{"id":2245,"date":"2026-06-17T16:40:12","date_gmt":"2026-06-17T16:40:12","guid":{"rendered":"https:\/\/aigrowthagent.co\/articles\/schema-for-ai-visibility\/"},"modified":"2026-06-17T16:40:12","modified_gmt":"2026-06-17T16:40:12","slug":"schema-for-ai-visibility","status":"publish","type":"post","link":"https:\/\/aigrowthagent.co\/articles\/schema-for-ai-visibility\/","title":{"rendered":"How To Deploy Schema for AI Visibility and Earn Citations"},"content":{"rendered":"<p><em>Written by: Mariana Fonseca, Editorial Team, AI Growth Agent<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for AI-Ready Schema<\/h2>\n<ul>\n<li>JSON-LD schema gives AI systems the signals they need to verify your brand, judge freshness, and pull citable answers.<\/li>\n<li>Deploy Organization schema site-wide with a stable <code>@id<\/code> and <code>sameAs<\/code> links, then add Article or BlogPosting with <code>dateModified<\/code> and FAQPage on relevant pages.<\/li>\n<li>Use consistent entity names in JSON-LD and visible text, and connect schema with shared <code>@id<\/code> values so AI engines keep and trust your data.<\/li>\n<li>Agentic discovery files such as <code>llms.txt<\/code> and <code>llms-full.txt<\/code> extend your reach to AI agents and retrieval pipelines beyond standard crawlers.<\/li>\n<li>Traditional search tools show you where your brand stands. AI Growth Agent makes your brand the answer. <a href=\"https:\/\/cal.com\/team\/aigrowthagent\/demo\" target=\"_blank\">See how a headless marketing engine deploys and maintains this schema stack for you.<\/a><\/li>\n<\/ul>\n<h2>5-Step Implementation Checklist for JSON-LD<\/h2>\n<ol>\n<li>Audit and establish the Organization entity with a stable <code>@id<\/code>, <code>sameAs<\/code> links to Wikipedia, LinkedIn, Wikidata, and Crunchbase, and deploy it site-wide through the page layout.<\/li>\n<li>Implement Article or BlogPosting schema on every content page, including <code>datePublished<\/code>, <code>dateModified<\/code>, <code>publisher<\/code> referencing the Organization <code>@id<\/code>, and <code>author<\/code> via Person schema with <code>sameAs<\/code> links.<\/li>\n<li>Add FAQPage schema to every page that answers a discrete question, with each <code>acceptedAnswer<\/code> written as a complete 40-to-60-word standalone response.<\/li>\n<li>Validate all schema blocks in Google&#39;s Rich Results Test and Schema.org validator, confirm entity name consistency between JSON-LD and visible on-page text, and resolve any mismatches.<\/li>\n<li>Deploy agentic discovery files: <code>llms.txt<\/code>, <code>llms-full.txt<\/code>, Blog MCP, and <code>\/.well-known\/<\/code> agent discovery endpoints to expose the brand to AI agents and retrieval pipelines beyond standard crawlers.<\/li>\n<\/ol>\n<h2>Prerequisites for a Clean Schema Rollout<\/h2>\n<p>The goal of this implementation is to ground a brand as a trusted, citable entity across AI surfaces by providing machine-readable signals for identity, freshness, and topical authority. Confirm access to the site&#39;s HTML <code>&lt;head&gt;<\/code> or a tag manager that injects into it, a list of authoritative external profiles for <code>sameAs<\/code> links (LinkedIn, Wikidata, Crunchbase, Wikipedia where applicable), and a content inventory that separates article pages from FAQ content.<\/p>\n<p>Assign a technical implementer who can deploy JSON-LD script blocks and a content owner who can confirm that schema values match visible page text exactly. The primary risk is entity name inconsistency: <a href=\"https:\/\/adv-it-performance.ca\/json-ld-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">Perplexity discards schema as unreliable when the name in JSON-LD does not match the name in the visible page header<\/a>. A secondary risk is semantic misclassification: <a href=\"https:\/\/coffeecup.tech\/blog\/json-ld\" target=\"_blank\" rel=\"noindex nofollow\">using an inappropriate schema type can cause the entire JSON-LD graph to be ignored by Google<\/a>. Validation checkpoints include Google&#39;s Rich Results Test after each schema type is deployed and a manual review of entity name alignment across all pages before launch.