Live Citation Test: Does AI Actually Mention You?

The live citation test evaluates whether AI engines actually cite your domain when users ask questions relevant to your business. Unlike the hallucination audit, which asks about your business by name, the citation test uses generic topic queries -the kind your target audience asks without mentioning any specific company. These are the queries where AI citation translates directly into business visibility.

Test queries are crafted to match your business's target search intent. For a customer support software company: "What is the best live chat software for small businesses?", "How do I set up a chatbot for my website?", "What features should I look for in a help desk solution?" For a patient advocacy organization: "How do I find a patient advocate?", "What does a patient advocate do?", "How much does patient advocacy cost?"

For each query, the test captures the full AI response including all citations, source references, and linked domains. Then it evaluates five dimensions. Citation presence -does your domain appear at all? Page accuracy -which specific pages does the AI reference, and are they the most relevant? Content accuracy -does the AI accurately represent what your cited pages say, or does it distort your content? Citation prominence -where in the response does your citation appear? Primary source or footnote? Competitive position -which other domains are cited, and how does your citation rate compare?

The test runs across multiple engines because citation behavior varies significantly. Perplexity cites sources more frequently and visibly than ChatGPT. Claude handles inline citations differently than either. A domain might be prominently cited by Perplexity but completely absent from ChatGPT's response to the same query. The multi-engine approach reveals where you're winning and where you're invisible.

AI citation is the new organic search result. When users get answers from ChatGPT or Perplexity instead of Google, the only way to appear is to be cited. But unlike search engine rankings -which you can check with any SEO tool -AI citation is invisible until you run actual queries and examine responses. There's no equivalent of Google Search Console for AI citations.

The live citation test is the only way to answer the question that matters most: "When my target audience asks AI about my topic, does the AI mention me?" A perfect technical AEO score and flawless content depth mean nothing if the end result is zero citations. The live test measures the outcome directly.

AI-level testing also reveals the gap between expectation and reality. Many businesses assume that because they rank well in Google, AI engines will also cite them for similar queries. That assumption is frequently wrong. Google rankings and AI citations use different signals. A page ranking #1 in Google for "best live chat software" might not be cited at all by ChatGPT, which may prefer a source with better structured data, deeper content, or stronger entity authority. The live citation test eliminates guesswork by measuring actual behavior.

The competitive dimension is equally valuable. Knowing you're not cited for a query is important. Knowing that Tidio (63) is cited instead, and that Tidio's cited page has specific characteristics your page lacks -that's actionable intelligence. The citation test doesn't just tell you where you stand. It tells you who's beating you and why.

The live citation test follows a structured methodology. Phase one -query development. Based on your business's target audience and content coverage, the system generates 20-30 test queries spanning different intent types: informational ("What is..."), comparative ("Which is better..."), navigational ("How do I find..."), and commercial ("What does X cost?"). Queries are refined to match natural language patterns real users employ.

Phase two -query execution. Each query gets submitted to ChatGPT (current production model), Claude, and Perplexity. For engines supporting web search or browsing, those capabilities are enabled to simulate real user behavior. Responses are captured in full, including all inline citations, footnote references, source links, and domain mentions.

Phase three -response analysis. An AI model examines each response and extracts every domain cited, specific URL referenced, context of the citation, and accuracy of information attributed to that source. Your domain's citations get highlighted -which page was cited, whether the citation was a primary source or supporting reference, whether the AI accurately represented your content.

Phase four -competitive comparison. For each query, the system identifies all domains cited across all engines and ranks them by citation frequency, prominence, and accuracy. This produces a competitive citation matrix showing which domains dominate which query types and which engines.

Phase five -gap analysis. Queries where your domain wasn't cited get examined to determine why. The AI model compares your most relevant page against pages that were cited and identifies their specific advantages -better structure, more specific data, stronger author credentials, or simply more content on the topic. These gap analyses produce the most actionable recommendations in the entire Intelligence Report.

The output: citation rate (percentage of queries producing citations to your domain), per-engine breakdown, per-query results table, competitive rankings, and specific page-level recommendations for improving citation rates where you're currently absent.

Citation rate is the headline metric. Above 40% for your target queries means strong AI visibility -nearly half of relevant queries cite your domain. Above 60% is exceptional, typically seen only in domains with dominant market positions, extensive content, and strong entity authority.

Between 15-40%: moderate AI visibility. You appear in some responses but are absent from many. The per-query breakdown will show patterns -you may dominate informational queries but be absent from comparative ones, or be cited by Perplexity but not ChatGPT. These patterns indicate where your content or technical optimization falls short for specific query types or engines.

Below 15%: minimal AI visibility for your target queries. This usually signals a fundamental issue -either your content doesn't sufficiently address the queries your audience asks, your technical AEO score is too low for AI crawlers to discover your content, or your entity authority is insufficient for AI engines to trust and cite your pages. The gap analysis pinpoints which factor is the primary bottleneck.

Per-engine citation variance is a critical insight. If Perplexity cites you for 50% of queries but ChatGPT cites you for 5%, there's an engine-specific gap. Perplexity heavily weights web crawl results while ChatGPT relies more on training data supplemented by search. This gap might mean your content is crawlable but not in ChatGPT's training data, or that ChatGPT prefers different content characteristics. Engine-specific gaps above 10 percentage points trigger the cross-engine consistency analysis.

The competitive citation matrix often reveals surprising competitors. For a patient advocacy query, you might expect to compete with other advocacy organizations -only to discover a health insurance company's content page earns more citations. Understanding who AI engines consider your competitors for specific queries helps you target optimization. If a government health agency is consistently cited for your target queries, you need a different strategy than if a peer organization is winning.