{"id":433,"date":"2025-11-17T05:00:31","date_gmt":"2025-11-17T05:00:31","guid":{"rendered":"https:\/\/blog.coffee.ai\/integration-with-salesforce-add-on-to-automate-meeting-notes-in-salesforce\/"},"modified":"2026-07-21T05:07:08","modified_gmt":"2026-07-21T05:07:08","slug":"integration-with-salesforce-add-on-to-automate-meeting-notes-in-salesforce","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/integration-with-salesforce-add-on-to-automate-meeting-notes-in-salesforce","title":{"rendered":"Automate Meeting Notes and Call Summaries in Salesforce"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: July 20, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Manual CRM data entry consumes 25% of a sales rep&#8217;s workweek and hurts forecast accuracy when 76% of organizations report incomplete CRM records.<\/li>\n<li>Einstein Conversation Insights creates basic summaries but cannot fill custom qualification fields like MEDDIC or BANT without an added automation layer.<\/li>\n<li>Third-party tools differ significantly in Salesforce write-back depth; Coffee stands out by mapping structured data directly to Opportunity custom fields and supporting in-person meetings via mobile.<\/li>\n<li>The Coffee Companion App follows a five-stage architecture of OAuth, transcription, structured extraction, validation, and write-back to keep Salesforce updates clean and conflict free.<\/li>\n<li>Teams can remove manual Salesforce data entry and improve forecast accuracy by <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">getting started with Coffee<\/a> today.<\/li>\n<\/ul>\n<h2>Salesforce Readiness Checklist Before Automation<\/h2>\n<p>Confirm a few technical foundations before you connect any automation layer. These requirements build on each other: the right Salesforce edition provides the API base, user permissions control what the integration can touch, and calendar connectivity lets the system match meetings to CRM records.<\/p>\n<ul>\n<li><strong>Salesforce Edition:<\/strong> Enterprise or Unlimited edition is required for full API access and connected app installation. Professional edition needs an API add-on.<\/li>\n<li><strong>User Permissions:<\/strong> Once API access is available, assign the integration user \u201cModify All Data\u201d or a custom profile with read\/write access to Opportunity, Task, Event, Contact, Account, and any custom objects that will receive AI-generated data.<\/li>\n<li><strong>Calendar and Email Access:<\/strong> With permissions configured, connect Google Workspace or Microsoft 365 and enable Einstein Activity Capture or a third-party sync so meetings can associate with CRM records.<\/li>\n<li><strong>Meeting Platform:<\/strong> Turn on recording and transcription at the org level for Zoom, Microsoft Teams, or Google Meet.<\/li>\n<li><strong>Existing Meeting Tools Audit:<\/strong> Document any active recorders such as Gong, Fathom, or Fireflies to avoid duplicate activity logging or conflicting write-back rules.<\/li>\n<li><strong>Field Schema Review:<\/strong> Identify the 10\u201315 Opportunity and Task fields your forecasting dashboards rely on most before you define any mapping rules.<\/li>\n<li><strong>Sandbox Environment:<\/strong> Confirm a sandbox org is available so you can test field mappings and Flow triggers before production rollout.<\/li>\n<\/ul>\n<h2>Step 1: Map Your Current Meeting Data Capture State<\/h2>\n<p>A focused data capture audit gives you a baseline before you introduce automation. Work through these actions in sequence.<\/p>\n<ol>\n<li>Export a sample of 50 closed Opportunities and review the Task and Event records attached to each. Note which fields such as Next Step, Close Date, MEDDIC qualifiers, and competitor mentions are consistently empty.<\/li>\n<li>Survey reps on where post-call notes live today, such as email drafts, personal Notion pages, Slack messages, or spreadsheets. Treat these locations as \u201cshadow CRM\u201d sources that automation must replace.<\/li>\n<li>Run a Salesforce report on field completion rate for forecast-critical Opportunity fields. Top-decile teams often reach 90%+ completion on these fields through automated activity logging, so benchmark your current rate against that target.<\/li>\n<li>Identify which meeting types, including discovery, demo, QBR, and in-person sessions, lack any structured capture mechanism. In-person meetings need a mobile recording path or manual upload capability that many native tools do not support.<\/li>\n<li>Document the average time reps spend on post-call documentation. Sales calls often require substantial manual updates, and quantifying this time creates the ROI baseline for automation.