{"id":7330,"date":"2026-06-06T05:02:34","date_gmt":"2026-06-06T05:02:34","guid":{"rendered":"https:\/\/www.coffee.ai\/articles\/salesforce-pipeline-intelligence-ai-agents\/"},"modified":"2026-06-06T05:02:34","modified_gmt":"2026-06-06T05:02:34","slug":"salesforce-pipeline-intelligence-ai-agents","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/salesforce-pipeline-intelligence-ai-agents","title":{"rendered":"Improve Salesforce Pipeline Intelligence with AI Agents"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee<\/em><\/p>\n<h2>Why AI Agents Change Salesforce Pipeline Intelligence<\/h2>\n<ul>\n<li>\n<p>Autonomous pipeline agents continuously monitor signals, research context, and execute actions across Salesforce without per-step human approval, unlike copilots that only recommend actions.<\/p>\n<\/li>\n<li>\n<p>Over 72% of sales organizations report forecast accuracy below 80%, and poor data quality wastes an estimated 27% of revenue on data-related inefficiencies, with the average B2B CRM showing 76% of records less than half complete.<\/p>\n<\/li>\n<li>\n<p>The seven-step framework uses the Signal \u2192 Research \u2192 Action agent taxonomy, starting with data-hygiene prerequisites in Salesforce Data Cloud before deploying AI agents.<\/p>\n<\/li>\n<li>\n<p>Coffee Companion App serves as the autonomous data-in layer that connects via OAuth, captures emails and transcripts, enriches records, and writes structured outputs back to Salesforce.<\/p>\n<\/li>\n<li>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/www.coffee.ai\/pricing\"><strong>Start your free Coffee trial<\/strong><\/a> to deploy your first autonomous pipeline agent and turn Salesforce into a real-time pipeline intelligence system.<\/p>\n<\/li>\n<\/ul>\n<h2>Step 1: Fix Salesforce Data Hygiene in Data Cloud First<\/h2>\n<p><strong>Purpose:<\/strong> AI agents amplify whatever data they find, so a weak data foundation produces noisy automation and unreliable forecasts. Most organizations lack confidence in their Salesforce data, which blocks successful AI adoption. Cleaning Salesforce before agent deployment prevents bad signals from compounding at machine speed.<\/p>\n<p><strong>Required inputs:<\/strong> A data quality audit that defines required fields such as first name, last name, email, company, amount, close date, stage, and assigned owner. A deduplication report that highlights merged and suspect records. A data governance policy that assigns a named data steward who owns ongoing quality.<\/p>\n<p><strong>Salesforce actions:<\/strong> In Salesforce Data Cloud, activate identity resolution to merge duplicate person and account records into a single unified profile. In Sales Cloud, enforce validation rules on Opportunity Amount, Close Date, and Stage at the point of record creation or modification. Use Salesforce Flow to trigger a duplicate-check rule on Contact and Lead creation. As noted earlier, most CRMs suffer from severe incompleteness and roughly 1% duplicate rates, so resolving both before agent deployment is non-negotiable.<\/p>\n<p><strong>Coffee configuration notes:<\/strong> Coffee\u2019s Intelligence layer, <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/www.coffee.ai\/changelog\">released in February 2026<\/a>, allows administrators to define ICP, product specifics, and competitor context so the agent\u2019s enrichment targets only records that match your buyer profile from the first sync.<\/p>\n<p><strong>Visual suggestion:<\/strong> A before and after data-flow diagram showing raw Salesforce records entering Data Cloud identity resolution and emerging as deduplicated golden records.<\/p>\n<p><strong>\u26a0 Common Mistake:<\/strong> One company spent $200,000 on AI tools despite having 47,000 duplicates in its CRM. Run deduplication before connecting any agent.<\/p>\n<h2>Step 2: Connect Coffee via OAuth to Your Clean Salesforce Org<\/h2>\n<p>With your Salesforce data now clean and trustworthy, the next step is to give Coffee secure access to that foundation. This connection lets the agent read unified records and write enriched data back without manual exports or spreadsheets.<\/p>\n<p><strong>Purpose:<\/strong> Establish a secure, bidirectional data channel between Coffee and Salesforce so the agent can sync data continuously and safely.<\/p>\n<p><strong>Required inputs:<\/strong> Salesforce System Administrator credentials, a Connected App with OAuth 2.0 scopes such as api, refresh_token, and offline_access, and a field-mapping specification for Contact, Account, Opportunity, and Activity objects.<\/p>\n<p><strong>Salesforce actions:<\/strong> Create a Connected App in Salesforce Setup. Grant the Coffee integration user a custom permission set with object-level read and write on Contact, Account, Opportunity, Task, and Event. Map Coffee\u2019s enrichment fields such as job title, LinkedIn URL, and funding stage to corresponding custom fields in Salesforce using the field-mapping UI.