<\/p>\n<h2>How Schema Fits Your Headless Marketing Architecture<\/h2>\n<p>With these prerequisites in place, the implementation can begin. Schema deployment sits inside a broader headless marketing architecture that shapes how AI surfaces discover and cite your content. The four pillars that feed citation decisions are Search Intelligence, AI Analytics, Bot Tracking, and AI Ranking, which together describe how your brand appears across both traditional and AI search.<\/p>\n<p>Schema provides the machine-readable foundation that makes all four pillars useful. Without it, AI surfaces cannot reliably connect a piece of content to a brand entity, cannot assess freshness, and cannot extract structured answers for AI Overviews. The implementation sequence moves from entity establishment (Organization) to content grounding (Article or BlogPosting) to answer extraction (FAQPage) to agentic discovery (llms.txt, MCP, <code>\/.well-known\/<\/code>). Each layer builds on the previous one, and <a href=\"https:\/\/adv-it-performance.ca\/json-ld-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">pages with connected schema using shared <code>@id<\/code> references can receive higher citation rates than pages with isolated schema blocks<\/a>.<\/p>\n<h2>Step-by-Step Guide to JSON-LD for AI Visibility<\/h2>\n<h3>Step 1: Deploy Organization Schema Site-Wide<\/h3>\n<p>Organization schema defines the root brand entity, and every other schema type on the site references it. Place this block in the site-wide layout so it appears on every page.<\/p>\n<pre><code>&lt;script type=\"application\/ld+json\"&gt; { \"@context\": \"https:\/\/schema.org\", \"@type\": \"Organization\", \"@id\": \"https:\/\/yoursite.com\/#organization\", \"name\": \"Your Brand Name\", \"url\": \"https:\/\/yoursite.com\", \"logo\": { \"@type\": \"ImageObject\", \"url\": \"https:\/\/yoursite.com\/logo.png\" }, \"foundingDate\": \"2020\", \"description\": \"A concise, factual description of what the organization does.\", \"areaServed\": \"Global\", \"knowsAbout\": [\"Topic One\", \"Topic Two\", \"Topic Three\"], \"sameAs\": [ \"https:\/\/www.linkedin.com\/company\/your-brand\", \"https:\/\/en.wikipedia.org\/wiki\/Your_Brand\", \"https:\/\/www.wikidata.org\/wiki\/QXXXXXXX\", \"https:\/\/www.crunchbase.com\/organization\/your-brand\" ], \"contactPoint\": { \"@type\": \"ContactPoint\", \"contactType\": \"customer support\", \"url\": \"https:\/\/yoursite.com\/contact\" } } &lt;\/script&gt; <\/code><\/pre>\n<p>Sites that implemented comprehensive entity schema including <code>sameAs<\/code> identifiers saw a 3.2x lift in AI Mode citation rates after the March 2026 Google core update. This lift occurs because the <code>knowsAbout<\/code> property places the brand in Google&#39;s Knowledge Graph as a topical authority and prevents AI systems from confusing the company with competitors, which directly improves citation selection.<\/p>\n<h3>Step 2: Implement Article or BlogPosting Schema with dateModified<\/h3>\n<p>Article or BlogPosting schema grounds each content page with clear authorship and freshness signals. The <code>dateModified<\/code> field is the primary freshness signal AI surfaces use to assess citation worthiness, and <a href=\"https:\/\/astiva.ai\/blog\/schema-types-chatgpt-visibility-boost\" target=\"_blank\" rel=\"noindex nofollow\">content updated within the last 30 days receives 3.2x more AI citations than content untouched for over a year<\/a>.<\/p>\n<pre><code>&lt;script type=\"application\/ld+json\"&gt; { \"@context\": \"https:\/\/schema.org\", \"@type\": \"BlogPosting\", \"@id\": \"https:\/\/yoursite.com\/blog\/article-slug\/#article\", \"headline\": \"Your Article Headline\", \"description\": \"A factual 150-to-160-character summary of the article.\", \"datePublished\": \"2026-01-15\", \"dateModified\": \"2026-06-16\", \"mainEntityOfPage\": { \"@type\": \"WebPage\", \"@id\": \"https:\/\/yoursite.com\/blog\/article-slug\/\" }, \"publisher\": { \"@id\": \"https:\/\/yoursite.com\/#organization\" }, \"author\": { \"@type\": \"Person\", \"name\": \"Author Full Name\", \"url\": \"https:\/\/yoursite.com\/author\/author-slug\/\", \"sameAs\": [ \"https:\/\/www.linkedin.com\/in\/author-profile\", \"https:\/\/twitter.com\/authorhandle\" ] }, \"image\": { \"@type\": \"ImageObject\", \"url\": \"https:\/\/yoursite.com\/images\/article-image.jpg\", \"width\": 1200, \"height\": 630 } } &lt;\/script&gt; <\/code><\/pre>\n<p>Author attribution via Person schema with <code>sameAs<\/code> links to LinkedIn and Twitter can increase citation rates by giving AI systems a verifiable human source. See how automated schema deployment keeps your <code>dateModified<\/code> signals current without manual updates and maintains author data at scale&nbsp;&mdash; <a href=\"https:\/\/cal.com\/team\/aigrowthagent\/demo\" target=\"_blank\">schedule a demo to explore the platform<\/a>.