<\/li>\n<\/ol>\n<h2>Step 2: Evaluate Native Einstein Conversation Insights and Its Object-Mapping Limits<\/h2>\n<p><a href=\"https:\/\/mixmax.com\/blog\/ai-meeting-assistant-crm\" target=\"_blank\" rel=\"noindex nofollow\">Salesforce Einstein Conversation Insights analyzes call recordings to create summaries, flag competitor mentions or product discussions, and lets managers build coaching playlists<\/a>. It writes call summaries directly to the Activity record on the related Contact or Opportunity, and it does this without any third-party integration.<\/p>\n<p>Its write-back depth is constrained in several ways that matter to RevOps teams.<\/p>\n<ul>\n<li><a href=\"https:\/\/goairspeed.com\/academy\/guides\/structured-crm-data-from-sales-calls-salesforce\" target=\"_blank\" rel=\"noindex nofollow\">Einstein Activity Capture auto-logs email and calendar events but does not extract call content into custom Opportunity fields or restricted picklists<\/a>, so you still need a dedicated write-back layer for structured data.<\/li>\n<li>MEDDIC or BANT qualification fields such as Economic_Buyer__c, Decision_Criteria__c, and Pain_Point__c are not populated by Einstein natively. Extracted values must flow through a separate automation layer.<\/li>\n<li>Adoption of Einstein Conversation Insights varies across teams, which signals friction in rollout and configuration.<\/li>\n<li>Einstein Conversation Insights requires Sales Cloud Einstein or Revenue Intelligence licensing, which adds cost that mid-market teams must weigh against third-party alternatives.<\/li>\n<li>In-person meetings and calls conducted outside Salesforce-connected dialers produce no Einstein output, so field sales teams face a structural gap.<\/li>\n<\/ul>\n<h2>Step 3: Compare Third-Party Recorders on Salesforce Write-Back Depth<\/h2>\n<p>Given these limits in native tooling, the next move is to compare third-party recorders on their ability to write structured data back to Salesforce custom fields, which Einstein does not handle. The table below compares five tools on the dimensions that matter most for structured Salesforce write-back. Write-back depth refers to whether the tool updates custom Opportunity fields and qualification frameworks beyond basic Task and Event logging.<\/p>\n<table>\n<thead>\n<tr>\n<th>Tool<\/th>\n<th>Salesforce Object Coverage<\/th>\n<th>Qualification Framework Mapping<\/th>\n<th>In-Person Meeting Support<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Fathom<\/td>\n<td><a href=\"https:\/\/emergent.sh\/integrations\/fathom\" target=\"_blank\" rel=\"noindex nofollow\">Opportunity, Task, Account via REST API and webhooks, mapping Meeting Title, Transcript, Summary, Action Items, Participants, and Recording URL<\/a><\/td>\n<td><a href=\"https:\/\/emergent.sh\/integrations\/fathom\" target=\"_blank\" rel=\"noindex nofollow\">Deal Stage Indicators and Next Steps require explicit field mapping by an admin, with no native MEDDIC or BANT extraction<\/a><\/td>\n<td>No native mobile recording path for in-person meetings<\/td>\n<\/tr>\n<tr>\n<td>Fireflies.ai<\/td>\n<td><a href=\"https:\/\/layer3labs.io\/ai-crm-integration\" target=\"_blank\" rel=\"noindex nofollow\">Pushes summaries, action items, and sentiment to Task, Contact, Opportunity, and Account via webhook mapping<\/a><\/td>\n<td>Summary and action items only, with custom qualification field mapping that requires a Zapier or Make intermediary<\/td>\n<td>Mobile app supports in-person recording, while CRM sync requires a manual trigger<\/td>\n<\/tr>\n<tr>\n<td>tl;dv<\/td>\n<td>Task and Event logging with summary text, with Opportunity field updates limited to the native integration scope<\/td>\n<td>No native MEDDIC, BANT, or SPICED extraction, so structured qualification data needs manual field mapping<\/td>\n<td>No dedicated in-person recording mode<\/td>\n<\/tr>\n<tr>\n<td>Colibri<\/td>\n<td>Activity logging to Contact and Opportunity timelines, with write-back depth limited to summary and next steps<\/td>\n<td>Real-time cue cards support MEDDIC prompting during calls, while post-call structured field write-back remains limited<\/td>\n<td>No in-person meeting support<\/td>\n<\/tr>\n<tr>\n<td>Coffee Companion App<\/td>\n<td><a href=\"https:\/\/www.coffee.ai\/changelog\" target=\"_blank\">Writes structured summaries back to Salesforce Task, Event, Opportunity, and custom objects, with customizable summary templates released November 2025<\/a><\/td>\n<td><a href=\"https:\/\/www.coffee.ai\/changelog\" target=\"_blank\">Native MEDDIC, BANT, and SPICED extraction with custom meeting briefing formats<\/a>, mapping qualification fields directly to custom Opportunity fields<\/td>\n<td>Supports in-person meeting capture via mobile, and structured write-back applies to all meeting types<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Step 4: Understand the Coffee Companion App Architecture<\/h2>\n<p>Before you implement Coffee, understand how the Companion App operates as an agent layer between your meeting platforms and Salesforce. The data flow moves through five clear stages.