<\/p>\n<p><strong>Coffee configuration notes:<\/strong> After OAuth authentication, Coffee scans connected Google Workspace or Microsoft 365 accounts to auto-create Contacts and Companies. It then associates every interaction with the correct Salesforce record automatically.<\/p>\n<p><strong>Visual suggestion:<\/strong> A screenshot of Coffee\u2019s OAuth connection screen alongside the Salesforce Connected App configuration panel.<\/p>\n<p><strong>\u26a0 Common Mistake:<\/strong> Granting the integration user a full System Administrator profile instead of a scoped permission set creates an audit and compliance risk. Use least-privilege access, because <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/tray.ai\/blog\/agent-vs-copilot-vs-chatbot\">production-grade AI agents require permission-aware tool access and full audit trails<\/a>.<\/p>\n<h2>Step 3: Turn On the Signal Agent for Email, Calendar, and Calls<\/h2>\n<p>Once Coffee can reach Salesforce, you can start feeding it real buyer activity. The Signal Agent becomes your perception layer and replaces manual activity logging.<\/p>\n<p><strong>Purpose:<\/strong> The Signal Agent continuously ingests unstructured data such as emails, calendar events, and call transcripts and converts them into structured Salesforce activity records without rep involvement.<\/p>\n<p><strong>Required inputs:<\/strong> Google Workspace or Microsoft 365 OAuth connection, Zoom, Teams, or Meet bot permissions, and a clear activity-logging policy that defines which email domains to capture and which calendar events to log.<\/p>\n<p><strong>Salesforce actions:<\/strong> Enable Einstein Activity Capture or use Salesforce Flow to receive inbound webhook payloads from Coffee. Once Coffee writes a Task or Event, map it to the correct Opportunity using the Account and Contact lookup fields so every interaction appears on the right deal. Then configure a Flow that stamps \u201cLast Activity Date\u201d on the Opportunity each time a new activity arrives so downstream stale-deal detection logic has an accurate recency signal.<\/p>\n<p><strong>Coffee configuration notes:<\/strong> Coffee\u2019s AI Meeting Bot joins Zoom, Teams, and Meet calls to record and transcribe. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/www.coffee.ai\/changelog\">Improved summary templates released in November 2025 are customizable to match workflows and writable back directly to Salesforce<\/a>, so every call produces a structured record in the CRM within minutes.<\/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><strong>Visual suggestion:<\/strong> A three-column table mapping Signal Source such as Email, Calendar, or Call Transcript to Coffee Action such as Auto-log Task, Auto-log Event, or Generate Summary, and to the Salesforce Object Updated such as Activity, Event, or Opportunity Note.<\/p>\n<p><strong>\u26a0 Common Mistake:<\/strong> Deals stalled beyond 28 days show 67% lower conversion rates (14.3% vs. 43.2%). Failing to configure the \u201cLast Activity Date\u201d Flow keeps stale deals invisible to risk-detection logic downstream.<\/p>\n<h2>Step 4: Use the Research Agent to Enrich and Match Buyers<\/h2>\n<p>With signals flowing into Salesforce, you can now add context. The Research Agent turns raw activities and records into buyer intelligence that supports prioritization.<\/p>\n<p><strong>Purpose:<\/strong> The Research Agent augments raw signal data with firmographic, technographic, and persona context so every Salesforce record reflects current, accurate buyer intelligence instead of stale manual entries.<\/p>\n<p><strong>Required inputs:<\/strong> A clear ICP definition that covers industry, company size, tech stack, and funding stage. Coffee Intelligence layer configuration from Step 1. Salesforce custom fields that receive enrichment outputs.<\/p>\n<p><strong>Salesforce actions:<\/strong> Create custom fields on the Account object for Funding Stage, Employee Count (Enriched), and LinkedIn URL. Use a Salesforce Flow triggered on Account update to stamp an \u201cEnrichment Date\u201d timestamp. Configure Data Cloud to ingest Coffee\u2019s enrichment payloads via the Salesforce API and reconcile them against the unified Account profile so enriched data stays aligned with golden records.<\/p>\n<p><strong>Coffee configuration notes:<\/strong> Coffee\u2019s Research Agent uses licensed data partners to augment records with job titles, funding data, and LinkedIn profiles, which removes the need for standalone enrichment tools. Using the Intelligence layer configured in Step 1, the Research Agent scores enriched records against your defined ICP and surfaces only buyer-matched accounts for pipeline prioritization.