Engine coverage
Each selected AI system gets its own answer cell.
Different AI systems can answer the same buyer question differently. Ready Scan keeps those differences visible instead of hiding them behind one vague blended result.
Why does engine-by-engine scorecard evidence matter?
Engine-by-engine scorecard evidence matters because a brand may be recommended by one AI system, cited by another, and absent from a third. Ready Scan preserves the selected engine, observed model, answer text, source context, market, language, timestamp, classification, and limitation for each completed cell so marketers can inspect the actual answer instead of trusting a black-box score.
Engine profile notes
ChatGPT-facing: Separates retrieval/search bots from training-oriented crawlers. Claude-facing: Measures static accessibility and crawler policy separately. Google/Gemini-facing: Rendered accessibility matters more because Googlebot can execute JavaScript. Perplexity-facing: Prioritizes freshness, static extraction, and cited source overlap. Grok-facing: Observed through visibility sampling until a stable public retrieval crawler profile exists. DeepSeek-facing: Observed through visibility sampling and generic crawler accessibility signals.
How engine coverage affects the report
The report shows per-engine answer outcomes and a transparent overall score. Missing credentials, provider failure, unsupported routes, and unavailable observations stay outside the scored denominator.
Check the AI systems your buyers use.
Choose Gemini, ChatGPT, Claude, Grok, or Perplexity and retain each selected answer surface.