{"id":3727,"date":"2026-09-29T04:45:49","date_gmt":"2026-09-29T11:45:49","guid":{"rendered":"https:\/\/frankmasotti.com\/insights\/?p=3727"},"modified":"2026-09-29T04:45:50","modified_gmt":"2026-09-29T11:45:50","slug":"what-disagreement-between-ai-models-can-reveal-about-your-business","status":"publish","type":"post","link":"https:\/\/frankmasotti.com\/insights\/what-disagreement-between-ai-models-can-reveal-about-your-business\/","title":{"rendered":"What Disagreement Between AI Models Can Reveal About Your Business"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-3727\" data-postid=\"3727\" class=\"themify_builder_content themify_builder_content-3727 themify_builder tf_clear\">\n                    <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_tcpn578 tb_first tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_5yqp579 first\">\n                    <!-- module plain text -->\n<div  class=\"module module-plain-text tb_0mrc863 \" data-lazy=\"1\">\n        <div class=\"tb_text_wrap\">\n    <script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"headline\": \"What Disagreement Between AI Models Can Reveal About Your Business\",\n  \"description\": \"When ChatGPT, Claude, and Gemini disagree about a business, the differences can reveal identity confusion, outdated information, weak evidence, unclear positioning, and other gaps in how the business is understood by AI.\",\n  \"image\": {\n    \"@type\": \"ImageObject\",\n    \"url\": \"https:\/\/frankmasotti.com\/insights\/wp-content\/uploads\/2026\/09\/What-Disagreement-Between-AI-Models-Can-Reveal-About-Your-Business.webp\"\n  },\n  \"author\": {\n    \"@type\": \"Person\",\n    \"name\": \"Frank Masotti\",\n    \"url\": \"https:\/\/frankmasotti.com\/about.html\"\n  },\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"Frank Masotti\",\n    \"url\": \"https:\/\/frankmasotti.com\/\"\n  },\n  \"datePublished\": \"2026-09-29\",\n  \"dateModified\": \"2026-09-29\",\n  \"mainEntityOfPage\": {\n    \"@type\": \"WebPage\",\n    \"@id\": \"https:\/\/frankmasotti.com\/insights\/what-disagreement-between-ai-models-can-reveal-about-your-business\/\"\n  },\n  \"url\": \"https:\/\/frankmasotti.com\/insights\/what-disagreement-between-ai-models-can-reveal-about-your-business\/\",\n  \"articleSection\": \"AI Business Understanding\",\n  \"keywords\": [\n    \"AI business understanding\",\n    \"ChatGPT\",\n    \"Claude\",\n    \"Gemini\",\n    \"AI model disagreement\",\n    \"AI business analysis\",\n    \"AI search\",\n    \"AI Business Understanding Report\"\n  ],\n  \"inLanguage\": \"en-US\"\n}\n<\/script>    <\/div>\n<\/div>\n<!-- \/module plain text --><!-- Breadcrumbs module -->\n<div  class=\"module module-breadcrumbs tb_0g0f927 \" data-lazy=\"1\">\n\t<nav role=\"navigation\" aria-label=\"Breadcrumbs\" class=\"tbp_breadcrumb_trail\"><ul class=\"tbp_trail_items\" itemscope itemtype=\"http:\/\/schema.org\/BreadcrumbList\"><meta name=\"numberOfItems\" content=\"1\" \/><meta name=\"itemListOrder\" content=\"Ascending\" \/><li itemprop=\"itemListElement\" itemscope itemtype=\"https:\/\/schema.org\/ListItem\" class=\"tbp_trail_item tbp_trail_end\"><a itemprop=\"item\" href=\"https:\/\/frankmasotti.com\/insights\/\" rel=\"home\"><span itemprop=\"name\">Home<\/span><\/a><meta itemprop=\"position\" content=\"1\" \/><\/li><\/ul><\/nav><\/div><!-- \/Breadcrumbs module -->\n<!-- module image -->\n<div  class=\"module module-image tb_2n8u214 image-center rounded drop-shadow  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" width=\"640\" height=\"360\" src=\"https:\/\/frankmasotti.com\/insights\/wp-content\/uploads\/2026\/09\/What-Disagreement-Between-AI-Models-Can-Reveal-About-Your-Business.webp\" class=\"wp-post-image wp-image-3728\" title=\"What Disagreement Between AI Models Can Reveal About Your Business\" alt=\"What Disagreement Between AI Models Can Reveal About Your Business\" srcset=\"https:\/\/frankmasotti.com\/insights\/wp-content\/uploads\/2026\/09\/What-Disagreement-Between-AI-Models-Can-Reveal-About-Your-Business.webp 640w, https:\/\/frankmasotti.com\/insights\/wp-content\/uploads\/2026\/09\/What-Disagreement-Between-AI-Models-Can-Reveal-About-Your-Business-300x169.webp 300w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image --><!