Free AI Appearance Check

Measuring How AI Engines Decide Which Brands to Recommend.

AppearAI tracks how often AI engines recommend a brand, surfaces which competitors dominate and why, and maps the signals that drive citation rates across ChatGPT, Perplexity, Gemini, and Claude.

Free · No signup required · Takes 30–60 seconds
Running your AI Appearance Check
Checking technical signals · Analysing discovery prompt patterns · Identifying competitors
Technical AI Readiness
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Analysing your site…
Signals checked
7
Website tested
—
Discovery prompts run
20
Discovery Prompts Tested
Appeared in — out of 20 discovery prompts run across 5 AI engines
Who Appeared Instead
Brands appearing in the same prompts where this brand did not
Your next step
Request a full analysis

The scan shows your baseline AI Appearance Rate and technical signal audit. The full analysis covers your complete citation rate across all four engines, the sources driving competitor visibility, and a ranked breakdown of what is holding your score back.

What the full report covers
✓Complete AI Appearance Rate across ChatGPT, Perplexity, Gemini, and Claude
✓Competitor consensus gap — which sources are driving their visibility
✓Full technical signal audit across seven checks with ranked remediation priorities
✓All 20 discovery prompts with per-engine appearance data
✓Priority action plan ranked by expected impact on your score
Request a Full Analysis →
hello@appearai.io
89.8%
of brands had zero AI mentions across eight platforms in Q1 2026. Victorious, Q1 2026 Quarterly Search Report.
58%
of consumers now turn to AI tools for brand recommendations. Trustpilot consumer research, April 2026.
42%
better conversion rate from AI-referred buyers versus traditional organic search visitors. Adobe Analytics, March 2026.
What AppearAI measures

What determines whether an AI engine recommends one brand over another?

That is the question AppearAI was built to answer. The platform runs structured prompt sets across multiple AI engines and multiple runs to measure citation frequency, surface dominant competitors, and identify the signals driving visibility differences between brands in the same category.

The methodology draws on primary research from Princeton, Ahrefs, Seer Interactive, and Muck Rack — tested against live citation behaviour across ChatGPT, Perplexity, Gemini, and Claude.

Read the AAR Methodology →
01
AI Appearance Rate
A frequency-based metric calculated across 20 buyer intent prompts, multiple runs, and four engines. The more runs, the clearer the distinction between brands AI consistently trusts and those appearing by chance.
02
Technical Signal Audit
Seven technical checks covering AI crawler access, structured data, schema markup, llms.txt, meta signals, content structure, and HTTPS — the infrastructure layer that determines whether AI crawlers can read and cite a brand at all.
03
Competitor Consensus Intelligence
For every prompt where a brand does not appear, the platform identifies which competitors do and traces the specific sources driving their citations — surfacing the independent consensus gap between brands in the same category.