{"id":3631,"date":"2026-04-11T18:35:41","date_gmt":"2026-04-11T18:35:41","guid":{"rendered":"https:\/\/blog.coffee.ai\/best-meddpicc-ai-tools-2026\/"},"modified":"2026-07-22T05:15:14","modified_gmt":"2026-07-22T05:15:14","slug":"best-meddpicc-ai-tools-2026","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/best-meddpicc-ai-tools-2026","title":{"rendered":"Best MEDDPICC AI Tools for Enterprise B2B Sales in 2026"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: July 21, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Enterprise MEDDPICC Teams<\/h2>\n<ul>\n<li>Enterprise B2B sales teams lose large blocks of time to post-call documentation and manual CRM updates, which blocks consistent MEDDPICC qualification at scale.<\/li>\n<li>Autonomous AI agents remove rep data entry by capturing MEDDPICC evidence from calls, emails, and calendars without human input, unlike passive conversation intelligence platforms.<\/li>\n<li>Coffee&#8217;s Companion App leads this evaluation by offering native Salesforce and HubSpot integration, zero-rep MEDDPICC field population, and 8\u201312 hours of time savings per rep per week.<\/li>\n<li>Enterprise teams with 100\u20132,000+ seats gain the most from companion agents that write directly into existing CRM instances, avoid rip-and-replace migrations, and preserve validation rules and reporting.<\/li>\n<li>Teams ready to deploy autonomous MEDDPICC qualification should <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">explore Coffee&#8217;s deployment options<\/a> to experience zero-rep data capture inside their existing Salesforce or HubSpot environment.<\/li>\n<\/ul>\n<h2>Why Enterprise Teams Are Moving to Autonomous MEDDPICC Agents<\/h2>\n<p>Enterprise sales reps spend only about 35% of their time selling according to market data shared by Coffee. The rest goes to post-call documentation, CRM updates, and qualification tasks. For teams running MEDDPICC, that overhead compounds quickly. Manual post-call CRM updates, including logging MEDDPICC criteria, noting next steps, and setting follow-up tasks, can consume several minutes per call. Across a 50-rep team running five calls per day, that overhead becomes hours of non-selling labor every day.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678549697-4e8d65abe17d.gif\" alt=\"GIF of Coffee platform where user is using AI to prep for a meeting with Coffee AI\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Automated meeting prep with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<p>The qualification consistency problem creates direct revenue impact. Sales organizations that adopt structured qualification frameworks often fail to maintain consistent usage over time, and leaders cite manual entry burden as the main barrier. In 2023, median quota attainment for account executives was around 50% and roughly 72% of reps missed quota. Poor deal qualification, often caused by abandoned or inconsistently applied frameworks, was identified as a root cause.<\/p>\n<p>The broader market is shifting toward agentic AI to address this gap. <a href=\"https:\/\/laxis.com\/blog\/state-of-ai-sales-agent-2026\" target=\"_blank\" rel=\"noindex nofollow\">Seventy-five percent of B2B sales organizations are expected to incorporate AI-driven sales development by the end of 2026<\/a>. <a href=\"https:\/\/gravity.fast\/blog\/ai-agent-adoption-statistics-2026\" target=\"_blank\" rel=\"noindex nofollow\">Gartner forecasts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025<\/a>. Yet adoption and production deployment remain misaligned. <a href=\"https:\/\/laxis.com\/blog\/state-of-ai-sales-agent-2026\" target=\"_blank\" rel=\"noindex nofollow\">While 79% of organizations report some level of agentic AI adoption, only 51% run agents in production<\/a>. The gap between piloting and operationalizing is where most enterprise MEDDPICC initiatives stall.<\/p>\n<h2>How This Guide Evaluates MEDDPICC AI Tools<\/h2>\n<p>To help enterprise teams close the pilot-to-production gap, this guide evaluates leading MEDDPICC AI tools against seven criteria that address the operational barriers described above. These criteria cover autonomous capture, CRM integration depth, data quality, and deployment effort so leaders can compare tools on outcomes, not hype. Every tool in this guide is assessed against the same seven criteria before any vendor discussion begins.<\/p>\n<ol>\n<li><strong>Autonomous data capture and enrichment:<\/strong> The tool must capture MEDDPICC evidence from calls, emails, and calendars without rep action, rather than relying on manual review and submission.<\/li>\n<li><strong>Salesforce\/HubSpot integration depth:<\/strong> The tool should write to standard and custom Opportunity fields natively, respect validation rules and stage-gate logic, and support two-way sync.