<\/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<ol>\n<li><strong>OAuth Connection:<\/strong> Coffee authenticates to Salesforce via OAuth 2.0 and receives read\/write access to Opportunity, Task, Event, Contact, Account, and designated custom objects. At the same time, it connects to Google Workspace or Microsoft 365 to ingest calendar invites and match meeting participants to existing CRM records.<\/li>\n<li><strong>Transcript Ingestion:<\/strong> The Coffee AI Meeting Bot joins Zoom, Teams, or Google Meet calls to record and transcribe in real time. For in-person meetings, mobile capture routes audio through the same transcription pipeline.<\/li>\n<li><strong>Structured Field Extraction:<\/strong> <a href=\"https:\/\/www.coffee.ai\/changelog\" target=\"_blank\">The Intelligence layer, launched February 2026, applies business model context, ICP definitions, and competitor data<\/a> to pull MEDDIC qualifiers, BANT signals, next steps, objections, and sentiment from the transcript.<\/li>\n<li><strong>Conflict-Resolution and Validation:<\/strong> Before writing, the agent compares proposed values against current Salesforce field values and last-modified timestamps. High-confidence direct-quote signals write automatically, while subjective qualification scores queue for one-click rep confirmation.<\/li>\n<li><strong>Write-Back to Salesforce Objects:<\/strong> Structured data writes to Task description, Event records, Opportunity custom fields, and any designated custom objects. The call logs as a Task or Event associated with the matched Contact, Account, and Opportunity at the same time.<\/li>\n<\/ol>\n<h2>Step 5: Install and Configure Coffee in Your Salesforce Org<\/h2>\n<p>With the architecture clear, move into installation and field mapping. This sequence is designed to reach production accuracy within one to two weeks.<\/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<ol>\n<li><strong>OAuth Authorization:<\/strong> From the Coffee dashboard, start the Salesforce connected app authorization. Grant the integration user profile read\/write permissions on all target objects before you complete OAuth.<\/li>\n<li><strong>Calendar and Email Sync:<\/strong> Connect Google Workspace or Microsoft 365 so Coffee can scan calendar invites, pre-populate meeting context, and match attendees to existing Contact and Account records using email domain and name matching.<\/li>\n<li><strong>Field Mapping Definition:<\/strong> In the Coffee field mapping console, pull the live Salesforce schema. Map each extraction output to its target field, such as call summary to Task Description, next step to Opportunity NextStep, and MEDDIC fields to their corresponding custom __c fields. <a href=\"https:\/\/askelephant.ai\/blog\/how-to-automate-salesforce-updates-from-sales-calls\" target=\"_blank\" rel=\"noindex nofollow\">Begin with 3\u20135 high-value fields such as Next Step and Summary and expand only after accuracy is validated through test calls.<\/a><\/li>\n<li><strong>Summary Template Configuration:<\/strong> <a href=\"https:\/\/www.coffee.ai\/changelog\" target=\"_blank\">Use Coffee&#8217;s customizable summary templates, released November 2025, to define the exact format and focus<\/a>, such as executive summary, technical breakdown, or qualification-focused views for each meeting type.<\/li>\n<li><strong>In-Person Meeting Handling:<\/strong> Enable the mobile recording path in Coffee settings so in-person sessions upload through the mobile app and route through the same extraction and write-back pipeline as virtual calls.<\/li>\n<li><strong>Test Call Validation:<\/strong> Run at least five test calls across different meeting types. Confirm each call logs correctly to the matched Opportunity, that dependent workflows trigger, and that no duplicate Activity records appear.<\/li>\n<\/ol>\n<h2>Step 6: Configure Summary Logic and Salesforce Automation Triggers<\/h2>\n<p>After installation, configure how the agent structures summaries and how Salesforce automation responds to new data. This step connects conversation content to real pipeline movement.<\/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<ul>\n<li><strong>Prompt Templates:<\/strong> Give each meeting type, such as discovery, demo, or renewal, a dedicated prompt template that instructs the extraction model on which signals to prioritize. Templates reference the Intelligence layer&#8217;s stored context on ICP, product specifics, and competitors to improve extraction accuracy.<\/li>\n<li><strong>Validation Gates:<\/strong> Direct-quote signals like explicit next steps, stated timelines, and competitor names write automatically at high confidence. Subjective scores such as deal health or champion strength queue for rep confirmation before they commit to Salesforce.