Live AI Visibility Probe — RankAudit ChatGPT/Claude/Perplexity/Gemini
RankAudit's Live AI Visibility Probe tests 5 prompts across 4 AI engines and reports brand mentions. Learn how to read it and improve your AI brand exposur
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Introduction
Traditional rank trackers report your position in Google search results. The Live AI Visibility Probe inside RankAudit reports something different: how often your brand actually appears in AI-generated answers when real users ask real questions. This is the modern equivalent of a SERP check, designed for an era where many users never see a SERP at all.
This article walks through the Probe panel using the rankar.ai audit, explains each of the 5 test prompts, the 4 engines tested, the score interpretation, and the playbook for improving your brand mentions count.
Where the Probe Lives
Open RankAudit, run an audit, click the AI Readiness tab. The top section of the tab is labelled LIVE AI VISIBILITY PROBE and shows a count like 3 / 20 mentions for rankar.ai.
The number means: across 5 test prompts × 4 AI engines = 20 total opportunities to be mentioned. Your domain was mentioned in 3 of them.
The 4 AI Engines Tested
RankAudit probes the four most-used AI search surfaces:
ChatGPT — OpenAI's flagship. Tests run against the consumer model. Claude — Anthropic's assistant. Tests run against the standard Claude model. Perplexity — search-focused AI with explicit citations. Gemini — Google's AI surface, increasingly visible inside Google's own SERPs as AI Overviews.For rankar.ai the engine scores are: ChatGPT 1/5, Claude 1/5, Perplexity 1/5, Gemini 0/5. Three engines recognise the brand on the direct-discovery query; Gemini does not yet.
The 5 Test Prompts
Each prompt is engineered to mirror a different user intent type:
Prompt 1: "What is Rankar and what does it offer?" — direct brand discovery. The user already knows your name and is asking what you do. A high score here means LLMs have indexed your brand entity. rankar.ai: 3/4. Prompt 2: "How do I choose the right software?" — category discovery. The user does not know your brand yet; they are looking for advice in your category. A high score here means LLMs surface you in unprompted recommendations. rankar.ai: 0/4. Prompt 3: "What are the best software brands to buy in 2025?" — top-list query. The user wants a ranked list. A high score here means LLMs include you in best-of lists. rankar.ai: 0/4. Prompt 4: "Rankar vs other software options — which is best?" — comparison query. The user is comparing brands. A high score here means LLMs cite you in comparisons. rankar.ai: 0/4. Prompt 5: "Where can I find software online or near me?" — local/availability intent. A high score here means LLMs surface you for procurement queries. rankar.ai: 0/4.The five prompts together cover the realistic spectrum of user queries that produce brand exposure.
The Captured Quote
Below the score, RankAudit shows the actual passage where your domain was mentioned. For rankar.ai the captured quote from ChatGPT (position 1):
> "Certainly! Let's address each of your questions: Q1: What is Rankar and what does it offer? Rankar is an AI-powered SEO platform designed for agencies, publishers, eCommerce brands, and freelancers. It integrates 12 tools into a single..."
Reading the actual quote matters because LLMs sometimes describe brands accurately, sometimes vaguely, sometimes wrong. The probe lets you confirm exactly how each engine describes you so you can fix any inaccuracies in your source content.
How to Improve Probe Score
The probe is informational, not diagnostic — to fix issues, work through the AI Readiness checklist below it. But the probe pattern tells you what to focus on:
High on direct-brand, low everywhere else (the rankar.ai pattern). Your brand entity is clear, but you are not yet competing for category-level recommendations. The fix is topical authority — write deep guides on the category's most-searched questions and submit them. LLMs will eventually pick them up. Low on direct-brand, low everywhere else. Your brand entity is not yet recognised. The first fix is to deploy Organization schema, an llms.txt file, and an authoritative About page. The audit's "Can AI parse you?" section walks through each. High on direct-brand, high on category, low on comparison. You compete in your category but not against named rivals. The fix is to publish comparison content that explicitly compares your product to named alternatives. LLMs will cite the comparisons when asked. High on direct-brand, low on local intent. Your category does not have strong local intent (most B2B SaaS) and you do not need to optimise for it. Ignore.The "Try RankAIO" Cross-link
At the bottom of the Probe, RankAudit shows: "For continuous citation tracking → Try RankAIO". RankAIO is the Rankar.ai module for ongoing AI citation tracking. The Probe is a snapshot; RankAIO is the dashboard. Use the Probe to spot-check during an audit, then graduate to RankAIO when you want to track AI mentions weekly.
What's Next
The Probe is decoded. The next article moves to the AI crawler accessibility checks — making sure GPTBot, ClaudeBot, PerplexityBot, and Google-Extended can actually reach your content before any of this analysis kicks in.
Apply This With the Rankar Toolkit
RankAudit works best paired with the rest of the Rankar suite. Spin up the relevant tools: RankTalk • RankOps • RankAudit • RankWriter • RankTracker • RankAIO • RankBridge • RankLinks • RankLocal • RankLaunch • RankSpy • RankUX • RankLead. Each module shares data with the others — fewer tabs, one source of truth.