-- module text -->\n<div  class=\"module module-text tb_o1ac927   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>What Disagreement Between AI Models Can Reveal About Your Business<\/h2><p>If ChatGPT, Claude, and Gemini give three different descriptions of your business, the disagreement is not noise. It is a diagnosis. Where the models split tells you which facts about your business are weakly supported, and the way they split usually points to why: a name you share with someone else, an old version of your business still circulating, a specialty your own material never ranks, or evidence so thin that one model guesses while another hedges.<\/p><p>The part most owners miss is that the three models are not three opinions formed from the same file. When they search the live web, they largely read from different indexes. So when one model is right about your business and another is wrong, the difference often traces back to a specific place where your information is current and another place where it is not. Read carefully, a disagreement is less like an argument and more like a trail.<\/p><h2>Why Disagreement Gets Dismissed<\/h2><p>The usual reaction to three different answers is to write it off as AI being inconsistent, or to keep the flattering answer and ignore the other two. Both reactions throw away the most useful thing in front of you.<\/p><p>Agreement is reassuring but quiet. When all three models describe you the same way, you learn that a fact is well supported, although, as covered in <a href=\"https:\/\/frankmasotti.com\/insights\/what-agreement-between-chatgpt-claude-and-gemini-can-tell-you\/\">What Agreement Between ChatGPT, Claude and Gemini Can Tell You<\/a>, three models can also agree on the same mistake. Disagreement is louder, and it is specific. It marks the exact spot where the evidence about your business stops telling one story.<\/p><p><a href=\"https:\/\/frankmasotti.com\/insights\/what-if-chatgpt-claude-and-gemini-know-three-different-versions-of-your-business\/\">What If ChatGPT, Claude, and Gemini Know Three Different Versions of Your Business?<\/a> made the case that divergence is a measurement of your information rather than a malfunction in the software. This post is about reading that measurement: what each kind of split means, and where it tells you to look.<\/p><h2>Three Readers With Different Reading Lists<\/h2><p>Start with the mechanism, because it changes how you interpret everything else.<\/p><p>Each model carries a baseline understanding learned from training data gathered at different points in time. When a question calls for current information, each one also searches the web, and they do not search the same web. Gemini is grounded in Google Search. Claude&#8217;s web search runs on Brave Search, which Anthropic lists as a web search vendor and which maintains its own independent index rather than reselling Google or Bing. ChatGPT draws on a blended stack: Bing&#8217;s index, which supplied its original search results, plus OpenAI&#8217;s own crawler, and OpenAI does not fully document how those pieces divide the work.<\/p><p>The effect shows up clearly in recent data. MentionBird asked ChatGPT and Gemini the same 517 commercial questions over 17 weeks this year and found they cited the same domain only 12.7 percent of the time, and the same page only 5.8 percent of the time. Slate, running 1,000 questions through four AI engines in August, found they named the same vendors only about 35 to 42 percent of the time. The models are not consulting one shared record of your business and interpreting it three ways. Much of the time, they are reading different pages.<\/p><p>That is why disagreement carries information. If a correction you made lives mostly inside Google&#8217;s ecosystem, Gemini has the shortest path to it. If a page is new and has had few visitors, it may not be in Brave&#8217;s index yet, so Claude answers from older material. Where each model lands often reflects what its particular pipeline could reach.<\/p><h2>Reading the Shape of the Split<\/h2><p>Not every disagreement means the same thing. In practice they fall into a handful of recognizable patterns, and each one points to a different cause.<\/p><p><strong>They disagree about who you are.<\/strong> One model describes your business. Another describes a company with a similar name in another state, or blends the two into one. This is an identity problem, not a description problem, and better copy about your services will not fix it until the models can tell you apart. <a href=\"https:\/\/frankmasotti.com\/insights\/entity-confusion-between-similar-businesses\/\">Entity Confusion Between Similar Businesses<\/a> covers how that blending happens.