<\/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><strong>Visual suggestion:<\/strong> A data-flow diagram showing raw Contact and Account records entering the Research Agent, enrichment sources feeding in from the right, and ICP-scored records writing back to Salesforce Data Cloud on the left.<\/p>\n<p><strong>\u26a0 Common Mistake:<\/strong> <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/digitalapplied.com\/blog\/crm-data-hygiene-2026-contact-management-guide\">B2B contact data decays at a 30% annual rate<\/a>. Treating enrichment as a one-time import instead of a continuous process makes ICP scores unreliable within months.<\/p>\n<h2>Step 5: Let the Action Agent Automate Follow-Up and Pipeline Compare<\/h2>\n<p>Once signals and research are in place, you can safely automate actions. The Action Agent closes the loop and keeps Salesforce updated without extra rep effort.<\/p>\n<p><strong>Purpose:<\/strong> The Action Agent writes clean, structured outputs back to Salesforce such as activity logs, meeting summaries, next-step tasks, and pipeline change reports without rep intervention.<\/p>\n<p><strong>Required inputs:<\/strong> Completed Signal and Research agent configurations, Salesforce Opportunity stage definitions, and a Pipeline Compare baseline snapshot that captures your starting pipeline state.<\/p>\n<p><strong>Salesforce actions:<\/strong> Use Salesforce Flow to auto-create follow-up Tasks from Coffee\u2019s post-call next-step extraction. Configure a custom Opportunity field named \u201cPipeline Compare Flag\u201d that Coffee updates when it detects week-over-week stage regression. Enable Salesforce Reports to consume the Pipeline Compare Flag for manager dashboards.<\/p>\n<p><strong>Coffee configuration notes:<\/strong> After each call, Coffee\u2019s Action Agent generates summaries, identifies next steps, and drafts follow-up emails in Gmail for rep review. The Pipeline Compare feature visualizes week-over-week changes such as progressed deals, stalled opportunities, and new additions, then writes delta records directly to Salesforce and removes the need for manual CSV exports. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/www.coffee.ai\/changelog\">Coffee\u2019s AI search on deals, released in January 2026, answers natural-language questions such as \u201cWhich deals are stuck in negotiation?\u201d or \u201cWhat\u2019s closing this month?\u201d<\/a> directly within the pipeline view.<\/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>Visual suggestion:<\/strong> A screenshot of Coffee\u2019s Pipeline Compare view alongside the corresponding Salesforce Opportunity list view filtered by Pipeline Compare Flag.<\/p>\n<p><strong>\u26a0 Common Mistake:<\/strong> Deploying the Action Agent without a baseline snapshot makes Pipeline Compare meaningless. Capture a full Opportunity export before activating the agent so week-one deltas are measurable.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/www.coffee.ai\/pricing\"><strong>Explore Coffee pricing and setup options<\/strong><\/a> to activate Signal, Research, and Action agents on your existing Salesforce instance.<\/p>\n<h2>Step 6: Add Human Approval Gates and Audit Logs<\/h2>\n<p>After automation goes live, governance becomes the next priority. You now need clear boundaries so agents handle routine work while humans oversee high-risk changes.<\/p>\n<p><strong>Purpose:<\/strong> Approval gates ensure high-stakes actions such as Opportunity stage changes or Contact merges receive human sign-off, and audit logs provide the accountability trail required for RevOps and compliance teams.<\/p>\n<p><strong>Required inputs:<\/strong> A risk matrix that defines which agent actions are auto-approved versus gated. Salesforce Field History Tracking enabled on key Opportunity fields. A designated approver queue that reviews and clears sensitive changes.<\/p>\n<p><strong>Salesforce actions:<\/strong> Enable Field History Tracking on Opportunity Stage, Amount, and Close Date. Create an Approval Process in Salesforce for any Coffee-initiated stage advancement beyond \u201cProposal.\u201d Use Salesforce Shield Event Monitoring to log all API writes from the Coffee integration user. Configure a Flow that notifies the assigned rep via Slack or email when Coffee proposes a record merge.<\/p>\n<p><strong>Coffee configuration notes:<\/strong> Coffee\u2019s agent operates within configurable guardrails. Meeting summaries and follow-up drafts appear for rep review before sending. Pipeline Compare flags write as informational records, not direct stage overwrites, which preserves rep authority over forecast-impacting fields.<\/p>\n<p><strong>Visual suggestion:<\/strong> A three-column table mapping Agent Action such as Stage Advancement, Contact Merge, or Follow-up Email Send to Approval Mode such as Auto, Gated, or Rep Review, and to Salesforce Audit Mechanism such as Field History, Approval Process, or Event Monitor.