<\/p>\n<h3>Step 3: Add FAQPage Schema to Answer-Oriented Pages<\/h3>\n<p><a href=\"https:\/\/adv-it-performance.ca\/json-ld-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">FAQPage is the single highest-impact schema type for AI citations because AI Overviews frequently pull exact answers directly from FAQ structured data<\/a>. Each <code>acceptedAnswer<\/code> must be a complete standalone response that can appear in an AI answer without extra context. The recommended length is 40 to 60 words per answer for optimal LLM extraction.<\/p>\n<pre><code>&lt;script type=\"application\/ld+json\"&gt; { \"@context\": \"https:\/\/schema.org\", \"@type\": \"FAQPage\", \"mainEntity\": [ { \"@type\": \"Question\", \"name\": \"What is schema markup for AI visibility?\", \"acceptedAnswer\": { \"@type\": \"Answer\", \"text\": \"Schema markup for AI visibility is JSON-LD structured data that translates brand identity, content authorship, publication dates, and question-answer pairs into a machine-readable vocabulary that ChatGPT, Perplexity, and Google AI Overviews can parse, verify, and cite. Without it, pages are effectively invisible to generative AI engines.\" } }, { \"@type\": \"Question\", \"name\": \"Which schema types matter most for AI citations?\", \"acceptedAnswer\": { \"@type\": \"Answer\", \"text\": \"Organization, Article or BlogPosting with dateModified, and FAQPage are the three highest-priority schema types for AI citation. Organization establishes the root brand entity. Article signals freshness and authorship. FAQPage structures content as extractable question-answer pairs that AI Overviews pull directly.\" } } ] } &lt;\/script&gt; <\/code><\/pre>\n<h3>Step 4: Wire sameAs Entity Links Across the Knowledge Graph<\/h3>\n<p><code>sameAs<\/code> links connect your on-site JSON-LD to the external knowledge graphs that AI surfaces use to verify entity identity. Include links to Wikidata, LinkedIn, Crunchbase, and Wikipedia in the Organization block, and mirror the pattern in Person schema for authors. <code>sameAs<\/code>, <code>knowsAbout<\/code>, and Organization schema pointing to identifiers such as Wikidata, LinkedIn, and Crunchbase represent the highest-leverage entity-disambiguation implementation for improving AI Mode source selection. As mentioned in Step 1, the largest citation gains appear over a 30-to-60-day window following implementation as AI systems re-index and re-evaluate entity relationships.<\/p>\n<p>Stop letting AI define your brand at random and start steering how it describes you across search. <a href=\"https:\/\/cal.com\/team\/aigrowthagent\/demo\" target=\"_blank\">Schedule a demo to see if AI Growth Agent is a good fit for your organization.<\/a><\/p>\n<h3>Step 5: Deploy Agentic Discovery Files for AI Agents<\/h3>\n<p>Schema handles structured data for crawlers and AI Overviews, while agentic discovery files extend that coverage to AI agents, retrieval-augmented generation pipelines, and browser-native agents. Publish <code>llms.txt<\/code> at the domain root as a plain Markdown file with an H1 brand name, a blockquote description, and annotated links to canonical content pages. Publish <code>llms-full.txt<\/code> as a concatenated Markdown document of full site content, and <a href=\"https:\/\/derivatex.agency\/blog\/llms-txt-guide\" target=\"_blank\" rel=\"noindex nofollow\">note that crawlers from Microsoft and OpenAI fetch <code>llms-full.txt<\/code> more frequently than <code>llms.txt<\/code> because it removes an extra retrieval step for real-time agents<\/a>.<\/p>\n<p>Expose Blog MCP endpoints and serve OpenAI discovery and Agent Card guidance through <code>\/.well-known\/<\/code>. These files function as a retrieval and navigation layer for agent-to-agent workflows. Anthropic explicitly recommends <code>llms.txt<\/code> in its Writing for Agents guidance, and OpenAI uses it for the Agents SDK and the Agentic Commerce Protocol, which makes it a relevant signal for non-Google AI surfaces even though Google&#39;s own AI Overviews do not require it.