<\/p><p><strong>They disagree about when.<\/strong> One model describes the business you run today. Another describes the one you ran five years ago: a service you dropped, an address you left, a market you exited. The split runs along a timeline, and the model that is current is usually the one whose sources picked up your change. That tells you where your correction landed and, more usefully, where it did not. It is the same dynamic described in <a href=\"https:\/\/frankmasotti.com\/insights\/what-happens-when-your-website-says-one-thing-and-older-sources-say-another\/\">What Happens When Your Website Says One Thing and Older Sources Say Another<\/a>, made visible model by model.<\/p><p><strong>They agree on the facts but disagree on what leads.<\/strong> All three list the same services, but each puts a different one first. This looks harmless and is not. It means nothing in your material establishes which service is your core and which are secondary, so each model ranks them by whatever it happened to read most. That ordering becomes <a href=\"https:\/\/frankmasotti.com\/insights\/how-ai-decides-what-your-business-is-known-for\/\">what AI decides your business is known for<\/a>, and right now it depends on which assistant your prospect opened.<\/p><p><strong>One commits and another hedges.<\/strong> One model describes you with confidence. Another says you &#8220;appear to offer&#8221; something. Owners naturally trust the confident answer, but the hedging model is often the more honest one. It is telling you the evidence is thin, while the confident model filled the same gap with a guess or a category default. <a href=\"https:\/\/frankmasotti.com\/insights\/what-ai-uncertainty-looks-like-when-it-describes-a-business\/\">What AI Uncertainty Looks Like When It Describes a Business<\/a> walks through the tells.<\/p><p><strong>They agree until the question turns to fit.<\/strong> All three describe what you do in similar terms, then split when asked who you are best for or whether you suit a particular project. That pattern means your identity is established but the information that decides a sale is not, which is the gap explored in <a href=\"https:\/\/frankmasotti.com\/insights\/what-does-ai-actually-know-about-who-your-business-is-best-for\/\">What Does AI Actually Know About Who Your Business Is Best For?<\/a><\/p><h2>The Minority Answer Is Where the Investigation Starts<\/h2><p>Majority rule is tempting. If two models say one thing and the third says another, it feels natural to assume the two are right. But the models are not voting, and a majority built on the same stale directory listing is still wrong.<\/p><p>The useful move is to check every version against how your business actually operates, then look hardest at the odd one out. If the lone dissenter is the accurate one, it shows you which source is carrying the correct story and which sources the other two models are still relying on. If the lone dissenter is wrong, it usually latched onto something specific: an old profile, a namesake&#8217;s review, a page that still describes a discontinued service. Either way, the minority answer tends to lead to a concrete source, and a concrete source is the only thing you can actually fix.<\/p><h2>When a Disagreement Is Not a Finding<\/h2><p>One caution keeps this honest. A single difference between two answers proves very little, because a single answer is itself unstable. In research published in January, SparkToro and Gumshoe found that when AI tools were asked the same recommendation question over and over, the same list came back less than one time in a hundred. Two models differing once may be nothing more than two draws from targets that are both moving.<\/p><p>A disagreement becomes a finding when it repeats: when the same split shows up across several questions, asked from several angles, in fresh sessions with no prior context. That discipline is the difference explained in <a href=\"https:\/\/frankmasotti.com\/insights\/why-asking-chatgpt-about-your-business-once-is-not-an-ai-search-engine-audit\/\">Why Asking ChatGPT About Your Business Once Is Not an AI Search Engine Audit<\/a>. Patterned disagreement reveals the structure of your information. A single difference reveals only that AI answers vary.