{"id":1181,"date":"2025-12-17T05:00:50","date_gmt":"2025-12-17T05:00:50","guid":{"rendered":"https:\/\/blog.coffee.ai\/ai-agent-for-sales-data-entry-ai-agent-for-sales\/"},"modified":"2026-08-09T05:03:05","modified_gmt":"2026-08-09T05:03:05","slug":"ai-agent-for-sales-data-entry-ai-agent-for-sales","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/ai-agent-for-sales-data-entry-ai-agent-for-sales","title":{"rendered":"How to Automate Sales CRM Data Entry with an AI Agent"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: August 7, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Automating CRM Data Entry<\/h2>\n<ul>\n<li>AI agents automate CRM data entry by capturing interactions from calls, emails, and calendars, then writing structured fields directly to records.<\/li>\n<li>Manual CRM entry consumes 70% of sales reps&#8217; time and hurts data quality, while AI automation recovers hours of selling time each week.<\/li>\n<li>This 8-step framework covers mapping data sources, choosing deployment models, setting confidence thresholds, configuring write-back logic, and monitoring.<\/li>\n<li>Teams can deploy either a Standalone CRM for 1\u201320 seat teams or a Companion App that layers the agent on top of existing Salesforce or HubSpot instances.<\/li>\n<li>Start automating your CRM data entry today with <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Coffee<\/a> and eliminate manual entry without custom development.<\/li>\n<\/ul>\n<h2>Why Automating CRM Data Entry Matters for Revenue Teams<\/h2>\n<p><a href=\"https:\/\/laureo.io\/blog\/crm-automation-time-savings\" target=\"_blank\" rel=\"noindex nofollow\">Salesforce&#8217;s State of Sales report found that reps spend only about 30% of their week actually selling<\/a>, with the remaining 70% absorbed by deal management, data entry, and administrative work. A sales rep spends 3\u20135 hours on post-meeting CRM administration, plus additional time on email documentation. The downstream cost is severe: <a href=\"https:\/\/askelephant.ai\/blog\/improve-crm-data-quality-conversation-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">37% of CRM users lost revenue directly because of poor data quality, with companies losing sales opportunities from unreliable records<\/a>. <a href=\"https:\/\/demandgenreport.com\/industry-news\/news-brief\/gartner-as-ai-saves-time-sales-organizations-fail-to-reinvest-time-in-high-value-activities\/52944\" target=\"_blank\" rel=\"noindex nofollow\">A Gartner study of 210 CSOs and senior sales leaders found that AI tools save sellers significant time each week<\/a>, and <a href=\"https:\/\/demandgenreport.com\/industry-news\/news-brief\/gartner-as-ai-saves-time-sales-organizations-fail-to-reinvest-time-in-high-value-activities\/52944\" target=\"_blank\" rel=\"noindex nofollow\">sales organizations that reinvest that time into high-impact activities are 3.1 times more likely to exceed lead-to-opportunity conversion goals<\/a>. Manual CRM entry is a workflow problem, not a software problem, and an AI agent provides a direct fix.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Recover those lost selling hours with Coffee&#8217;s AI-powered CRM automation.<\/a><\/p>\n<h2>Readiness Checklist for AI CRM Automation<\/h2>\n<p>Teams need a basic technical and process foundation before an AI agent can automate CRM data entry reliably. These prerequisites prevent rework later and allow you to move straight into implementation.<\/p>\n<ul>\n<li>Google Workspace or Microsoft 365 access for email and calendar ingestion<\/li>\n<li>An existing Salesforce or HubSpot instance, or willingness to adopt a standalone system of record<\/li>\n<li>Defined buyer personas and qualification methodology (BANT, MEDDIC, or SPICED)<\/li>\n<li>Named field owners who will perform weekly spot-checks on agent outputs<\/li>\n<li>A sandbox CRM environment for testing write-back logic before production deployment<\/li>\n<\/ul>\n<h2>8-Step Implementation Framework<\/h2>\n<h3>Step 1: Map Data Sources and CRM Fields<\/h3>\n<p>Start by listing every interaction channel the agent must ingest, including calendar invites, email threads, call recordings, and video transcripts. Once you know the sources, define which CRM fields each source populates, the data type (text, picklist, date, currency), and the field owner responsible for accuracy. <a href=\"https:\/\/agxntsix.ai\/reports\/autonomous-conversational-agents-crm-write-operations\" target=\"_blank\" rel=\"noindex nofollow\">Each write must map to a CRM field with a defined data type, a constrained value set where applicable, and a clear permission scope that blocks changes to unrelated records such as billing or contracts.