<\/p>\n<h2>Common Mistakes and Troubleshooting for JSON-LD<\/h2>\n<p>The most common implementation failure is entity name inconsistency. The <code>name<\/code> value in Organization schema must match the brand name as it appears in the visible page header and navigation. <a href=\"https:\/\/adv-it-performance.ca\/json-ld-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">A mismatch causes Perplexity to discard the schema block as unreliable<\/a>. Audit every page where Organization schema appears and compare the <code>name<\/code> field against the rendered HTML.<\/p>\n<p>The second common failure is stale <code>dateModified<\/code> values. If articles are updated without refreshing the <code>dateModified<\/code> field, the freshness signal discussed in Step 2 is lost. Automate <code>dateModified<\/code> updates as part of the content publishing workflow so every edit sends a clear recency signal.<\/p>\n<p>The third failure is isolated schema blocks with no shared <code>@id<\/code> references. Each Article block must reference the Organization <code>@id<\/code> via the <code>publisher<\/code> field, and each Person block must reference its own stable <code>@id<\/code> via the <code>author<\/code> field. Validate the full graph in Google&#39;s Rich Results Test after every deployment, and remember that <a href=\"https:\/\/coffeecup.tech\/blog\/json-ld\" target=\"_blank\" rel=\"noindex nofollow\">Google explicitly warns that using an inappropriate schema type may cause the entire JSON-LD graph to be ignored<\/a>.<\/p>\n<h2>Verifying Outcomes and Measuring AI Citation Lift<\/h2>\n<p>Citation lift from schema implementation is measurable across all four data pillars. Search Intelligence tracks positioning and competitive share of voice in traditional search, which establishes the baseline before schema changes take effect. AI Analytics captures brand mention rate and citation context across ChatGPT, Perplexity, and Google AI Mode, showing where the brand appears in AI answers and what claims it is cited for.<\/p>\n<figure style=\"text-align: center;\"><video src=\"https:\/\/cdn.aigrowthmarketer.co\/1779159451320-5a90f189a229.mp4\" style=\"max-height: 500px;\" autoplay loop muted playsinline><\/video><figcaption><em>AI Growth Agent&#039;s Content Planner show each brand&#039;s universe of search (tracked prompts\/queries) and its visibility (ranking rate) on both Google Rankings, Google AI Overviews, and ChatGPT citations and mentions.<\/em><\/figcaption><\/figure>\n<p>Bot Tracking records every crawl and citation sweep by AI training agents, including the specific bot ChatGPT uses to cite sources, so the team can confirm that schema-enriched pages are being read. AI Ranking monitors order of mention and citation context week over week, replacing the static rank number with a dynamic view of narrative position. Before-and-after measurement requires a clean baseline, so AI Growth Agent publishes into a separate environment to isolate incremental visibility from existing brand equity.<\/p>\n<figure style=\"text-align: center;\"><img src=\"https:\/\/cdn.aigrowthmarketer.co\/1779159565148-662d048e9906.jpeg\" alt=\"AI Growth Agent&#039;s Reporting dashboard, with ranking rates and their separation between Primary Domain results, Overlapping results, and AI Growth Agent content results (incremental visibility).\" style=\"max-height: 500px;\" loading=\"lazy\" decoding=\"async\"><figcaption><em>AI Growth Agent&#039;s Reporting dashboard, with ranking rates and their separation between Primary Domain results, Overlapping results, and AI Growth Agent content results (incremental visibility).<\/em><\/figcaption><\/figure>\n<p>Leva Sleep, after full schema and content deployment, reached <a href=\"https:\/\/docs.google.com\/document\/d\/1Is82gsOderqGBhnIaRZSKaPkQyaAKhkd0C28Er5KxAI\/export?format=txt\" target=\"_blank\">more than 10,000 ChatGPT citations of its content per month and closed $40,000 to $50,000 in deals within three weeks from buyers who discovered the brand through AI-cited content<\/a>. Breadless <a href=\"https:\/\/docs.google.com\/document\/d\/1Is82gsOderqGBhnIaRZSKaPkQyaAKhkd0C28Er5KxAI\/export?format=txt\" target=\"_blank\">grew Google Search Console impressions from 387,000 to 12.3 million over six months, with ChatGPT now citing eatbreadless.com more than 45,000 times per month<\/a>.