<\/li>\n<li><strong>MEDDPICC field population accuracy:<\/strong> The platform needs a documented accuracy rate on structured qualification fields, with evidence traceable to a specific conversation or email.<\/li>\n<li><strong>Rep time saved:<\/strong> The vendor should demonstrate a measurable reduction in post-call admin and manual CRM entry per rep per week.<\/li>\n<li><strong>Forecast reliability:<\/strong> The tool should improve forecast variance by grounding pipeline data in conversation evidence instead of rep self-reporting.<\/li>\n<li><strong>Implementation effort:<\/strong> Leaders need clarity on deployment timelines and the internal engineering or RevOps resources required.<\/li>\n<li><strong>Total cost of ownership:<\/strong> Evaluation must include all-in per-seat cost, including implementation, add-ons, and CRM licensing overhead.<\/li>\n<\/ol>\n<h2>Side-by-Side Comparison of Leading MEDDPICC AI Tools<\/h2>\n<p>The table below compares four directly measurable dimensions across tools. Metrics that do not share a common unit or scale are covered in the narrative that follows.<\/p>\n<table>\n<thead>\n<tr>\n<th>Tool<\/th>\n<th>Autonomous Capture<\/th>\n<th>Native CRM Write-Back<\/th>\n<th>Reported Time Savings<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Coffee (Companion App)<\/td>\n<td>Yes, the agent captures calls, emails, and calendar data with zero rep action and populates MEDDPICC and custom fields automatically.<\/td>\n<td>Yes, native Salesforce and HubSpot sync that writes structured qualification data, activity logs, and enriched contact records.<\/td>\n<td><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">8\u201312 hours saved per rep per week<\/a> on data entry and admin.<\/td>\n<\/tr>\n<tr>\n<td>Clari<\/td>\n<td>Partial, Wingman\/Clari Copilot captures conversation signals, but forecasting and pipeline inspection still require rep-confirmed data inputs.<\/td>\n<td>Yes, integrates with Salesforce and HubSpot for forecasting and pipeline workflows, though write-back depth varies by module.<\/td>\n<td>Not independently verified at a per-rep weekly level in available 2026 data.<\/td>\n<\/tr>\n<tr>\n<td>Gong<\/td>\n<td>Partial, records and transcribes calls and surfaces MEDDPICC signals via Smart Trackers, but <a href=\"https:\/\/goairspeed.com\/blog\/ai-revenue-intelligence-platforms-salesforce-hubspot\" target=\"_blank\" rel=\"noindex nofollow\">focuses on analysis instead of automating CRM execution<\/a>.<\/td>\n<td>Partial, connects to Salesforce and HubSpot with deep analytics, while field write-back requires configuration and rep review steps.<\/td>\n<td>Sales reps using AI tools broadly report more time selling, but Gong-specific autonomous write-back savings are not independently published.<\/td>\n<\/tr>\n<tr>\n<td>Scratchpad<\/td>\n<td>No, functions as a rep-facing notepad and pipeline editor, so data entry is rep-initiated rather than autonomous.<\/td>\n<td>Yes, writes rep-entered notes and field edits to Salesforce, but does not capture from calls or emails autonomously.<\/td>\n<td>Reduces friction of manual Salesforce entry; autonomous capture savings do not apply to this architecture.<\/td>\n<\/tr>\n<tr>\n<td>Glyphic AI (now Airspeed)<\/td>\n<td>Yes, <a href=\"https:\/\/goairspeed.com\/blog\/ai-revenue-intelligence-platforms-salesforce-hubspot\" target=\"_blank\" rel=\"noindex nofollow\">scores MEDDIC, BANT, and SPICED from conversation data without manual input and writes qualification scores back to the CRM<\/a>.<\/td>\n<td>Yes, <a href=\"https:\/\/goairspeed.com\/blog\/ai-revenue-intelligence-platforms-salesforce-hubspot\" target=\"_blank\" rel=\"noindex nofollow\">writes call summaries, next steps, contacts, and qualification scores to 20+ mapped CRM fields within approximately five minutes of call completion<\/a>.<\/td>\n<td>Not independently published at a per-rep weekly level in available 2026 data.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Clari excels at forecasting aggregation and pipeline inspection at the RevOps layer, but it is not built as a zero-rep qualification capture agent. Gong delivers strong conversation analytics and coaching at scale, yet its MEDDPICC enforcement depends on Smart Tracker configuration and rep review instead of autonomous field population. Scratchpad speeds up rep-initiated CRM edits but keeps humans in the data entry loop. Airspeed (formerly Glyphic) is the closest architectural peer to Coffee in this group, with native write-back and autonomous scoring, although its enterprise Salesforce depth and companion-app positioning for large-instance deployments differ from Coffee&#8217;s purpose-built approach.