<\/a><\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678186019-5cc1a76ac78e.gif\" alt=\"Build people lists automatically with Coffee AI CRM Agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Build people lists automatically with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<p>A minimal field map for a discovery call looks like this:<\/p>\n<pre><code>{ \"source\": \"zoom_transcript\", \"crm_object\": \"Deal\", \"fields\": { \"deal_stage\": \"Qualification\", \"budget_confirmed\": true, \"next_step\": \"Send proposal by 2026-08-14\", \"economic_buyer\": \"Jane Smith, CFO\", \"confidence_score\": 0.91 } }<\/code><\/pre>\n<p><strong>Pitfall:<\/strong> Skipping field-level permission scoping allows the agent to overwrite fields it should never touch, such as contract values or billing contacts.<\/p>\n<h3>Step 2: Choose a Deployment Model That Fits Your Team<\/h3>\n<p>Select a deployment model that matches team size and existing systems, because this choice controls setup effort and long-term maintenance.<\/p>\n<table>\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>Standalone CRM (Coffee)<\/th>\n<th>Companion App (Coffee + Salesforce\/HubSpot)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Best for<\/td>\n<td>1\u201320 seat teams replacing spreadsheets<\/td>\n<td>5\u201375 seat teams committed to existing CRM<\/td>\n<\/tr>\n<tr>\n<td>Setup effort<\/td>\n<td>Connect Google Workspace or M365, and the agent populates records immediately<\/td>\n<td>OAuth authentication to Salesforce\/HubSpot, then the agent syncs, enriches, and writes back<\/td>\n<\/tr>\n<tr>\n<td>Maintenance model<\/td>\n<td>Agent manages system of record, with no legacy schema to maintain<\/td>\n<td><a href=\"https:\/\/woyce.ai\/blog\/ai-agent-maintenance\" target=\"_blank\" rel=\"noindex nofollow\">Integration maintenance requires active monthly reviews because third-party APIs can change without notice and create silent failures<\/a><\/td>\n<\/tr>\n<tr>\n<td>Scalability<\/td>\n<td>Flat-rate seat pricing, with unlimited agent labor<\/td>\n<td><a href=\"https:\/\/conduyt.com\/resources\/bring-your-own-ai-agent-crm\" target=\"_blank\" rel=\"noindex nofollow\">Per-seat CRM pricing models create scalability friction for layered AI agents, while flat-rate pricing removes this constraint<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Step 3: Connect Identity, Calendar, and Email<\/h3>\n<p>Authenticate the agent to Google Workspace or Microsoft 365 using OAuth2 with scoped read permissions on calendar and mail. After authentication, the agent scans these sources to auto-create contacts, companies, and activity records. <a href=\"https:\/\/conduyt.com\/resources\/bring-your-own-ai-agent-crm\" target=\"_blank\" rel=\"noindex nofollow\">Teams should use scoped tokens, service account identities, and separate read and write permissions to maintain security and auditability.<\/a> Coffee completes this step on first login, so teams avoid custom connector work.<\/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<h3>Step 4: Configure the AI Meeting Bot and Transcription Quality<\/h3>\n<p><a href=\"https:\/\/plavno.io\/blog\/ai-sales-assistant-how-revenue-teams-use-ai-to-move-faster\" target=\"_blank\" rel=\"noindex nofollow\">An audio processing worker pulls audio file URLs from a queue and uses ASR models to generate raw transcripts, with built-in retries and circuit breakers.<\/a> <a href=\"https:\/\/agxntsix.ai\/reports\/autonomous-conversational-agents-crm-write-operations\" target=\"_blank\" rel=\"noindex nofollow\">Speech-to-text accuracy, especially named entity recognition for company names, product codes, and monetary amounts, acts as the upstream constraint that determines reliability of all downstream CRM writes.<\/a> Coffee&#8217;s meeting bot joins Zoom, Teams, and Google Meet to record and transcribe, then structures outputs automatically according to BANT, MEDDIC, or SPICED.<\/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><strong>Pitfall:<\/strong> Deploying a bot without speaker attribution produces transcripts that cannot reliably identify which party stated a budget figure or objection, which degrades extraction accuracy.