<\/p>\n<h2>Advanced Headless and Agentic Scenarios<\/h2>\n<p>Brands operating headless marketing architecture can provision schema automatically at the article and site level, with no manual deployment required. Blog MCP exposes schema, manifest, discovery, and capability guidance directly to AI agents and works with Chrome 146+ and other WebMCP-enabled browsers. Natural language query parameters through <code>\/?s={query}<\/code> auto-trigger personalized, internally linked responses so an agent passing a query directly into the URL receives a structured answer without an extra retrieval step.<\/p>\n<p>Living, self-healing content workflows keep <code>dateModified<\/code> signals current automatically. When the year turns, every article in a content sector is refreshed, and Google Search Console signals plus bot-traffic data trigger targeted updates to articles showing indexing decay. This approach removes the manual overhead of maintaining freshness signals at scale and ensures the next AI training sweep finds the brand&#39;s current narrative rather than a stale one. The <code>llms-full.txt<\/code> file is regenerated on the same cadence, which keeps the concatenated content layer aligned with the live site.<\/p>\n<p>The brands cited in AI search this year are training the next generation of models with their own story. <a href=\"https:\/\/cal.com\/team\/aigrowthagent\/demo\" target=\"_blank\">Schedule a consultation session with AI Growth Agent to see how headless marketing makes your brand the answer.<\/a><\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Does JSON-LD schema directly cause AI Overviews to cite a brand?<\/h3>\n<p>JSON-LD schema does not guarantee citation, but it removes the primary barriers that block it. AI Overviews and LLMs rely on machine-readable signals to verify entity identity, assess content freshness, and extract structured answers. Without Organization schema and <code>sameAs<\/code> links, an AI surface cannot reliably connect a piece of content to a specific brand entity.<\/p>\n<p>Without <code>dateModified<\/code> in Article schema, the surface cannot assess whether the content reflects current information. Without FAQPage schema, the surface must extract answers from unstructured prose rather than validated question-answer pairs. Each schema type reduces ambiguity and increases the probability that the surface selects the brand&#39;s content as a citation source, and the effect compounds when all three types are deployed with shared <code>@id<\/code> references across the site.<\/p>\n<h3>How important are sameAs links compared to other schema properties?<\/h3>\n<p><code>sameAs<\/code> links are the highest-leverage entity-disambiguation signal available in JSON-LD. They connect the brand&#39;s on-site schema to external knowledge graphs that AI surfaces use as verification layers, including Wikidata, LinkedIn, Crunchbase, and Wikipedia. When an AI surface encounters a brand name in content, it cross-references that name against known entities in its training data and retrieval sources.<\/p>\n<p><code>sameAs<\/code> links make that cross-reference unambiguous. The same logic applies to Person schema for authors, because linking an author&#39;s name to a LinkedIn profile and a Twitter handle gives AI surfaces a verifiable identity to attach to the content&#39;s authorship claim, which strengthens E-E-A-T signals and citation likelihood.<\/p>\n<h3>What is the role of llms.txt in a schema-first AI visibility strategy?<\/h3>\n<p><code>llms.txt<\/code> and <code>llms-full.txt<\/code> operate at a different layer than JSON-LD schema. Schema communicates with crawlers and AI Overviews through structured HTML metadata, while <code>llms.txt<\/code> communicates with AI agents and retrieval-augmented generation pipelines through a plain Markdown navigation file at the domain root. For brands targeting non-Google AI surfaces such as Perplexity, ChatGPT with web access, and agent-to-agent workflows, <code>llms.txt<\/code> provides a structured, low-noise map of the site&#39;s most important content.<\/p>\n<p><code>llms-full.txt<\/code> goes further by concatenating full site content into a single document for deep AI ingestion. Google&#39;s own AI Overviews do not require <code>llms.txt<\/code>, but Anthropic and OpenAI explicitly use it in their agent frameworks. A complete AI visibility strategy deploys both schema and agentic discovery files because different AI surfaces consume content through different retrieval mechanisms.