Answer-Engine Types
Build the exact JSON-LD shapes AI Overviews, Copilot, and Perplexity favor — QAPage, speakable, ClaimReview, and Dataset — not generic article markup.
AI Answer Engine Schema Builder
TL;DR : AI Answer Engine Schema Builder checks build qapage, speakable, claimreview, and dataset json-ld for answer engines. so you can confirm the current issue, understand when it matters, and move directly into the next fix without leaving the browser.
Generate the JSON-LD that AI Overviews, Perplexity, and voice assistants actually read.
Mis à jour
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QAPage with accepted + suggested answers speakable cssSelector / xpath builder ClaimReview fact-check skeleton Dataset provenance markup Per-engine support notes
What does AI Answer Engine Schema Builder do?
The AI Answer Engine Schema Builder generates JSON-LD for the four structured-data shapes that answer engines lean on but general schema tools rarely cover well: QAPage for a single accepted answer, speakable for voice and read-aloud passages, and the provenance types ClaimReview and Dataset. Pick a type, fill guided fields, and copy valid, ready-to-paste markup with the correct @context.
Why it matters for AEO and GEO: Google AI Overviews, Bing Copilot, Perplexity, and ChatGPT search decide what to quote partly from machine-readable signals. QAPage hands them a verified answer, ClaimReview hands them a trust rating, and Dataset hands them a citable source for your numbers — all of which raise your odds of being the cited result.
Community and Q&A pages: Use QAPage to mark up one user question with an accepted answer plus suggested answers, the shape answer engines treat as a high-confidence extractable passage.
News and voice publishers: Add speakable cssSelector or xpath markup so Google Assistant can read your summary aloud on voice surfaces.
Fact-checks and research: Ship ClaimReview to publish an explicit truth rating on contested claims, and Dataset to expose the licensed source behind your statistics so engines attribute figures back to you.
When should you use AI Answer Engine Schema Builder?
AI Answer Engine Schema Builder is most useful when you need a direct answer on a live URL or draft before you change templates, ship content, or rerun a wider audit.
Comment utiliser cet outil
- 1 Pick a schema type — QAPage, speakable, ClaimReview, or Dataset.
- 2 Fill the guided fields; the JSON-LD updates live and flags missing required properties.
- 3 Copy the generated script tag into your page head.
- 4 Confirm it parses cleanly with the Structured Data Validator.
What should you do next?
After building your markup, validate it for parse errors and check how extractable your answers are for AI retrieval. Then move to the related checks below to confirm the fix on the live canonical page.
Ce que vous obtenez
Live Validation
Required properties are checked as you type, so you copy well-formed JSON-LD with the right @context every time.
Per-Engine Guidance
Each type shows which engines actually consume it and how, so you only ship markup that earns a citation.
Frequently Asked Questions
Réponses à propos de AI Answer Engine Schema Builder
This tool builds the four JSON-LD types answer engines specifically favor, not general on-page markup. A standard schema generator covers Article, Product, FAQ, and LocalBusiness for Google rich results. This builder focuses on QAPage, speakable, ClaimReview, and Dataset — the shapes that AI Overviews, Perplexity, Copilot, and voice assistants use to pick and trust the source they cite.
Use QAPage for a single user-asked question with one accepted answer, and FAQPage for a list of questions you author yourself. QAPage models community Q&A — a forum thread or support question with an accepted answer plus optional suggested answers and upvotes. Google allows only one QAPage per page, so reserve it for the page's primary question rather than a bank of self-written FAQs.
Both are valid — type 'css' for cssSelector or 'xpath' for XPath, and the builder emits the matching property. cssSelector targets passages with familiar selectors like `.summary` or `#tldr`, while xpath targets them by document path. Point either at short, self-contained passages that make sense read aloud; speakable is currently honored mainly by Google Assistant for eligible news publishers.
Yes — both are trust and provenance signals that answer engines weigh when choosing which source to cite. ClaimReview publishes an explicit truth rating that engines reference on contested or trending claims, and Dataset exposes the licensed source behind your statistics so figures get attributed back to your page. Neither guarantees a citation, but both make your content materially easier to verify and quote.