Preview How AI Engines Will Answer
Any Query Before You Create Content

The simulator models how major AI engines respond to any query: what format the answer takes, which sources are cited, what content signals those sources have, and what information gaps exist in the current answer. This intelligence guides content creation before you invest editorial time — so every piece you create has a specific, identified pathway to being cited.

An information gap is a specific piece of information that users would expect an AI answer to include for a query, but that current cited sources do not adequately cover. Content that fills this gap has a high probability of earning AI citation because it provides unique, valuable information that existing cited sources lack.

ChatGPT, Perplexity, and Google AI Overview — the three engines with the largest current user bases. The engines differ in citation preferences: ChatGPT favours high domain authority and comprehensive coverage, Perplexity weights recency more heavily, and Google AI Overview strongly weights pages already ranking in the top 5 organically.

Run simulations across all your target queries before building your content calendar. Queries with identified information gaps should be prioritised as first content investments. Queries where the citation landscape is saturated by very high-authority competitors should be scheduled later, once your domain authority in the relevant cluster is established.

The simulator accurately models the citation patterns and information gap landscape for 87% of queries based on validation against real AI engine responses. For rapidly evolving topics, the simulation represents the patterns of the last 30 days of AI response behaviour.

Yes. Fully available on the free plan with 15 runs per month. Each run provides answer format analysis, citation pattern breakdown, information gap identification, content specification, and competitive positioning strategy.

Citation landscapes change at different rates depending on topic type. Breaking news and trend-based queries can shift within days. Product and software recommendation queries typically shift when major new entrants publish strong content or established sources update — usually over weeks to months. Informational and definitional queries are the most stable — changing only when fundamental new information emerges. For competitive strategic queries, re-simulating monthly gives you early warning of competitive moves before they affect your citation rates.

Yes. The query analysis includes traditional SERP feature detection alongside AI citation pattern analysis. For queries with featured snippet opportunities, the content specification notes the direct-answer format required for snippet eligibility — which is typically the same format that produces AI citations. Optimising for featured snippets and AI citations simultaneously is usually more efficient than treating them as separate optimisation targets.

A query with no current stable AI answer represents an emerging opportunity. It means AI engines have not yet converged on preferred citation sources, so early high-quality content has a first-mover advantage. The simulator identifies these emerging queries and flags them as high-priority targets — the citation landscape is still forming and a well-optimised piece published now can establish early authority before competitors discover the opportunity.

The content specification adapts to your domain's current authority level. For lower-authority domains, it emphasises information gain differentiation and niche-specific expertise signals that can compete without high DA. For higher-authority domains, it identifies where comprehensive coverage and schema implementation can consolidate an already strong position. The citation threshold analysis shows the realistic entry requirements for your specific domain profile.