Jira is more visible in AI search across this prompt set, by 8 citations.
The finding in one paragraph
Software engineering teams choosing a project management tool increasingly start with a prompt to an AI assistant before clicking through to product pages. We tested 5 standardized buyer questions any engineering leader might plausibly type into ChatGPT or Claude, ran each one 3 times per engine across 4 engines, and counted how often Linear and Jira each appeared in the responses. The result, the per-engine breakdown, and a per-prompt comparison are below.
Per-engine breakdown
| Engine | Linear | Jira | Edge |
|---|---|---|---|
| ChatGPT | 3 / 15 | 5 / 15 | Jira by 2 |
| Claude | 11 / 15 | 8 / 15 | Linear by 3 |
| Gemini | 0 / 15 | 1 / 15 | Jira by 1 |
| Perplexity | 7 / 15 | 15 / 15 | Jira by 8 |
Per-prompt comparison
Each prompt was asked 12 times (4 engines × 3 trials). The cells show how often each brand showed up in those 12 responses.
| Buyer prompt | Linear | Jira |
|---|---|---|
| What is the best issue tracking and project management tool for software engineering teams? | 2 / 12 | 6 / 12 |
| What are the modern alternatives to Jira for fast-moving startups? | 4 / 12 | 3 / 12 |
| What project management tools do high-performance engineering teams use in 2026? | 5 / 12 | 6 / 12 |
| How should a Series B SaaS startup choose between Linear and Jira? | 8 / 12 | 10 / 12 |
| What is the best agile sprint planning tool for product and engineering teams? | 2 / 12 | 4 / 12 |
What the full audit would fix for a online-only / SaaS brand
The head-to-head above measures citation share on a single shared prompt set. The paid SXO Audit produces specific, copy-paste-ready fix recommendations across the categories below. Counts are the approximate number of distinct fix recommendations the audit pipeline can produce per brand; the exact count varies by site.
| Fix category | Fix recommendations |
|---|---|
| Core fix categories (every audit) | |
| AI Search Visibility (LLM citations across 4 engines) | 8 |
| Schema Markup & JSON-LD (Organization, Product, FAQPage) | 6 |
| Buyer-Prompt-Matching FAQ + Content | 5 |
| Technical SEO Foundations (crawlability, render, canonicals) | 7 |
| Knowledge Graph & Entity Signals | 4 |
| Citation Footprints (third-party signals) | 5 |
| Core Web Vitals & Performance | 4 |
| Accessibility (WCAG 2.1 AA) | 5 |
| Core subtotal | ~44 |
| Total fix recommendations per brand | ~44 |
Every Web Cited audit produces this set of fix recommendations regardless of business type. Storefront and service-area brands get additional category coverage on top of these, reflecting the location-presence signals AI assistants consult for those business models. The Playbook ships every fix as a copy-paste-ready remediation with the file, code, or content change spelled out.
Methodology
Identical to the Web Cited AI Visibility Index methodology. Replicable by anyone with API access to the four engines.
Anthropic Claude (claude-haiku-4-5-20251001)
Google Gemini (gemini-2.5-flash-lite)
Perplexity (sonar)
max_tokens 800
N = 3 trials per (engine, prompt)
The 5 standardized buyer prompts
- What is the best issue tracking and project management tool for software engineering teams?
- What are the modern alternatives to Jira for fast-moving startups?
- What project management tools do high-performance engineering teams use in 2026?
- How should a Series B SaaS startup choose between Linear and Jira?
- What is the best agile sprint planning tool for product and engineering teams?
Want to know how your brand stacks up?
Web Cited runs the same measurement against your domain, your competitors, and your top buyer prompts. Citation Monitor reads those prompts every week and ships a click-to-copy Playbook your engineers ship from in the next sprint, from $49/mo.
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