<\/p><h2>What an Unread Disagreement Costs<\/h2><p>Your prospects never see the disagreement. Each of them asks one assistant and receives one answer, delivered with the same confidence as the other two. The prospect who drew the identity split heard about someone else&#8217;s business. The one who drew the timeline split heard about services you stopped offering. The one who drew the hedge heard a shrug. None of them will tell you which version they got, and none of those losses leaves a trace in your analytics.<\/p><p>Meanwhile, the disagreement sits there as a diagnosis nobody reads. It already contains the location of your weak points. It simply has to be collected, repeated enough to trust, and interpreted.<\/p><h2>Turning Disagreement Into a Diagnosis<\/h2><p>That is the work at the center of the <a href=\"https:\/\/frankmasotti.com\/how-it-works.html\">AI Business Understanding Report<\/a>. I put the same 15 questions to ChatGPT, Claude, and Gemini, each in a fresh session, which produces forty two answers about your business. I read every one, check each against how your business actually operates, and document where the models agree, where they split, what kind of split it is, and what it points to. The <a href=\"https:\/\/frankmasotti.com\/methodology.html\">methodology<\/a> explains how the responses are preserved and compared, and <a href=\"https:\/\/frankmasotti.com\/answers-why-three-ai-models.html\">Why Three AI Models?<\/a> covers why the comparison, rather than any single answer, is the point.<\/p><p>A disagreement between AI models is your own information telling you where it is weak. If you want to know what the disagreement about your business says, and where it points, <a href=\"https:\/\/frankmasotti.com\/order-now.html\">ordering a report<\/a> is how you read it.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>If ChatGPT, Claude, and Gemini give three different descriptions of your business, the disagreement is not noise.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-3727","post","type-post","status-publish","format-standard","hentry","category-frank-masottis-insights","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2 - 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It is a diagnosis. Where the models split tells you which facts about your business are weakly supported, and the way they split usually points to why: a name you share with someone else, an old version of your business still circulating, a specialty your own material never ranks, or evidence so thin that one model guesses while another hedges.<\/p><p>The part most owners miss is that the three models are not three opinions formed from the same file. When they search the live web, they largely read from different indexes. So when one model is right about your business and another is wrong, the difference often traces back to a specific place where your information is current and another place where it is not. Read carefully, a disagreement is less like an argument and more like a trail.<\/p><h2>Why Disagreement Gets Dismissed<\/h2><p>The usual reaction to three different answers is to write it off as AI being inconsistent, or to keep the flattering answer and ignore the other two. Both reactions throw away the most useful thing in front of you.<\/p><p>Agreement is reassuring but quiet. When all three models describe you the same way, you learn that a fact is well supported, although, as covered in <a href=\"https:\/\/frankmasotti.com\/insights\/what-agreement-between-chatgpt-claude-and-gemini-can-tell-you\/\">What Agreement Between ChatGPT, Claude and Gemini Can Tell You<\/a>, three models can also agree on the same mistake. Disagreement is louder, and it is specific. It marks the exact spot where the evidence about your business stops telling one story.<\/p><p><a href=\"https:\/\/frankmasotti.com\/insights\/what-if-chatgpt-claude-and-gemini-know-three-different-versions-of-your-business\/\">What If ChatGPT, Claude, and Gemini Know Three Different Versions of Your Business?<\/a> made the case that divergence is a measurement of your information rather than a malfunction in the software. This post is about reading that measurement: what each kind of split means, and where it tells you to look.<\/p><h2>Three Readers With Different Reading Lists<\/h2><p>Start with the mechanism, because it changes how you interpret everything else.