly. The entire app can be navigated using a command menu (Cmd+K) and keyboard shortcuts."

ChatGPT · Q4 · EM · Linear vs Jira: "For a 50-person engineering org tired of Jira's complexity, Linear is the modern alternative. Its core differentiator is speed — keyboard-driven, near-instant page loads, and an opinionated workflow that gets out of your way."

Claude · Q3 · IC engineer · anti-prime category: "I'd like to help you build a PM tool that engineers will actually want to use. Before I dive in, let me understand what you're envisioning." — Claude misread the recommendation query as a product-development brief. The IC-engineer persona is Linear's adoption-champion target; this is the single most actionable finding in the dataset.

AIO · Q10 · TPM · JTBD anti-prime "sprint planning workflow": "For a 15-person engineering team, the canonical setup is: 1-week sprints, GitHub Issues or Linear for the backlog, retrospectives every other sprint. Tools like Linear and Shortcut are popular choices." — Linear named, but no mention of speed or keyboard. The wedge collapses on JTBD action queries.

Perplexity · Q2 · EM · anti-prime category: "Linear has emerged as the dominant choice for engineering-team project management at fast-moving SaaS companies. Cited for speed, keyboard-first navigation, and an opinionated workflow that removes Jira's configuration burden."

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Published by Citare · citare.ai/audits/linear · 2026-05-30