<\/p>\n<h2>Autonomous Agents Compared to Passive Conversation Intelligence<\/h2>\n<p><a href=\"https:\/\/spotlight.ai\/post\/conversation-intelligence-b2b-sales\" target=\"_blank\" rel=\"noindex nofollow\">Autonomous deal execution platforms structure conversation data into evidence-based MEDDPICC qualification, update CRM fields, and execute follow-on actions, while conversation intelligence platforms stop after transcription and keyword-based analysis without qualifying deals or reducing rep data entry.<\/a> This architectural difference determines whether a tool fixes the qualification consistency problem or only highlights it.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678412915-a11943d2b0b8.gif\" alt=\"Join a meeting from the Coffee AI platform\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Join a meeting from the Coffee AI platform<\/em><\/figcaption><\/figure>\n<p>Passive platforms produce unstructured outputs such as transcripts, keyword flags, and coaching alerts that a rep or manager must translate into CRM fields. <a href=\"https:\/\/spotlight.ai\/post\/meddpicc-autopilot-how-spotlight-ai-automates-deal-qualification\" target=\"_blank\" rel=\"noindex nofollow\">Fields that previously went empty for weeks now populate immediately after each interaction when an autonomous agent handles extraction<\/a>. That shift keeps data fresh in a way passive tools cannot match. MEDDPICC qualification fields can reach high accuracy because the framework uses clearly named slots that map cleanly to extraction logic.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678321672-5c8717cf0024.gif\" alt=\"Create instant meeting follow-up emails with the Coffee AI CRM agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Create instant meeting follow-up emails with the Coffee AI CRM agent<\/em><\/figcaption><\/figure>\n<p>The companion-app model fits enterprise teams already on Salesforce or HubSpot. Instead of replacing the system of record, a companion agent writes enriched, evidence-backed MEDDPICC data into the existing instance and preserves validation rules, stage-gate logic, and reporting infrastructure. <a href=\"https:\/\/laxis.com\/blog\/state-of-ai-sales-agent-2026\" target=\"_blank\" rel=\"noindex nofollow\">AI sales agents deliver typical first-year ROI of 300\u2013500%, with realistic payback of 9\u201312 months when utilization stays above 75%, while returns collapse without automatic two-way CRM write-back.<\/a> That requirement for write-back is the critical differentiator, because passive platforms without it cannot deliver compounding ROI regardless of analytical sophistication.<\/p>\n<p><a href=\"https:\/\/gravitasconsulting.com\/insight\/agentic-ai-for-sales\" target=\"_blank\" rel=\"noindex nofollow\">Bain estimates that early sales deployments of agentic AI produce 30%+ improvements in win rates when teams rethink the underlying sales process instead of simply automating existing steps.<\/a> For MEDDPICC enforcement, autonomous agents that enforce qualification at the data-capture layer change the process itself. Passive platforms that surface gaps after the fact leave the old process intact.<\/p>\n<h2>Best-Fit MEDDPICC Architectures by Team Size and CRM Stack<\/h2>\n<p>For mid-market and enterprise teams with 100 to 2,000+ seats already on Salesforce or HubSpot, the companion-app model is usually the most practical choice. A rip-and-replace CRM migration at this scale introduces change management risk, data governance complexity, and multi-quarter implementation timelines. Autonomous qualification agents are designed to avoid those costs. Coffee&#8217;s Companion App authenticates against an existing Salesforce or HubSpot instance and begins writing MEDDPICC-structured data immediately, without rebuilding the system of record.<\/p>\n<p>Enterprise deployments must also account for several operational considerations.<\/p>\n<ul>\n<li><strong>Change management:<\/strong> <a href=\"https:\/\/matheusvizotto.com\/blog\/ai-literacy-training-roi-2026\" target=\"_blank\" rel=\"noindex nofollow\">Organizations with structured AI training see employee adoption rates increase from 25% to 76%.<\/a> Companion agents reduce the training surface because reps keep using the same CRM interface they already know.<\/li>\n<li><strong>Data governance:<\/strong> Coffee is SOC 2 Type 2 and GDPR compliant, and the platform does not use customer data to train public models, which many regulated-adjacent enterprises treat as a hard requirement.<\/li>\n<li><strong>Scalability:<\/strong> Seat-based pricing with no metering on LLM usage or agent processes keeps cost growth tied to headcount instead of call volume or field-update frequency.