<\/p><p>Each model carries a baseline understanding learned from training data gathered at different points in time. When a question calls for current information, each one also searches the web, and they do not search the same web. Gemini is grounded in Google Search. Claude's web search runs on Brave Search, which Anthropic lists as a web search vendor and which maintains its own independent index rather than reselling Google or Bing. ChatGPT draws on a blended stack: Bing's index, which supplied its original search results, plus OpenAI's own crawler, and OpenAI does not fully document how those pieces divide the work.<\/p><p>The effect shows up clearly in recent data. MentionBird asked ChatGPT and Gemini the same 517 commercial questions over 17 weeks this year and found they cited the same domain only 12.7 percent of the time, and the same page only 5.8 percent of the time. Slate, running 1,000 questions through four AI engines in August, found they named the same vendors only about 35 to 42 percent of the time. The models are not consulting one shared record of your business and interpreting it three ways. Much of the time, they are reading different pages.<\/p><p>That is why disagreement carries information. If a correction you made lives mostly inside Google's ecosystem, Gemini has the shortest path to it. If a page is new and has had few visitors, it may not be in Brave's index yet, so Claude answers from older material. Where each model lands often reflects what its particular pipeline could reach.<\/p><h2>Reading the Shape of the Split<\/h2><p>Not every disagreement means the same thing. In practice they fall into a handful of recognizable patterns, and each one points to a different cause.<\/p><p><strong>They disagree about who you are.<\/strong> One model describes your business. Another describes a company with a similar name in another state, or blends the two into one. This is an identity problem, not a description problem, and better copy about your services will not fix it until the models can tell you apart. <a href=\"https:\/\/frankmasotti.com\/insights\/entity-confusion-between-similar-businesses\/\">Entity Confusion Between Similar Businesses<\/a> covers how that blending happens.<\/p><p><strong>They disagree about when.<\/strong> One model describes the business you run today. Another describes the one you ran five years ago: a service you dropped, an address you left, a market you exited. The split runs along a timeline, and the model that is current is usually the one whose sources picked up your change. That tells you where your correction landed and, more usefully, where it did not. It is the same dynamic described in <a href=\"https:\/\/frankmasotti.com\/insights\/what-happens-when-your-website-says-one-thing-and-older-sources-say-another\/\">What Happens When Your Website Says One Thing and Older Sources Say Another<\/a>, made visible model by model.<\/p><p><strong>They agree on the facts but disagree on what leads.<\/strong> All three list the same services, but each puts a different one first. This looks harmless and is not. It means nothing in your material establishes which service is your core and which are secondary, so each model ranks them by whatever it happened to read most. That ordering becomes <a href=\"https:\/\/frankmasotti.com\/insights\/how-ai-decides-what-your-business-is-known-for\/\">what AI decides your business is known for<\/a>, and right now it depends on which assistant your prospect opened.<\/p><p><strong>One commits and another hedges.<\/strong> One model describes you with confidence. Another says you \"appear to offer\" something. Owners naturally trust the confident answer, but the hedging model is often the more honest one. It is telling you the evidence is thin, while the confident model filled the same gap with a guess or a category default. <a href=\"https:\/\/frankmasotti.com\/insights\/what-ai-uncertainty-looks-like-when-it-describes-a-business\/\">What AI Uncertainty Looks Like When It Describes a Business<\/a> walks through the tells.<\/p><p><strong>They agree until the question turns to fit.<\/strong> All three describe what you do in similar terms, then split when asked who you are best for or whether you suit a particular project. That pattern means your identity is established but the information that decides a sale is not, which is the gap explored in <a href=\"https:\/\/frankmasotti.com\/insights\/what-does-ai-actually-know-about-who-your-business-is-best-for\/\">What Does AI Actually Know About Who Your Business Is Best For?