{"id":3159,"date":"2026-10-14T03:26:24","date_gmt":"2026-10-14T03:26:24","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/zero-click-crm-opportunities\/"},"modified":"2026-10-14T03:26:24","modified_gmt":"2026-10-14T03:26:24","slug":"zero-click-crm-opportunities","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/zero-click-crm-opportunities\/","title":{"rendered":"Mapping Zero Click AI Searches to CRM Opportunities"},"content":{"rendered":"<p><em>By maxaeo.ai \uff5c Published 2026-10-14 \uff5c Updated 2026-10-14<\/em><\/p>\n<p><strong>Mapping zero click AI searches to CRM opportunities<\/strong> requires more than adding ChatGPT or Perplexity as a traffic source. A buyer may see your brand in an AI answer, leave without clicking, return through branded search, and create an opportunity weeks later. The practical solution is an evidence-based model that connects AI visibility, buyer-reported discovery, CRM records, and pipeline outcomes without claiming more certainty than the data supports.<\/p>\n<figure class=\"wp-block-image size-large\" style=\"margin:1.5em 0;\"><img decoding=\"async\" src=\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/10\/backend-6486-1.jpg\" alt=\"Mapping zero click AI searches to CRM opportunities across the B2B SaaS funnel\" style=\"max-width:100%;height:auto;\"><\/figure>\n<h2>What are zero-click AI searches?<\/h2>\n<p>A zero-click AI search is a search interaction where the user receives a useful answer, shortlist, or product recommendation inside an AI interface without visiting the cited website. The interaction can still influence later actions such as a branded search, direct visit, demo request, or sales conversation.<\/p>\n<p>Traditional attribution systems struggle because the first meaningful touchpoint may not create a browser session. The buyer can ask ChatGPT, Gemini, Perplexity, Claude, or Google AI features for recommendations, remember one vendor, and only later interact with your website.<\/p>\n<p>Google\u2019s search ecosystem already treats direct-answer formats as a measurement challenge; HubSpot defines zero-click results as searches resolved directly on the results page rather than through a website visit. (<a href=\"https:\/\/blog.hubspot.com\/marketing\/zero-click-searches\" target=\"_blank\" rel=\"noopener\">blog.hubspot.com<\/a>) AI answers extend this behavior from a single result page into a longer research conversation.<\/p>\n<p>The key distinction is:<\/p>\n<ul>\n<li><strong>Visibility:<\/strong> Was the brand mentioned, recommended, or cited?<\/li>\n<li><strong>Influence:<\/strong> Did the exposure affect buyer consideration?<\/li>\n<li><strong>Attribution:<\/strong> Can the exposure be connected to a person, account, opportunity, or revenue event?<\/li>\n<\/ul>\n<p>These are related signals, but they are not interchangeable.<\/p>\n<h2>Why standard CRM attribution misses AI-assisted demand<\/h2>\n<p>Standard source fields usually record the session that created the lead, not the research interaction that shaped the shortlist. This creates four common blind spots:<\/p>\n<ol>\n<li><strong>No click:<\/strong> The AI answer satisfies the initial information need.<\/li>\n<li><strong>Delayed conversion:<\/strong> The buyer returns days or weeks later through another channel.<\/li>\n<li><strong>Cross-device research:<\/strong> Discovery happens on a phone, while the form submission happens on a laptop.<\/li>\n<li><strong>Committee buying:<\/strong> One person sees the recommendation, but another contact opens the opportunity.<\/li>\n<\/ol>\n<p>A last-touch model may credit organic search, direct traffic, paid retargeting, or a sales email. Those channels may have assisted the conversion, but they do not necessarily explain why the brand entered the buyer\u2019s consideration set.<\/p>\n<p>For B2B SaaS, the correct question is not \u201cDid AI generate the form submission?\u201d It is:<\/p>\n<blockquote>\n<p>\u201cWhat evidence shows that AI search contributed to account awareness, vendor consideration, or opportunity creation?\u201d<\/p>\n<\/blockquote>\n<p>That wording prevents overclaiming while still giving revenue teams a way to measure an otherwise invisible channel.<\/p>\n<h2>A practical AI-to-CRM evidence ladder<\/h2>\n<p>The most defensible approach is to assign AI influence using evidence levels rather than a binary \u201cAI-sourced\u201d label.