<\/li>\n<li><strong>Flow and Apex Triggers:<\/strong> Field updates on Opportunity records fire existing Salesforce Flows. For example, a write to a Stage_Signal__c field can trigger a Flow that alerts the manager, creates a follow-up Task with a due date, or updates the Forecast Category. <a href=\"https:\/\/askelephant.ai\/blog\/how-to-automate-salesforce-updates-from-sales-calls\" target=\"_blank\" rel=\"noindex nofollow\">Post-call automation that imports new data from conversation content and Salesforce Flow that automates internal processes work together rather than compete.<\/a><\/li>\n<li><strong>Duplicate Prevention:<\/strong> Coffee applies \u201cupdate if exists, create only if not found\u201d logic using email for Contacts and domain for Accounts, which prevents duplicate records from piling up during high-volume call periods.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>Get started with Coffee and eliminate manual Salesforce data entry for your sales team.<\/strong><\/a><\/p>\n<h2>Step 7: Track Validation Metrics and Fix Common Sync Issues<\/h2>\n<p>After go-live, track a small set of metrics weekly for the first 90 days so you can confirm that automation is working as planned.<\/p>\n<ul>\n<li><strong>Field Completion Rate:<\/strong> Target the 90%+ completion rate that top-decile teams achieve on forecast-critical Opportunity fields. <a href=\"https:\/\/vantagepoint.io\/blog\/sf\/case-studies\/salesforce-adoption-roi-transformation\" target=\"_blank\" rel=\"noindex nofollow\">A structured methodology moved one mid-market org from 30% to 85%+ Salesforce adoption with 350% data completeness improvement within 12 months.<\/a><\/li>\n<li><strong>Time Saved Per Rep:<\/strong> Conversation intelligence and AI note-takers can automate a large share of CRM logging, returning several hours per rep per week.<\/li>\n<li><strong>Pipeline Forecast Accuracy:<\/strong> The same mid-market org raised pipeline forecast accuracy significantly after it improved data completeness through automation.<\/li>\n<li><strong>Call-to-Record Match Rate:<\/strong> Monitor the percentage of calls matched to an existing Opportunity or Contact. A match rate below 90% usually points to attendee email domain gaps in the CRM that need cleanup.<\/li>\n<\/ul>\n<p>Several sync failures appear frequently, and each has a clear fix.<\/p>\n<ul>\n<li><strong>Orphaned Activity Records:<\/strong> Calls log to the wrong Account or to no record at all. To fix this, verify that attendee email domains are populated on Contact records and that the calendar invite lists the correct external participants.<\/li>\n<li><strong>Restricted Picklist Rejection:<\/strong> <a href=\"https:\/\/goairspeed.com\/academy\/guides\/structured-crm-data-from-sales-calls-salesforce\" target=\"_blank\" rel=\"noindex nofollow\">The Salesforce API silently drops field values that do not match existing restricted-picklist API names.<\/a> Fix this by mapping extraction outputs to exact picklist API values in the Coffee field mapping console.<\/li>\n<li><strong>Duplicate Task Creation:<\/strong> This issue appears when both Einstein Activity Capture and Coffee write to the same Activity object. Fix it by disabling Einstein Activity Capture for call logging on accounts where Coffee is active, or configure Coffee to suppress Task creation when an Einstein-generated record already exists.<\/li>\n<li><strong>Delayed Transcript Processing:<\/strong> High call volume at quarter end can create processing queues. <a href=\"https:\/\/automationlabz.com\/automations\/meeting-notes-action-items\" target=\"_blank\" rel=\"noindex nofollow\">Monitor queue depth and alert when more than 30 minutes of audio is waiting to be processed<\/a> so follow-ups do not slip and push deals into the next quarter.<\/li>\n<\/ul>\n<h2>Step 8: Scale Coffee Across Teams and Meeting Types<\/h2>\n<p>Once the core flow is stable, expand Coffee across team sizes, channels, and pipeline reporting so the whole revenue engine benefits.<\/p>\n<p><strong>Small teams (under 25 reps)<\/strong> see the fastest wins when they start with a single meeting type such as discovery calls and a minimal field set of five fields before they expand. <a href=\"https:\/\/askelephant.ai\/blog\/how-to-automate-salesforce-updates-from-sales-calls\" target=\"_blank\" rel=\"noindex nofollow\">Most teams complete the full setup within the timeframe outlined in Step 5<\/a> when they keep the initial scope tight.<\/p>\n<p><strong>Mid-market teams (25\u2013200 reps)<\/strong> need role-based field mapping, where AE summaries differ from SDR handoff notes, and manager-level validation gates on high-stakes fields such as contract value and close date. Mid-market companies show strong AI sales tool adoption, yet effective integration that consistently changes outcomes sits closer to 35\u201340%, which makes a structured rollout approach essential.