<\/a><\/p><h2>The Minority Answer Is Where the Investigation Starts<\/h2><p>Majority rule is tempting. If two models say one thing and the third says another, it feels natural to assume the two are right. But the models are not voting, and a majority built on the same stale directory listing is still wrong.<\/p><p>The useful move is to check every version against how your business actually operates, then look hardest at the odd one out. If the lone dissenter is the accurate one, it shows you which source is carrying the correct story and which sources the other two models are still relying on. If the lone dissenter is wrong, it usually latched onto something specific: an old profile, a namesake's review, a page that still describes a discontinued service. Either way, the minority answer tends to lead to a concrete source, and a concrete source is the only thing you can actually fix.<\/p><h2>When a Disagreement Is Not a Finding<\/h2><p>One caution keeps this honest. A single difference between two answers proves very little, because a single answer is itself unstable. In research published in January, SparkToro and Gumshoe found that when AI tools were asked the same recommendation question over and over, the same list came back less than one time in a hundred. Two models differing once may be nothing more than two draws from targets that are both moving.<\/p><p>A disagreement becomes a finding when it repeats: when the same split shows up across several questions, asked from several angles, in fresh sessions with no prior context. That discipline is the difference explained in <a href=\"https:\/\/frankmasotti.com\/insights\/why-asking-chatgpt-about-your-business-once-is-not-an-ai-search-engine-audit\/\">Why Asking ChatGPT About Your Business Once Is Not an AI Search Engine Audit<\/a>. Patterned disagreement reveals the structure of your information. A single difference reveals only that AI answers vary.<\/p><h2>What an Unread Disagreement Costs<\/h2><p>Your prospects never see the disagreement. Each of them asks one assistant and receives one answer, delivered with the same confidence as the other two. The prospect who drew the identity split heard about someone else's business. The one who drew the timeline split heard about services you stopped offering. The one who drew the hedge heard a shrug. None of them will tell you which version they got, and none of those losses leaves a trace in your analytics.<\/p><p>Meanwhile, the disagreement sits there as a diagnosis nobody reads. It already contains the location of your weak points. It simply has to be collected, repeated enough to trust, and interpreted.<\/p><h2>Turning Disagreement Into a Diagnosis<\/h2><p>That is the work at the center of the <a href=\"https:\/\/frankmasotti.com\/how-it-works.html\">AI Business Understanding Report<\/a>. I put the same 15 questions to ChatGPT, Claude, and Gemini, each in a fresh session, which produces forty two answers about your business. I read every one, check each against how your business actually operates, and document where the models agree, where they split, what kind of split it is, and what it points to. The <a href=\"https:\/\/frankmasotti.com\/methodology.html\">methodology<\/a> explains how the responses are preserved and compared, and <a href=\"https:\/\/frankmasotti.com\/answers-why-three-ai-models.html\">Why Three AI Models?<\/a> covers why the comparison, rather than any single answer, is the point.<\/p><p>A disagreement between AI models is your own information telling you where it is weak. If you want to know what the disagreement about your business says, and where it points, <a href=\"https:\/\/frankmasotti.com\/order-now.html\">ordering a report<\/a> is how you read it.<\/p>","_links":{"self":[{"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/posts\/3727","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/comments?post=3727"}],"version-history":[{"count":5,"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/posts\/3727\/revisions"}],"predecessor-version":[{"id":3733,"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/posts\/3727\/revisions\/3733"}],"wp:attachment":[{"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/media?parent=3727"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/categories?post=3727"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/frankmasotti.com\/insights\/wp-json\/wp\/v2\/tags?post=3727"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}