<\/p>\n<div style=\"overflow-x:auto;\">\n<table style=\"width:100%;border-collapse:collapse;margin:1.5em 0;font-size:0.95em;\">\n<thead>\n<tr>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">Evidence level<\/th>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">Signal<\/th>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">CRM interpretation<\/th>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">Suggested confidence<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">1<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Brand appears in tracked AI answers<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Market visibility<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Low<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">2<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">AI answer cites the brand\u2019s site or content<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Discoverability and source presence<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Low to medium<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">3<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Branded search or direct traffic rises after visibility improves<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Possible assisted demand<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Medium<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">4<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Prospect says they found or evaluated the brand through AI<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Self-reported influence<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Medium to high<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">5<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">AI-referred session creates a known contact<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Direct digital conversion<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">High<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">6<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">AI evidence is attached to an opportunity and confirmed during discovery<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Opportunity-level influence<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Highest practical confidence<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>This ladder is an original operating framework, not a promise of perfect causality. It separates what your monitoring data can prove from what must be confirmed by the buyer or sales team.<\/p>\n<p>A CRM record should preserve the evidence itself, not just a source label. Recommended fields include:<\/p>\n<ul>\n<li><code>AI Discovery Reported<\/code>: Yes \/ No \/ Unknown<\/li>\n<li><code>AI Engine<\/code>: ChatGPT, Perplexity, Gemini, Claude, or another platform<\/li>\n<li><code>AI Prompt Theme<\/code>: category, comparison, use case, pricing, migration, or implementation<\/li>\n<li><code>AI Brand Position<\/code>: first recommendation, included, cited, or omitted<\/li>\n<li><code>AI Citation Source<\/code>: domain or page cited in the answer<\/li>\n<li><code>Discovery Date<\/code>: when the prospect reported the interaction<\/li>\n<li><code>AI Influence Confidence<\/code>: low, medium, high<\/li>\n<li><code>AI Evidence URL or Note<\/code>: supporting transcript, screenshot, or sales note<\/li>\n<\/ul>\n<h2>How to map prompts to CRM buying stages<\/h2>\n<p>AI prompts should be grouped by buying intent before they are connected to opportunity stages. Search Engine Land recommends mapping AI prompts across the sales funnel because apparently similar questions can represent very different levels of purchase intent. (<a href=\"https:\/\/searchengineland.com\/map-ai-search-prompts-sales-funnel-486060\" target=\"_blank\" rel=\"noopener\">searchengineland.com<\/a>)<\/p>\n<p>A useful SaaS mapping looks like this:<\/p>\n<div style=\"overflow-x:auto;\">\n<table style=\"width:100%;border-collapse:collapse;margin:1.5em 0;font-size:0.95em;\">\n<thead>\n<tr>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">Buyer stage<\/th>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">Example AI prompt<\/th>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">CRM event to monitor<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Problem awareness<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">\u201cHow can a SaaS company monitor AI search visibility?\u201d<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">New contact or content engagement<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Category research<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">\u201cWhat tools track brand mentions in ChatGPT?\u201d<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Marketing-qualified lead<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Shortlisting<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">\u201cWhat are the best AI visibility platforms for SaaS?\u201d<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Demo request or sales qualification<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Comparison<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">\u201cMaxAEO vs. other AI search monitoring tools\u201d<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Opportunity creation<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Validation<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">\u201cWhich platform tracks citations and competitor sentiment?\u201d<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Technical evaluation or stakeholder meeting<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Purchase readiness<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">\u201cHow should an enterprise measure AI search ROI?