<\/p>\n<h3>Step 5: Set Confidence Thresholds and Human-Review Rules<\/h3>\n<p><a href=\"https:\/\/superkind.ai\/ai-lexicon\/human-in-the-loop\" target=\"_blank\" rel=\"noindex nofollow\">Well-calibrated human-in-the-loop systems escalate fewer than 15% of tasks to human reviewers, leaving the rest fully automated.<\/a> Use the following approval matrix as a starting point so that high-risk changes always receive human oversight.<\/p>\n<table>\n<thead>\n<tr>\n<th>Action Type<\/th>\n<th>Risk Level<\/th>\n<th>Review Pattern<\/th>\n<th>Threshold<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Contact\/company enrichment<\/td>\n<td>Low<\/td>\n<td><a href=\"https:\/\/aiexpert.ee\/en\/articles\/human-in-the-loop-design-patterns\" target=\"_blank\" rel=\"noindex nofollow\">Sampling: 5% of outputs or 20 random cases per week<\/a><\/td>\n<td>Confidence \u2265 0.80<\/td>\n<\/tr>\n<tr>\n<td>Call notes and next steps<\/td>\n<td>Low\u2013Medium<\/td>\n<td>Exception review for ambiguous cases<\/td>\n<td>Confidence \u2265 0.85<\/td>\n<\/tr>\n<tr>\n<td>Deal stage changes<\/td>\n<td>High<\/td>\n<td><a href=\"https:\/\/chronic.digital\/blog\/human-in-loop-ai-sdr-approvals\" target=\"_blank\" rel=\"noindex nofollow\">Approve any action that changes CRM truth such as stage changes, disqualification, or suppression<\/a><\/td>\n<td>Human approval required<\/td>\n<\/tr>\n<tr>\n<td>Disqualification or suppression<\/td>\n<td>High<\/td>\n<td>Human ownership of final decision<\/td>\n<td>No autonomous execution<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><a href=\"https:\/\/aiexpert.ee\/en\/articles\/human-in-the-loop-design-patterns\" target=\"_blank\" rel=\"noindex nofollow\">Define stop rules that halt workflows when more than 3% of sampled outputs fail a checklist, any cross-customer data exposure is detected, or more than five high-risk exceptions remain unreviewed for 24 hours.<\/a><\/p>\n<p><a href=\"https:\/\/swiftheadway.ai\/case-studies\/ai-crm-automation-saas-sales-team\" target=\"_blank\" rel=\"noindex nofollow\">AI transcript analysis can achieve high deal stage classification accuracy at a calibrated confidence threshold, with updates below threshold flagged for rep review in Slack.<\/a><\/p>\n<h3>Step 6: Define Update Logic and CRM Write-Back Rules<\/h3>\n<p><a href=\"https:\/\/justcall.io\/blog\/ai-voice-agent-crm-integration.html\" target=\"_blank\" rel=\"noindex nofollow\">The integration layer authenticates via OAuth or a REST API connection, with bidirectional sync required to pull contact records before a call and push transcript, call outcome, sentiment, and next-step actions afterward.<\/a> Define idempotency keys so that a retried webhook does not create duplicate activity records. <a href=\"https:\/\/chronic.digital\/blog\/human-in-loop-ai-sdr-approvals\" target=\"_blank\" rel=\"noindex nofollow\">Audit logs must capture at minimum the AI agent ID and human approver, timestamps, before-and-after field values, evidence and sources used, and the outcome with a reason code.<\/a><\/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><strong>Pitfall:<\/strong> Writing to a generic notes field instead of structured deal properties destroys the queryability of extracted data and prevents pipeline intelligence from functioning correctly.<\/p>\n<h3>Step 7: Add Pipeline-Intelligence Outputs for Leaders<\/h3>\n<p>The agent captures every interaction in a built-in data warehouse, so week-over-week pipeline comparison becomes automatic. Coffee&#8217;s Pipeline Compare feature visualizes progressed deals, stalled opportunities, and new additions without CSV exports. <a href=\"https:\/\/swiftheadway.ai\/case-studies\/ai-crm-automation-saas-sales-team\" target=\"_blank\" rel=\"noindex nofollow\">VP of Sales forecast accuracy improved after AI automation raised pipeline data accuracy.<\/a> Configure week-over-week alerts to surface deals that have not advanced in 14 days.<\/p>\n<h3>Step 8: Monitor, Audit, and Iterate on the Agent<\/h3>\n<p><a href=\"https:\/\/agxntsix.ai\/reports\/autonomous-conversational-agents-crm-write-operations\" target=\"_blank\" rel=\"noindex nofollow\">Governance of autonomous CRM writes works best when each automated field or field group has a named owner who reviews outputs against call transcripts and feeds discrepancies into model refinement cycles.