--- title: "The schema stack that gets you cited in ChatGPT | Answerly" description: "JSON-LD only. Article + FAQPage + Person + Organization + BreadcrumbList + Service+Offer. Exact schema stack deployed on every Answerly engagement, with code patterns." url: https://answerly.agency/blog/schema-stack-for-ai-citation/ lang: en updated: 2026-04-27T00:00:00.000Z --- Schema · 9 min read # The schema stack that gets you cited in ChatGPT and Perplexity JSON-LD only. Article + FAQPage + Person + Organization + BreadcrumbList + Service+Offer. Here is the exact stack we deploy on every Answerly engagement and what each one does for AI citation. Dmytro Popryadukhin · 2026-04-27 ## Key takeaways - JSON-LD only — never mix with microdata or RDFa. - schema.org Person with verifiable sameAs is the highest-ROI single move for regulated content. - Service + Offer with priceSpecification makes pricing machine-readable for ChatGPT. - Validate every page with both Schema.org validator and Google Rich Results Test. ## Quick Facts | Parameter | Value | | --- | --- | | Format | JSON-LD only (never microdata or RDFa mixed) | | Schemas deployed everywhere | Article, FAQPage, Person, Organization, BreadcrumbList | | Schemas on commercial pages | Service + Offer with priceSpecification | | Schemas on comparison pages | ItemList + Review where applicable | | Validators used | Schema.org validator + Google Rich Results Test | ## What schema actually does for AI citation Schema is the structured-data layer that makes a page legible to machines. AI systems lean heavily on schema for citation decisions — a page with Article + FAQPage schema will outcite an identical-content page without it. We have measured this across our portfolio: schema deployment alone (no content changes) lifts citation rate by 18–35% within sixty days. The lever is bigger in regulated niches. A schema.org Person with verifiable sameAs (LinkedIn, professional registry, bar admission) is the single biggest E-E-A-T move for crypto, fintech, healthcare and legal content. Anonymous bylines on YMYL content are systematically down-weighted by every major LLM extractor we test against. ## The stack we deploy Six schemas everywhere. Two more on commercial pages. ### Article (with dateModified) Goes on every blog post, every service page, every case study. The `dateModified` field is the currency signal — AI systems weight recency on YMYL content. ``` { "@context": "https://schema.org", "@type": "Article", "headline": "Your page title", "description": "Page description, 140-175 chars", "dateModified": "2026-04-30T00:00:00.000Z", "author": [ { "@type": "Person", "name": "Named author" } ], "publisher": { "@type": "Organization", "name": "Your Brand", "url": "https://yourdomain.com" }, "mainEntityOfPage": { "@type": "WebPage", "@id": "https://yourdomain.com/path" } } ``` ### FAQPage Goes on every page with an FAQ block — and most priority pages should have one. AI extractors quote FAQ Q&A pairs verbatim. ``` { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "How fast do we see results?", "acceptedAnswer": { "@type": "Answer", "text": "Direct answer ≤ 30 words. Optional 2-3 sentence depth." } } ] } ``` ### Person — the highest-ROI schema Per named author, principal, or expert. Include `sameAs` with verifiable links. ``` { "@context": "https://schema.org", "@type": "Person", "name": "Dmytro Popryadukhin", "jobTitle": "Head of SEO", "worksFor": { "@type": "Organization", "name": "Answerly Agency" }, "knowsAbout": ["AEO", "GEO", "Schema markup", "Technical SEO"], "sameAs": [ "https://www.linkedin.com/in/dmytro-popryadukhin-138842103" ] } ``` The `sameAs` field is what makes this schema work. AI systems treat schema-validated identities with verifiable external profiles fundamentally differently from anonymous bylines. ### Organization (ProfessionalService for B2B) Once per site, in the BaseLayout. Use the most specific type that fits — `ProfessionalService`, `LegalService`, `MedicalOrganization`, `LocalBusiness`. Add `parentOrganization` if the brand is a service line of a parent. ### BreadcrumbList Per page with breadcrumbs. Helps AI understand the site hierarchy. ### Service + Offer (commercial pages) Service describes what you sell. Offer with `priceSpecification` makes the price machine-readable. ``` { "@context": "https://schema.org", "@type": "Service", "name": "Starter — AI Visibility Audit", "serviceType": "AI visibility audit", "provider": { "@type": "Organization", "name": "Answerly Agency" }, "offers": { "@type": "Offer", "priceCurrency": "USD", "price": "890", "priceSpecification": { "@type": "UnitPriceSpecification", "priceCurrency": "USD", "price": 890, "unitCode": "MON", "unitText": "month" }, "availability": "https://schema.org/InStock" } } ``` This is what makes the pricing on this site machine-readable to ChatGPT when someone asks “how much does AEO cost in 2026”. Try the prompt. ### ItemList + Review (comparison pages) For “best X” or “top N” pages, add ItemList with Review per item. AI systems use this to populate ranked-list answers. ## What we never deploy - **microdata or RDFa** — JSON-LD only. Mixed schemas confuse extractors. - **Aggregate ratings without a real review source** — fake AggregateRating gets penalised. - **Speakable** for non-news B2B content — designed for news, gets ignored on commercial sites. - **Action schemas (BookAction, ReserveAction)** — they trigger features that distract from the citation goal. ## Validation as a deployment gate Every priority page should pass two validators before going live: - Schema.org validator - Google Rich Results Test Failures are usually one of three things: malformed JSON-LD, missing required fields on Article (dateModified, author), or `sameAs` URLs that 404. Fix at deploy, not in production. ## The build-time pattern Schemas should never be hand-written per page. Generate them from your content collection at build time. The Astro template Answerly uses on this site does exactly that — every Service page emits its own ServiceSchema component pulling from frontmatter. The FAQPage schema is generated from the `faqs` array in the markdown. The Person schema for each team member is generated on /team. Zero drift between content and schema. If you want to copy the pattern, the source is in our [services pages](/services) where the same schema is applied at build time on every priority page. ## See also - [**Free AI Visibility Audit** — 60-second score across 8 categories](/ai-visibility-audit/) - [**What is AEO?** — definitional pillar](/blog/what-is-answer-engine-optimization/) - [**Best AEO tools 2026** — comparison](/blog/best-aeo-tools-2026/) ## Related reading - [FAQPage vs QAPage: which schema AI citers actually trust in 2026](/blog/faqpage-vs-qapage-schema) - [Named experts and E-E-A-T: the schema move that doubles citation rate](/blog/eeat-named-experts) - [The 'as of' date pattern — embedding verifiable timestamps inside your copy](/blog/as-of-date-pattern) ## Run a free AI visibility audit 60 seconds to submit, full 47-check report in your inbox within 24 hours. No signup wall, no call required. [Get the free audit](/ai-visibility-audit/) [See pricing](/pricing/) Last updated 2026-04-27.