\u201d<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Proposal, procurement, or negotiation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The same brand mention has different commercial value depending on the prompt. A generic educational answer may create awareness, while a comparison answer can influence a shortlist even when no click occurs.<\/p>\n<p>For more detailed prompt planning, use a <a href=\"https:\/\/maxaeo.ai\/blog\/generative-search-prompt-clusters-for-b2b\/\">generative search prompt cluster framework for B2B<\/a> to organize prompts around buyer needs rather than only traditional SEO keywords.<\/p>\n<h2>How to connect zero-click exposure with CRM opportunities<\/h2>\n<p>Use a five-step workflow.<\/p>\n<h3>1. Monitor the prompt universe<\/h3>\n<p>Create a stable set of prompts covering category, competitor, use-case, pain-point, and purchase-intent questions. Run the same prompt groups consistently so changes in visibility are comparable over time.<\/p>\n<p>MaxAEO monitors brand mentions, recommendations, rankings, sentiment, and citation sources across eight AI engines, including ChatGPT, Perplexity, Gemini, Claude, Copilot, Grok, Google AI Mode, and Google AI Overviews. Its daily monitoring can provide the exposure layer required for this workflow.<\/p>\n<h3>2. Store answer-level evidence<\/h3>\n<p>Do not record only \u201cmentioned: yes.\u201d Store the answer text, position, sentiment, cited domain, prompt category, and competitors shown. A brand recommended in position one is not equivalent to a brand listed last or mentioned with a negative qualification.<\/p>\n<h3>3. Capture self-reported discovery<\/h3>\n<p>Add a short form question such as:<\/p>\n<blockquote>\n<p>\u201cDid an AI assistant influence your research or decision?\u201d<\/p>\n<\/blockquote>\n<p>Use selectable options for ChatGPT, Gemini, Perplexity, Claude, Google AI features, another AI tool, or \u201cnot sure.\u201d Keep the free-text field so prospects can describe the actual recommendation.<\/p>\n<h3>4. Attach AI signals to the account and opportunity<\/h3>\n<p>AI discovery is often account-level, not contact-level. When a sales representative hears that a buyer used an AI assistant, attach the evidence to the account and opportunity. This avoids losing the signal when multiple stakeholders participate in the buying process.<\/p>\n<h3>5. Report influence separately from sourced pipeline<\/h3>\n<p>Use separate metrics:<\/p>\n<ul>\n<li><strong>AI-sourced pipeline:<\/strong> The first identifiable conversion came directly from an AI referral or AI-specific form response.<\/li>\n<li><strong>AI-influenced pipeline:<\/strong> AI exposure was confirmed or strongly supported, but another channel created the recorded conversion.<\/li>\n<li><strong>AI-visible opportunities:<\/strong> The account engaged after the brand was monitored in relevant AI prompts, but no direct influence evidence exists.<\/li>\n<\/ul>\n<p>This separation keeps the model credible. It also gives executives a clearer view of whether AI search is creating demand, assisting demand, or simply improving market visibility.<\/p>\n<figure class=\"wp-block-image size-large\" style=\"margin:1.5em 0;\"><img decoding=\"async\" src=\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/10\/backend-6486-2.jpg\" alt=\"CRM evidence ladder for AI search visibility, influence, and opportunity attribution\" style=\"max-width:100%;height:auto;\"><\/figure>\n<h2>A defensible attribution formula for AI-assisted opportunities<\/h2>\n<p>A practical opportunity score can combine evidence without pretending that each signal proves causation:<\/p>\n<p><strong>AI Influence Score = 30% buyer confirmation + 25% direct AI referral + 20% prompt relevance + 15% citation strength + 10% timing alignment<\/strong><\/p>\n<p>Each component can be scored from 0 to 1.<\/p>\n<p>For example, an opportunity may receive:<\/p>\n<ul>\n<li>Buyer confirmation: 1.0<\/li>\n<li>Direct AI referral: 0.0<\/li>\n<li>Prompt relevance: 0.8<\/li>\n<li>Citation strength: 0.7<\/li>\n<li>Timing alignment: 0.9<\/li>\n<\/ul>\n<p>The resulting score is:<\/p>\n<p><strong>0.30 + 0 + 0.16 + 0.105 + 0.09 = 0.655, or 65.5%<\/strong><\/p>\n<p>That does not mean AI caused 65.5% of the deal. It means the opportunity has strong supporting evidence of AI influence and should be analyzed separately from unattributed pipeline.<\/p>\n<p>Teams can then compare win rate, sales cycle, deal size, and expansion outcomes across AI-influenced and non-influenced opportunities. The comparison is more useful than assigning arbitrary revenue credit to every brand mention.<\/p>\n<p>For a broader measurement architecture, see the <a href=\"https:\/\/maxaeo.ai\/blog\/ai-search-attribution-model\/\">AI search attribution model for enterprise SaaS<\/a>.