<\/p>\n<p><strong>In-person vs. virtual:<\/strong> Virtual meetings route through the bot automatically. In-person meetings rely on the mobile recording path and a quick post-meeting upload step. Field sales teams should configure Coffee to apply the same MEDDIC extraction template to both meeting types so qualification data stays consistent across channels.<\/p>\n<p><strong>Pipeline Compare:<\/strong> Because Coffee captures structured history in a built-in data warehouse, the Pipeline Compare feature visualizes week-over-week changes automatically. It highlights progressed deals, stalled opportunities, and new additions without manual CSV exports. Deals where AI call summaries were shared promptly after discovery calls closed faster, and the structured data Coffee writes to Salesforce provides the foundation for that speed.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does it take to set up the Coffee Companion App with Salesforce?<\/h3>\n<p>The typical setup timeline is one to two weeks, as detailed in Step 5. The recommended approach is to begin with three to five high-value fields such as Next Step and Call Summary, validate accuracy across at least five test calls, and then expand to qualification framework fields like MEDDIC or BANT. Teams that try to map every field on day one usually encounter higher error rates and longer validation cycles.<\/p>\n<h3>Is the Coffee Companion App SOC 2 Type 2 and GDPR compliant?<\/h3>\n<p>Yes. Coffee is SOC 2 Type 2 certified and GDPR compliant. Call recordings and transcript data are not used to train public AI models. For teams operating in states with two-party consent requirements, Coffee recommends obtaining legal sign-off on AI processing of call content before you enable recording for all participants, because consent requirements vary by jurisdiction.<\/p>\n<h3>How does Coffee&#8217;s pricing model work for the Companion App?<\/h3>\n<p>Coffee uses seat-based pricing. You pay for the human seats on your team, and the agent&#8217;s labor for meeting recording, transcription, extraction, and Salesforce write-back is included without extra metering on AI usage or the number of calls processed. There are no separate charges for LLM tokens or automation runs.<\/p>\n<h3>What happens to our Salesforce data if we later decide to move to a different CRM?<\/h3>\n<p>Coffee writes structured data directly into standard and custom Salesforce objects, so your Salesforce org fully owns all records. If your team migrates to a different CRM in the future, the data is exportable through standard Salesforce data export tools. Coffee&#8217;s agent layer does not create proprietary data silos and instead writes to your system of record using your field schema.<\/p>\n<h3>Can Coffee handle meeting notes from in-person sales calls, not just virtual meetings?<\/h3>\n<p>Yes. The Coffee mobile app supports in-person meeting capture. Audio recorded on mobile routes through the same transcription and extraction pipeline as virtual calls, applying the same MEDDIC, BANT, or SPICED templates and writing structured output back to the matched Salesforce Opportunity, Task, and Contact records. This closes the gap that native Einstein tools and many third-party recorders leave for field sales teams.<\/p>\n<h2>Ready to Eliminate Manual Data Entry in Salesforce?<\/h2>\n<p>Manual call logging is a structural problem, not a rep discipline problem. Sales reps spend 65% of their time on non-selling tasks, and as noted earlier, the majority of CRM data fails accuracy and completeness standards by the time it reaches a forecast. The Coffee Companion App acts as the agent layer that captures unstructured meeting data, structures it against your qualification framework, and writes clean records directly into Salesforce Opportunity, Task, Event, and custom objects without replacing your CRM or disrupting your existing workflows.<\/p>\n<p>The eight-step implementation sequence above gives RevOps and sales leaders a clear path from audit to production in under two weeks. The result is a Salesforce org where field completion exceeds 90%, forecast accuracy reflects reality, and reps spend their time selling instead of typing.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>Get started with Coffee and automate your Salesforce meeting notes and call summaries.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stop manual CRM data entry. Coffee auto-logs AI meeting notes and call summaries straight into Salesforce. Start saving hours every week.<\/p>\n","protected":false},"author":11,"featured_media":8245,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-433","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\/433","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=433"}],"version-history":[{"count":4,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/433\/revisions"}],"predecessor-version":[{"id":8246,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/433\/revisions\/8246"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/8245"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}