<\/a> <a href=\"https:\/\/superkind.ai\/ai-lexicon\/human-in-the-loop\" target=\"_blank\" rel=\"noindex nofollow\">Structured human feedback loops can reduce escalation rates over time as the model improves.<\/a> Use weekly spot-check findings to inform quarterly prompt schema reviews, and update schemas whenever qualification methodology or buyer personas change.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Coffee handles this entire 8-step framework out-of-the-box, so you can start automating quickly.<\/a><\/p>\n<h2>Validation: Data Quality, Time Saved, and Adoption Signals<\/h2>\n<p>Three metric categories confirm that the deployment works as intended and continues to deliver value.<\/p>\n<ul>\n<li><strong>Data quality:<\/strong> Spot-check 20 randomly selected call records per week against transcripts. Target field completion above 90% and <a href=\"https:\/\/www.lido.app\/blog\/ai-vs-manual-data-entry\" target=\"_blank\" rel=\"noindex nofollow\">AI data entry accuracy of 99%+, compared to manual entry\u2019s 96\u201398% accuracy<\/a>.<\/li>\n<li><strong>Time saved:<\/strong> <a href=\"https:\/\/swiftheadway.ai\/case-studies\/ai-crm-automation-saas-sales-team\" target=\"_blank\" rel=\"noindex nofollow\">A 12-rep SaaS team recovered 2.1 hours per rep per day after deploying an AI automation layer, reducing daily CRM admin time from 2.5 hours to 24 minutes per rep.<\/a><\/li>\n<li><strong>Adoption signals:<\/strong> Track the percentage of activities logged automatically versus manually. <a href=\"https:\/\/swiftheadway.ai\/case-studies\/ai-crm-automation-saas-sales-team\" target=\"_blank\" rel=\"noindex nofollow\">Post-deployment, the majority of call activities were logged automatically shortly after call end.<\/a> A ratio below 80% indicates reps are bypassing the agent.<\/li>\n<\/ul>\n<h2>How the Framework Scales from Startup to Mid-Market<\/h2>\n<p>For a 5-person startup, the Standalone CRM model removes setup complexity entirely. Connect Google Workspace, define one buyer persona, and the agent begins populating records immediately. Teams can start with permissive confidence thresholds such as 0.75, then tighten them as they validate outputs over the first 30 days.<\/p>\n<p>For a 50\u201375 seat mid-market team on Salesforce or HubSpot, the Companion App model preserves existing quota structures, forecasting hierarchies, and required fields while layering the agent on top. <a href=\"https:\/\/chronic.digital\/blog\/human-in-loop-ai-sdr-approvals\" target=\"_blank\" rel=\"noindex nofollow\">A two-stage model recommends requiring human approval for all CRM truth changes in the first 30 days, then allowing autonomous updates only for low-risk fields after audit evidence shows consistent accuracy.<\/a> MEDDIC and SPICED methodologies map directly to Coffee&#8217;s structured note templates, which keeps qualification data consistent across every rep and territory.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How accurate is AI CRM logging compared to manual entry?<\/h3>\n<p>AI-powered CRM logging consistently outperforms manual entry on both accuracy and completeness. Human data entry carries an inherent error rate driven by fatigue, inconsistent formatting, and missed fields, and these problems compound as call volume increases. AI agents cross-reference multiple sources simultaneously, including transcripts, email threads, and enrichment databases, to produce structured records. In production deployments, accuracy rates for structured field extraction from call transcripts typically exceed 90% at calibrated confidence thresholds, with deal stage classification reaching 93% accuracy when validated against historical data. A practical benchmark to target is field completion above 90% and a manual correction rate below 5% on sampled outputs. Coffee&#8217;s agent applies this logic automatically and flags any extraction that falls below the configured confidence threshold for rep review instead of writing uncertain data to the record.<\/p>\n<h3>Is Coffee secure and compliant with data privacy regulations?<\/h3>\n<p>Coffee is SOC 2 certified and GDPR compliant. Call transcripts and email content processed by the agent are not used to train public models. PII redaction occurs before any content reaches large language model processing layers, and all data remains within defined organizational boundaries. Audit logs capture every agent action, including field-level before-and-after values, the source evidence used, and the human approver where applicable, which provides the documentation trail required for compliance reviews. Teams in regulated-adjacent industries should confirm specific data residency requirements with Coffee&#8217;s team before deployment.