<\/p>\n<h2>What to measure every week<\/h2>\n<p>A zero-click CRM program should connect visibility metrics with revenue metrics:<\/p>\n<ul>\n<li>AI mention rate by prompt cluster<\/li>\n<li>Average recommendation position<\/li>\n<li>Competitor share of voice<\/li>\n<li>Citation frequency and cited domains<\/li>\n<li>Sentiment and factual accuracy<\/li>\n<li>Branded search lift<\/li>\n<li>Self-reported AI discovery rate<\/li>\n<li>AI-influenced contacts and accounts<\/li>\n<li>AI-influenced opportunities<\/li>\n<li>Opportunity creation rate<\/li>\n<li>Win rate and sales-cycle length<\/li>\n<li>Pipeline value by confidence level<\/li>\n<\/ul>\n<p>MaxAEO\u2019s competitor comparison and citation tracking can help identify which brands appear in answers, which sources are being cited, and where recommendation gaps exist. Its free AI visibility audit can provide an initial diagnostic before a CRM measurement program is designed.<\/p>\n<figure class=\"wp-block-image size-large\" style=\"margin:1.5em 0;\"><img decoding=\"async\" src=\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/10\/backend-6486-3.jpg\" alt=\"AI search attribution dashboard connecting prompt visibility to CRM pipeline stages\" style=\"max-width:100%;height:auto;\"><\/figure>\n<h2>Common questions<\/h2>\n<h3>Can zero-click AI searches be attributed with certainty?<\/h3>\n<p>Usually not at the individual search level. AI visibility data, buyer self-reporting, referral data, branded demand, and CRM notes can create a strong evidence base, but they should be reported as sourced, influenced, or visible rather than treated as identical.<\/p>\n<h3>Should AI search receive first-touch or multi-touch credit?<\/h3>\n<p>Use multi-touch reporting for executive analysis and preserve first-touch data for demand-generation questions. A single model cannot answer every revenue question, especially when AI discovery occurs before a known contact exists.<\/p>\n<h3>What if the prospect says \u201cGoogle,\u201d not ChatGPT?<\/h3>\n<p>Record the answer as unknown rather than forcing a channel classification. Add a follow-up question when appropriate: \u201cDid you see our website in a traditional result, an AI Overview, or an AI assistant such as ChatGPT or Perplexity?\u201d<\/p>\n<h3>How long should the attribution window be?<\/h3>\n<p>Use a window that matches the sales cycle and validate it against observed lag between discovery, first conversion, and opportunity creation. Short windows can miss delayed B2B influence; unlimited windows can over-credit unrelated visibility.<\/p>\n<h3>What is the first implementation step?<\/h3>\n<p>Start with a small prompt set tied to commercial stages, then add CRM fields for AI discovery, engine, prompt theme, citation source, and confidence. Measure consistently before adding complex modeling.<\/p>\n<h2>Final takeaway<\/h2>\n<p>Zero-click AI searches should not be forced into a traditional referral model. The stronger approach is to map <strong>prompt intent \u2192 AI exposure \u2192 buyer confirmation \u2192 account evidence \u2192 CRM opportunity \u2192 revenue outcome<\/strong>.<\/p>\n<p>This produces a more honest measurement system: one that recognizes AI\u2019s role in shaping shortlists while distinguishing visibility from verified influence. For SaaS teams, that distinction is the foundation for making AI search performance useful to marketing, sales, and revenue operations.<\/p>\n<p><a href=\"https:\/\/maxaeo.ai\/\">Run a free AI visibility audit with MaxAEO<\/a>.<\/p>\n<p><script type=\"application\/ld+json\">\n{\"@context\":\"https:\/\/schema.org\",\"@type\":\"Article\",\"author\":{\"@type\":\"Organization\",\"name\":\"maxaeo.ai\"},\"dateModified\":\"2026-10-14\",\"datePublished\":\"2026-10-14\",\"description\":\"Learn how to map zero-click AI searches to CRM opportunities with prompt evidence, self-reported intent, branded demand, and pipeline stages. Build a defensible attribution model.\",\"headline\":\"Mapping Zero Click AI Searches to CRM Opportunities | maxaeo.ai\",\"image\":\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/10\/art-10419-cover.jpg\",\"publisher\":{\"@type\":\"Organization\",\"name\":\"maxaeo.ai\"}}\n<\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to map zero-click AI searches to CRM opportunities with prompt evidence, self-reported intent, branded demand, and pipeline stages. Build a defensible attribution model.<\/p>\n","protected":false},"author":1,"featured_media":3158,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3159","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/posts\/3159","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/comments?post=3159"}],"version-history":[{"count":0,"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/posts\/3159\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/media\/3158"}],"wp:attachment":[{"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/media?parent=3159"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/categories?post=3159"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/maxaeo.ai\/blog\/wp-json\/wp\/v2\/tags?post=3159"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}