<\/li>\n<li><strong>CRM integration depth:<\/strong> <a href=\"https:\/\/knowlee.ai\/blog\/ai-sales-tools-for-enterprise-2026\" target=\"_blank\" rel=\"noindex nofollow\">Enterprise AI sales platforms in 2026 require two-way sync with Salesforce, support for custom objects, respect for formula fields, and awareness of governor limits to achieve deep integration beyond basic connectivity.<\/a> Coffee&#8217;s integration architecture targets the complexity of production Salesforce and HubSpot instances, including quota management, required fields, and forecasting hierarchies.<\/li>\n<\/ul>\n<p>Smaller teams that need a full system of record, typically one to twenty seats without an existing enterprise CRM, are better served by Coffee&#8217;s Standalone AI-First CRM. In that setup, the agent manages the entire system of record instead of augmenting an existing one.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>See Coffee pricing and deployment paths to bring autonomous MEDDPICC qualification into your Salesforce or HubSpot instance.<\/strong><\/a><\/p>\n<h2>Risks, Limitations, and a Practical Decision Framework<\/h2>\n<p>No software tool enforces process discipline by itself. <a href=\"https:\/\/gravitasconsulting.com\/insight\/agentic-ai-for-sales\" target=\"_blank\" rel=\"noindex nofollow\">Ninety-five percent of enterprise generative AI pilots deliver no measurable P&amp;L impact<\/a>, and <a href=\"https:\/\/gravity.fast\/blog\/ai-agent-adoption-statistics-2026\" target=\"_blank\" rel=\"noindex nofollow\">Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.<\/a> The failure patterns are consistent. Teams struggle with broken CRM write-back, weak manager adoption of AI-surfaced insights, and fragmentation across point tools that lose context between outreach and meetings.<\/p>\n<p>Objective risks by tool category appear below.<\/p>\n<ul>\n<li><strong>Passive conversation intelligence (Gong, Clari Copilot):<\/strong> These tools provide strong analytics but keep the rep data entry burden in place, so MEDDPICC consistency still depends on individual rep discipline instead of automated enforcement.<\/li>\n<li><strong>Rep-initiated editors (Scratchpad):<\/strong> These platforms reduce friction for manual entry but do not remove it, which means qualification completeness remains a human variable.<\/li>\n<li><strong>Autonomous agents (Coffee, Airspeed):<\/strong> These tools require a one-time integration setup, and accuracy on nuanced fields such as Implicit Pain and Champion improves over a tuning period of about two weeks according to published benchmarks.<\/li>\n<li><strong>All categories:<\/strong> <a href=\"https:\/\/blog.brazn.ai\/content\/the-state-of-ai-in-b2b-sales-2026\" target=\"_blank\" rel=\"noindex nofollow\">The biggest story of 2026 in B2B sales AI is the deployment versus adoption gap, where tools are deployed but only a minority of reps use them consistently.<\/a> Manager reinforcement of AI-sourced qualification data in pipeline reviews is essential for real ROI.<\/li>\n<\/ul>\n<p>The decision framework by CRM stack, team size, and primary need breaks down clearly. Enterprise teams on Salesforce or HubSpot with 100+ seats and a MEDDPICC enforcement mandate should evaluate Coffee&#8217;s Companion App first. Teams that need deep forecasting aggregation across a complex multi-CRM environment should layer Clari on top of an autonomous capture agent instead of using it as a standalone qualification solution. Teams that prioritize coaching and deal analytics over zero-rep capture should evaluate Gong with explicit write-back configuration. Smaller teams that require a full system of record replacement should evaluate Coffee&#8217;s Standalone CRM.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does it take to implement a companion agent for MEDDPICC qualification?<\/h3>\n<p>Coffee&#8217;s Companion App implementation starts with a simple authentication against an existing Salesforce or HubSpot instance. The agent begins capturing and writing data immediately after connection. Full MEDDPICC field accuracy on nuanced elements such as Champion and Implicit Pain improves after a short tuning period, while the agent calibrates extraction logic to the team&#8217;s deal language and CRM field configuration. Teams avoid multi-month implementation projects, custom object rebuilds, and data migrations.<\/p>\n<h3>How much migration effort is required when moving from an existing conversation intelligence tool?<\/h3>\n<p>The companion-app model avoids disruptive migration. Coffee writes into the same Salesforce or HubSpot instance a team already uses and preserves existing pipeline data, validation rules, stage-gate logic, dashboards, and reporting. Teams running Gong or Clari alongside Coffee can keep those platforms for analytics and forecasting while Coffee manages autonomous data capture. The architectures complement each other instead of forcing a rip-and-replace decision.