Dual-Mode Analysis
AI models behave differently when they search the web versus when they rely on training data. Dual-mode analysis tests both — so you see the full picture of your brand's AI presence, not just half of it.
Dual-mode analysis is a core capability of the AI Visibility Score. Every strategic question about your industry is sent to ChatGPT, Claude, and Perplexity twice — once with web search enabled, once with native knowledge only. The result is two separate scores that reveal whether your brand is discoverable in real time, embedded in AI memory, or both. The gap between those scores is the single most actionable insight in AI visibility today.
Two scores. Two realities. One decisive gap.
Your web search score measures real-time discoverability: when AI crawls the internet for fresh answers, does your brand appear? Your native score measures embedded reputation: has AI "learned" your brand from its training data, so it cites you even without internet access?
Most brands are strong in one mode and invisible in the other. A startup with great content might score 82 in web search but only 31 in native — AI finds them online but hasn't memorized them yet. An established enterprise might score 68 in native but 44 in web search — their historical reputation is solid, but their current site is underperforming. The gap tells you exactly where to invest next.
Without dual-mode analysis, you're optimizing blind. You might pour budget into content creation when the real problem is authority building — or vice versa. Two scores eliminate the guesswork.
Single-Mode Guessing vs Dual-Mode Clarity
Most tools test one mode. That's like checking only half the scoreboard. Here's what you miss.
Half the picture, wrong conclusions
You test how AI responds with web search and see a decent score. You assume everything is fine. But without testing native knowledge, you miss that AI doesn't actually "know" your brand — it just found you this time. If a user disables search, or uses a model without internet access, you vanish completely.
Worse, you optimize for the wrong mode. You keep publishing blog posts when the real gap is authority and press coverage. Budget wasted, months lost, competitors pulling ahead in the mode you're not measuring.
Complete visibility, precise action
You see both scores side by side. The gap immediately tells you what to do: a web-native gap means invest in authority building (press, Wikipedia, high-DA backlinks). A native-web gap means fix your current content and Schema.org. Equal scores mean balanced presence — maintain and grow both.
Every question is tested in both modes across ChatGPT, Claude, and Perplexity. You see per-question, per-model breakdowns. Your strategy becomes surgical: target the exact questions and modes where you're weakest, track improvement with each re-scan.
Per-model breakdown: each AI treats modes differently
ChatGPT with search enabled pulls from Bing results and cited sources. Claude prioritizes its training corpus even with web access. Perplexity is search-first by design. Each model's dual-mode behavior is unique — and your visibility varies accordingly.
LLMRanky tests all three models in both modes, giving you a 6-cell matrix: ChatGPT-search, ChatGPT-native, Claude-search, Claude-native, Perplexity-search, Perplexity-native. You see exactly which model-mode combinations are strong and which need work. A brand might dominate Perplexity search but be absent from Claude's native memory. That's a specific, fixable gap — not a vague "improve visibility" recommendation.
The per-model view also reveals which AI platform your customers use most. If your audience skews toward ChatGPT, prioritize your ChatGPT scores. If you're in research or tech, Claude matters more. Allocate effort where it counts.
How Dual-Mode Analysis Works
A controlled scientific comparison — same question, same model, two contexts — to isolate the exact impact of web access on your visibility.
1. Questions generated from your site
We crawl your website, extract your brand profile, products, competitors, and keywords. From this we generate 30–100 strategic questions your customers are asking AI right now — the queries that drive purchase decisions in your industry.
2. Each question sent twice per model
Every question goes to ChatGPT, Claude, and Perplexity with web search enabled — then again with native knowledge only. Same wording, same model, different access. This controlled test isolates the variable: does web access change whether AI recommends you?
3. Responses scored independently
Each response is analyzed for mention presence, position, accuracy, sentiment, and competitor citations. You get a web search score and a native score per question, per model — then aggregated into your dual-mode dashboard with gap analysis.
4. Gap analysis drives your roadmap
The gap between modes becomes your action plan. Large web-native gap? Build authority via press and Wikipedia. Large native-web gap? Fix Schema.org and publish fresh content. Small gap? You're balanced — maintain and grow. Specific, measurable, actionable.
See Both Sides of Your AI Visibility
Your brand has two AI reputations — one built in real time, one embedded in memory. Dual-mode analysis reveals both in 2 minutes. Free scan, no credit card required.
Run Dual-Mode Analysis →