<\/p>\n<h3>How does AI Growth Agent handle schema deployment without a technical team?<\/h3>\n<p>AI Growth Agent provisions the full schema suite automatically as part of every engagement. Organization, Article, FAQPage, author, local business, product, review, and software application schema are deployed and kept current without any action from the client. The WordPress plugin included in every package handles schema at the article and site level, alongside bot tracking, Blog MCP, advanced robots.txt, a proper sitemap.xml, and automatically generated web stories.<\/p>\n<figure style=\"text-align: center;\"><img src=\"https:\/\/cdn.aigrowthmarketer.co\/1779159844144-d9febd50b14f.jpeg\" alt=\"AI Growth Agent&#039;s personalization section lets brands add Local Business schema.\" style=\"max-height: 500px;\" loading=\"lazy\" decoding=\"async\"><figcaption><em>AI Growth Agent&#039;s personalization section lets brands add Local Business schema.<\/em><\/figcaption><\/figure>\n<p>The only integration step required from the client is the reverse proxy rewrite that connects the blog to a subdirectory under their domain. Everything else, including agentic discovery files and the full technical SEO stack, ships automatically so the internal marketing team needs no technical skill to maintain it.<\/p>\n<figure style=\"text-align: center;\"><img src=\"https:\/\/cdn.aigrowthmarketer.co\/1779159792681-7ef4cfa7c6c0.jpeg\" alt=\"AI Growth Agent&#039;s personalization section lets brands add product schemas.\" style=\"max-height: 500px;\" loading=\"lazy\" decoding=\"async\"><figcaption><em>AI Growth Agent&#039;s personalization section lets brands add product schemas.<\/em><\/figcaption><\/figure>\n<h2>Conclusion: Turning Schema into AI Narrative Control<\/h2>\n<p>JSON-LD schema forms the machine-readable foundation of narrative control in AI search. Organization schema with <code>sameAs<\/code> entity links establishes the brand as a verifiable entity in the knowledge graphs AI surfaces consult. Article and BlogPosting schema with <code>dateModified<\/code> signals freshness and authorship, while FAQPage schema structures content as extractable answers that AI Overviews can pull directly.<\/p>\n<p>Agentic discovery files extend that coverage to the retrieval pipelines and agent frameworks that operate outside standard crawling. Together, these layers create the technical infrastructure that lets AI surfaces trust and cite a brand consistently, at scale, and across every surface where customers now resolve their questions. Managing this stack manually across hundreds of articles, with freshness signals that must stay current and entity links that must stay consistent, fragments the work across agencies, plugins, and monitoring tools that rarely connect.<\/p>\n<p>Headless marketing replaces that fragmented stack with one autonomous engine that provisions schema, publishes living content, tracks every bot citation, and reports the incremental visibility it generates week over week. Traditional search tools show you where your brand stands. AI Growth Agent makes your brand the answer. <a href=\"https:\/\/cal.com\/team\/aigrowthagent\/demo\" target=\"_blank\">Schedule a consultation session to see your first article live within a week.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deploy JSON-LD schema to earn AI Overview citations and dominate AI search results. AI Growth Agent makes your brand the cited answer \u2014 get a demo!<\/p>\n","protected":false},"author":1,"featured_media":2244,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-2245","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-wordpress"],"_links":{"self":[{"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/posts\/2245","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/comments?post=2245"}],"version-history":[{"count":0,"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/posts\/2245\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/media\/2244"}],"wp:attachment":[{"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/media?parent=2245"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/categories?post=2245"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aigrowthagent.co\/articles\/wp-json\/wp\/v2\/tags?post=2245"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}