<\/p>\n<h3>What internal expertise is required to operate an autonomous MEDDPICC agent?<\/h3>\n<p>Coffee&#8217;s Companion App targets RevOps leaders and Heads of Sales rather than engineering teams. The initial CRM connection uses standard OAuth authentication. MEDDPICC field mapping relies on a one-time configuration interface that aligns Coffee&#8217;s extraction outputs with existing custom fields on the Opportunity object. Ongoing operation does not require SQL, API development, or LLM prompt engineering. After mapping, the agent handles data unification, enrichment, and field population autonomously.<\/p>\n<h3>How does autonomous agent data quality compare to third-party enrichment providers?<\/h3>\n<p>Coffee&#8217;s agent delivers contact and company enrichment, including job titles, funding data, and LinkedIn profiles, through licensed data partners at a quality level comparable to dedicated enrichment tools for most enterprise needs. For MEDDPICC-specific qualification fields, extraction from actual call transcripts and emails produces higher accuracy than third-party enrichment, because the data comes from direct buyer conversations instead of firmographic inference. Evidence links back to a specific call timestamp or email thread, which makes it auditable in pipeline reviews in a way that rep-entered or enrichment-sourced data is not.<\/p>\n<h3>How should enterprise teams evaluate companion agents versus official MEDDPICC certification platforms?<\/h3>\n<p>Official MEDDPICC certification platforms, such as MEDDICC Ltd&#8217;s training programs, focus on methodology adoption and rep skill development. Companion agents focus on methodology enforcement and data capture. These functions complement each other rather than compete. A team that completes MEDDPICC training but lacks an autonomous capture agent will still see inconsistent CRM data because enforcement relies on rep discipline. A team that deploys an autonomous agent without methodology training may populate fields accurately but lack the deal-execution skills to act on qualification gaps. The highest-performing teams in 2026 pair structured methodology training with autonomous agents that enforce the methodology at the data layer and remove the manual entry burden that often causes framework abandonment after twelve months.<\/p>\n<h2>Conclusion: Selecting the Right MEDDPICC AI Tool for Your Org<\/h2>\n<p>Research across 2025 and 2026 shows a consistent pattern. Passive conversation intelligence platforms improve visibility into qualification gaps but do not close them, because they keep the data entry burden on reps. <a href=\"https:\/\/www.ringlyn.com\/blog\/ai-call-summaries-analytics-roi-2026\/\" target=\"_blank\" rel=\"noindex nofollow\">Post-call automation eliminates 2\u201310 minutes of admin after every call<\/a>. That time savings compounds across every meeting and makes post-call automation the highest-ROI AI use case in 2026 for B2B sales teams. Autonomous agents that write MEDDPICC-structured data directly to CRM fields, without rep action, are the only architecture that delivers this compounding return.<\/p>\n<p>For enterprise RevOps leaders, Heads of Sales, and CROs at 100-to-2,000-seat organizations already on Salesforce or HubSpot, Coffee&#8217;s Companion App is the evidence-based recommendation. It delivers zero-rep MEDDPICC capture, native CRM write-back, 8\u201312 hours of rep time reclaimed per week, and SOC 2 Type 2 and GDPR compliance inside the existing CRM instance, without a migration project.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>Review Coffee pricing and launch autonomous MEDDPICC qualification on your Salesforce or HubSpot instance today.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Coffee auto-fills all 8 MEDDPICC fields and cuts 8\u201312 hrs of CRM data entry per rep weekly. See the top AI tools for enterprise B2B sales teams.<\/p>\n","protected":false},"author":11,"featured_media":3578,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3631","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/3631","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/comments?post=3631"}],"version-history":[{"count":2,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/3631\/revisions"}],"predecessor-version":[{"id":8258,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/3631\/revisions\/8258"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/3578"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=3631"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=3631"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=3631"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}