<\/p>\n<h3>How does Coffee integrate with Salesforce and HubSpot?<\/h3>\n<p>Coffee&#8217;s Companion App connects to Salesforce and HubSpot via OAuth authentication, giving the agent scoped read and write permissions aligned to the field map defined in Step 1 of this framework. The agent pulls existing contact and deal records before each meeting to provide rep briefings, then writes structured outputs such as call notes, deal stage updates, next steps, and qualification fields back to the CRM within minutes of call completion. This bidirectional sync preserves all existing Salesforce configurations including quota structures, required fields, forecasting hierarchies, and routing rules. For teams that need connections to additional tools in their stack, Coffee currently supports integration via Zapier, with deeper native connectors on the product roadmap.<\/p>\n<h3>What does Coffee cost?<\/h3>\n<p>Coffee uses straightforward seat-based pricing. Teams pay for human seats, and the agent&#8217;s labor, including data entry, enrichment, meeting management, pipeline intelligence, and outreach, is included without additional metering on AI usage or process volume. There are no separate charges for LLM calls, transcript processing, or enrichment lookups. This model works especially well for teams scaling call volume, because the agent handles more work without the per-action cost increases common in usage-based pricing structures. Full pricing details are available at the Coffee pricing page.<\/p>\n<h3>How long does deployment take, and when will teams see ROI?<\/h3>\n<p>For the Standalone CRM model, the agent begins populating records as soon as Google Workspace or Microsoft 365 is connected, which typically happens within the same day. For the Companion App model layered on Salesforce or HubSpot, the OAuth connection and field mapping configuration usually take one to three days, with the first automated CRM writes occurring immediately afterward. Most teams see measurable time savings within the first week as activity logging shifts from manual to automatic. Full ROI, measured as recovered selling hours, improved pipeline data accuracy, and more reliable forecasts, typically appears within the first 30 to 90 days.<\/p>\n<h2>Conclusion: Put CRM Data Entry on Autopilot<\/h2>\n<p>Manual CRM data entry is a workflow problem with a clear solution: an AI agent that captures every interaction, extracts structured fields, and writes accurate data to the CRM without human effort. The 8-step framework above, from field mapping and deployment model selection through confidence thresholds, write-back logic, pipeline intelligence, and ongoing audit, provides a complete path from current state to automated operation. Coffee ships this entire workflow out-of-the-box in both Standalone CRM and Companion App configurations, with no custom development required. Teams that deploy the agent recover hours of selling time per rep per week, raise pipeline data accuracy above 90%, and give sales leadership the reliable forecasts they need to make confident decisions.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Start recovering those hours and improving your pipeline accuracy with Coffee.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recover 8\u201312 hrs\/week with Coffee&#8217;s AI agent that auto-logs calls, emails &amp; deals in your CRM. Start automating data entry \u2014 no custom dev needed.<\/p>\n","protected":false},"author":11,"featured_media":1169,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1181","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\/1181","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=1181"}],"version-history":[{"count":3,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/1181\/revisions"}],"predecessor-version":[{"id":8468,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/1181\/revisions\/8468"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/1169"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=1181"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=1181"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=1181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}