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Prompt-to-Rank
Simulator

Enter any query and see the actual answers from ChatGPT, Gemini, and Perplexity — side by side, with every cited source.

Definition

What Is Prompt-to-Rank?

Direct Answer

Prompt-to-rank refers to the process of submitting a query to AI engines and identifying which brands and domains appear as cited sources in the generated responses. It is the AI equivalent of checking Google search results — except instead of a ranked list of links, you see the full answer that AI produces and exactly whose content the AI trusted to construct it.

In traditional SEO, “ranking” means appearing in position 1–10 for a keyword. In AI search, ranking means being cited in the AI's generated answer — a binary outcome where your domain either appears or it doesn't. There is no page 2 in an AI response. This makes prompt-to-rank monitoring critical for any brand operating in competitive informational markets.

The Simulator runs your query against ChatGPT, Gemini, and Perplexity simultaneously — the three platforms that collectively handle the vast majority of AI-mediated research queries — and returns the actual answers alongside the full list of cited sources, so you can see exactly who is winning AI search for your most important prompts.

The Problem

Flying Blind in AI Search

Without seeing actual AI responses, you don't know if your brand is being recommended — or if competitors are dominating the answers users actually receive.

No Visibility Into Who AI Recommends

Rank trackers show your position in Google's link list. They cannot show you whether ChatGPT recommends your brand, whether Perplexity cites your pages, or whether Gemini mentions a competitor every time a user asks about your category.

Citation Competitors Are Different From Rank Competitors

The sites that dominate AI citations for your topic are often not the same sites you track in traditional competitive analysis. They may rank lower in Google but earn more AI citations due to superior FAQ schema and direct-answer content structures.

Content Strategy Disconnected from AI Reality

Without seeing what AI engines actually say about your topic, content strategy is based on keyword data — not on the specific questions and sub-topics AI models are synthesising into answers. The fan-out queries reveal the real content gap.

// How to Use

Simulate AI Rankings in 3 Steps

01

Enter the Query You Want to Rank For

Type the question or prompt that users in your niche ask AI engines — e.g. "what is the best CRM for small businesses" or "how do I fix crawl budget issues". Use natural, conversational language as users actually phrase AI queries, not short-tail keyword format.

02

Compare Answers Across ChatGPT, Gemini, and Perplexity

Review the three AI answers side by side. For each model, note: (a) which domains are cited as sources, (b) whether your brand appears, (c) how competitors are framed. The "Cited Sources" list under each answer shows every domain the AI pulled from — these are your direct AEO competitors for this query.

03

Analyse Fan-Out Queries to Build Your Content Plan

The fan-out queries section reveals the sub-questions AI engines researched internally to answer your prompt. Each one is a content gap opportunity. If your domain doesn't have dedicated, high-quality content addressing each fan-out query, you're missing the coverage depth AI models need to cite you consistently.

// FAQ

Frequently Asked Questions