<\/p>\n<p><strong>\u26a0 Common Mistake:<\/strong> <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/tray.ai\/blog\/agent-vs-copilot-vs-chatbot\">Agents require permission-aware tool access, change logs, and human-in-the-loop for high-risk steps<\/a>. Skipping approval gates on stage-change actions exposes forecast integrity to agent errors and removes the audit trail needed for SOC 2 compliance.<\/p>\n<h2>Step 7: Track Before and After Metrics to Prove ROI<\/h2>\n<p>With governance in place, you can measure impact confidently. Clear metrics show whether agents improve pipeline intelligence and where to adjust configuration.<\/p>\n<p><strong>Purpose:<\/strong> Quantifying agent impact validates the investment, surfaces configuration gaps, and builds internal support for scaling the deployment.<\/p>\n<p><strong>Required inputs:<\/strong> Pre-deployment baseline metrics such as forecast accuracy percentage, hours per week on manual data entry, pipeline coverage ratio, and average deal cycle length. A 30, 60, and 90-day measurement cadence that compares results over time.<\/p>\n<p><strong>Salesforce actions:<\/strong> Build a Salesforce Dashboard with four report components. Include Forecast Accuracy that compares Closed Won versus Forecast by period. Add Activity Coverage that tracks the percentage of Opportunities with activity in the last 14 days. Include Data Completeness that measures the percentage of Contacts with all required fields populated. Add Pipeline Velocity that tracks average days per stage. Schedule the dashboard to refresh daily and distribute it to RevOps and sales leadership.<\/p>\n<p><strong>Coffee configuration notes:<\/strong> Coffee\u2019s Pipeline Compare history provides the week-over-week delta data that feeds the Pipeline Velocity report. The agent\u2019s activity logging ensures Activity Coverage reflects actual engagement, not rep-reported estimates.<\/p>\n<p><strong>Visual suggestion:<\/strong> A four-column before and after table with Metric, Pre-Deployment Baseline, 90-Day Target, and Measurement Source. Targets draw from industry studies on automated forecasting tools, CRM data-quality fixes, and the relationship between accurate sales forecasts and revenue growth.<\/p>\n<p><strong>\u26a0 Common Mistake:<\/strong> Measuring only forecast accuracy hides the productivity story. Track rep hours saved per week separately, because Coffee saves reps 8\u201312 hours per week on data entry tasks, so leadership can quantify the full ROI case, not just the forecasting improvement.<\/p>\n<h2>Scaling Coffee Agents and Using Agentforce Together<\/h2>\n<p>For mid-market teams of 20\u2013100 reps, a two-person RevOps team can complete the seven steps in four to eight weeks. Start with Steps 1 through 3 to establish clean data and signal capture, then layer Research and Action agents in a second sprint once the perception layer proves stable.<\/p>\n<p>Enterprise-lite teams running Salesforce with Agentforce can deploy Coffee Companion alongside Agentforce without conflict. Coffee handles the unstructured data layer such as emails, transcripts, and calendar context, while Agentforce manages structured workflow automation and service orchestration. Salesforce launched Agentforce 3 in 2025 with a Command Center for governance and observability, which complements Coffee\u2019s audit log outputs described in Step 6. The two systems share the same Salesforce data layer, so Coffee\u2019s enriched records and activity logs are immediately available to Agentforce workflows without additional ETL.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What are the Big 4 AI agent types used in sales pipeline management?<\/h3>\n<p>The four agent types most commonly deployed in sales pipeline contexts are Signal Agents, which monitor and capture engagement data from emails, calendars, and call transcripts. Research Agents enrich and contextualize captured signals against firmographic and persona data. Action Agents execute downstream tasks such as activity logging, summary generation, and pipeline updates. Orchestration Agents coordinate the other three, manage execution order, and enforce approval gates. Coffee Companion App implements all four functions within a single agent framework layered on top of Salesforce, which removes the need for separate point solutions for each role.<\/p>\n<h3>What are the 4 pillars of pipeline intelligence?<\/h3>\n<p>The four pillars are Data Quality, Signal Coverage, Risk Detection, and Forecast Accuracy. Data Quality means clean, complete, deduplicated CRM records as the foundation. Signal Coverage means comprehensive capture of all buyer interactions across email, calendar, calls, and web. Risk Detection means automated identification of stalled, regressing, or at-risk opportunities based on activity gaps and stage velocity. Forecast Accuracy means reliable, data-driven revenue projections derived from the first three pillars. Legacy Salesforce implementations typically fail at pillar one and two because they rely on manual data entry. Coffee Companion App addresses pillars one and two autonomously, which in turn makes pillars three and four reliable outputs rather than manual exercises.<\/p>\n<h3>How does Coffee Companion App differ from Salesforce Agentforce?<\/h3>\n<p>Agentforce is Salesforce\u2019s native agent framework designed primarily for structured workflow automation, service orchestration, and actions within the Salesforce platform itself. Coffee Companion App specializes in the unstructured data layer by ingesting emails, calendar events, and call transcripts from Google Workspace and Microsoft 365, enriching records with third-party firmographic data, and writing clean structured outputs back to Salesforce. The two tools are complementary because Coffee handles the \u201cdata in\u201d problem that Agentforce assumes has already been solved. Teams running both benefit from Coffee\u2019s enriched, activity-complete records feeding directly into Agentforce workflows without additional configuration.<\/p>\n<h3>How long does it take to see measurable pipeline intelligence improvements after deploying Coffee on Salesforce?<\/h3>\n<p>Most RevOps teams see measurable improvements within 30 days of completing Steps 1 through 5. Basic CRM data-quality fixes such as enforcing required fields, deduplication, and validation rules can improve forecast accuracy within the first month. Signal Agent deployment adds activity coverage visibility within the first week of connection, because Coffee immediately begins scanning connected email and calendar accounts. Full Signal \u2192 Research \u2192 Action agent deployment, including Pipeline Compare baselines and ROI dashboards, typically requires four to eight weeks for a mid-market team. The 90-day mark is the right point for a comprehensive before and after ROI review.<\/p>\n<h3>Is Coffee Companion App secure enough for mid-market Salesforce environments?<\/h3>\n<p>Coffee is SOC 2 Type 2 and GDPR compliant. Data processed by the Coffee agent is not used to train public models. The OAuth integration with Salesforce uses a scoped permission set rather than administrator-level access, and all agent writes are logged via Salesforce Field History Tracking and Event Monitoring as configured in Step 6. For teams with additional compliance requirements, Coffee\u2019s approval gate configuration allows all agent-initiated record changes to route through a human review queue before committing to Salesforce, which provides a full audit trail for internal and external audits.<\/p>\n<h2>Workflow Checkpoints Recap<\/h2>\n<p>The seven checkpoints for improving Salesforce pipeline intelligence with AI agents are: (1) Establish data-hygiene prerequisites in Salesforce Data Cloud. (2) Connect Coffee Companion via OAuth and map objects. (3) Deploy the Signal Agent to capture emails, calendars, and call transcripts. (4) Configure the Research Agent for enrichment and buyer-persona matching. (5) Build the Action Agent for automated activity logging, meeting summaries, and Pipeline Compare. (6) Set up human-in-the-loop approval gates and audit logs. (7) Measure ROI with before and after metrics on a 30, 60, and 90-day cadence.<\/p>\n<p>Each step builds on the previous one. Clean data enables accurate signal capture. Accurate signals enable reliable enrichment. Reliable enrichment enables trustworthy action automation. Governed automation enables defensible forecasting. Skipping any step weakens every step that follows.<\/p>\n<p>Coffee Companion App is the autonomous data-in layer that executes steps two through seven on top of your existing Salesforce instance without replacing it, without spreadsheets, and without requiring reps to act as data entry clerks. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/www.coffee.ai\/pricing\"><strong>See how Coffee upgrades your Salesforce pipeline intelligence<\/strong><\/a> and turn your Salesforce instance into a real-time pipeline intelligence system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stop flying blind in Salesforce. Coffee&#8217;s autonomous AI agents enrich records, qualify leads, and sharpen forecast accuracy \u2014 automatically. See how.<\/p>\n","protected":false},"author":11,"featured_media":7329,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-7330","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\/7330","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=7330"}],"version-history":[{"count":0,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/7330\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/7329"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=7330"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=7330"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=7330"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}