{"id":8920,"date":"2026-09-07T05:00:49","date_gmt":"2026-09-07T05:00:49","guid":{"rendered":"https:\/\/www.coffee.ai\/articles\/crm-that-updates-itself"},"modified":"2026-09-07T05:00:49","modified_gmt":"2026-09-07T05:00:49","slug":"crm-that-updates-itself","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/crm-that-updates-itself","title":{"rendered":"CRM That Updates Itself: Autonomous Agents Fix Data"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Legacy CRMs force reps to spend 10\u201311 hours weekly on manual data entry, which costs millions in lost productivity and bad data.<\/li>\n<li>Traditional bolt-on AI tools create new data silos and still cannot handle unstructured data at the architecture level.<\/li>\n<li>An autonomous CRM agent captures emails, calendars, and call transcripts in real time and writes structured data to the right fields without human help.<\/li>\n<li>Teams gain pipeline intelligence, visitor identification, and stack consolidation through a single agent that preserves history in a data warehouse.<\/li>\n<li>Eliminate manual data entry and keep your pipeline accurate\u2014<a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">get started with Coffee<\/a> today.<\/li>\n<\/ul>\n<h2>Sales Reps Lose Days Each Week to CRM Admin<\/h2>\n<p>The average sales representative <a href=\"https:\/\/startupfinanceguide.com\/startup-finance\/sales-team-crm-data-entry-time-waste\" target=\"_blank\" rel=\"noindex nofollow\">loses 10 to 11 hours per week to manual CRM data entry<\/a>, which equals 25% to 30% of a full workweek before a single customer conversation. For a team of ten reps, that becomes roughly <a href=\"https:\/\/askelephant.ai\/blog\/why-reps-spend-25-percent-of-time-on-crm\" target=\"_blank\" rel=\"noindex nofollow\">100 lost selling hours per week, the same as 2.5 full-time sellers doing nothing but data entry<\/a>.<\/p>\n<p>The financial toll is direct. <a href=\"https:\/\/askelephant.ai\/blog\/why-reps-spend-25-percent-of-time-on-crm\" target=\"_blank\" rel=\"noindex nofollow\">A sales rep earning $100,000 per year who spends 25% of that time on CRM admin represents $25,000 in misallocated compensation per rep annually<\/a>. Scaled across a team, this becomes significant lost productivity and budget. At the organizational level, Gartner research from 2020 estimates that poor data quality costs organizations at least $12.9 million per year on average.<\/p>\n<p>Data decay compounds the problem. <a href=\"https:\/\/askelephant.ai\/blog\/why-reps-spend-25-percent-of-time-on-crm\" target=\"_blank\" rel=\"noindex nofollow\">CRM data decays at a rate of 30% per year when reps rush through manual entries<\/a>, so leaders forecast on records that are already stale. Behavior shifts in response. <a href=\"https:\/\/askelephant.ai\/blog\/why-reps-spend-25-percent-of-time-on-crm\" target=\"_blank\" rel=\"noindex nofollow\">Thirty-seven percent of sales staff admit to fabricating CRM data because the burden of manual entry conflicts with quota pressure<\/a>. When the system of record runs on fabricated inputs, pipeline reviews turn into guesswork instead of strategy.<\/p>\n<h2>Why Legacy CRMs and Add-Ons Cannot Keep Up<\/h2>\n<p>Legacy revenue systems like Salesforce, Microsoft Dynamics 365, and HubSpot were built primarily as systems of record to store information about accounts, contacts, opportunities, and activities, not to continuously interpret signals or predict outcomes. Their relational database architecture handles structured fields well. It struggles with unstructured data such as email body text, call transcripts, or meeting notes without heavy custom development.<\/p>\n<p>Add-on tools, such as separate meeting recorders and enrichment databases, sit on top of this limitation. They create new silos instead of fixing the core design. Fifty-one percent of sales leaders with AI say tech silos delay or limit AI initiatives, and many teams now buy software just to connect other software, which shows how fragmented the stack has become.<\/p>\n<p>The table below compares legacy stacks and autonomous agents on factors that directly affect data quality and selling time.<\/p>\n<table>\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>Legacy CRM + Add-On Tools<\/th>\n<th>Autonomous CRM Agent<\/th>\n<th>Source<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Data entry method<\/td>\n<td>Manual rep input after each interaction<\/td>\n<td>Automatic capture from email, calendar, and transcripts<\/td>\n<td>Salesforce<\/td>\n<\/tr>\n<tr>\n<td>Annual data decay rate<\/td>\n<td>30% when entry is manual<\/td>\n<td>Continuous enrichment reduces decay in real time<\/td>\n<td><a href=\"https:\/\/askelephant.ai\/blog\/why-reps-spend-25-percent-of-time-on-crm\" target=\"_blank\" rel=\"noindex nofollow\">Salesforce State of Sales<\/a><\/td>\n<\/tr>\n<tr>\n<td>Admin time saved per rep<\/td>\n<td>Minimal, tools add switching cost<\/td>\n<td>2\u20135 hours per week reclaimed from manual record-keeping<\/td>\n<td><a href=\"https:\/\/agxntsix.ai\/reports\/autonomous-conversational-agents-crm-write-operations\" target=\"_blank\" rel=\"noindex nofollow\">LionOBytes \/ Accordion<\/a><\/td>\n<\/tr>\n<tr>\n<td>Historical context retention<\/td>\n<td>Overwritten on field update, history lost<\/td>\n<td>Data warehouse preserves full interaction history<\/td>\n<td>Aviso<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>The Agent-Led CRM Architecture Explained<\/h2>\n<p>These architectural limitations and data silos create a clear need for a system that processes unstructured data natively instead of bolting intelligence onto a passive database. The autonomous CRM agent meets that need and resolves the data-quality problem at its source. Instead of asking humans to translate interactions into database fields, the agent ingests both structured data and unstructured data into a unified data warehouse where historical context stays preserved.<\/p>\n<p><a href=\"https:\/\/agxntsix.ai\/reports\/autonomous-conversational-agents-crm-write-operations\" target=\"_blank\" rel=\"noindex nofollow\">Agentic CRM integration cuts sales representative admin time by 50% to 70% and trims operational expenses by 30% or more at the workflow level<\/a>. The mechanism relies on four components. An ingestion-capable AI model processes raw interactions. An orchestration engine manages multi-step tool calls. Data connectors tie directly to CRM APIs. State management captures every conversation metric and writes those outputs to the correct CRM fields.<\/p>\n<p>Coffee runs this model in two configurations. As a standalone CRM, the Coffee Agent becomes the system of record for small and growing teams. As a companion app, it operates as an intelligent layer on top of existing Salesforce or HubSpot installations, handling the data-in process so the current system of record stays accurate without human effort.<\/p>\n<h2>How Coffee Updates CRM Automatically After Calls<\/h2>\n<p>The agent workflow around a sales call shows how a self-updating CRM behaves in daily use. Before the meeting, Coffee surfaces a briefing that includes attendee roles, company context, and prior interaction history. During the call, the agent joins via Zoom, Teams, or Google Meet, performs live transcription with speaker identification, and flags key moments such as budget discussions, objections, and decision-maker involvement.<\/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>AI meeting assistants generate structured summaries with next steps within seconds of the call ending, and Coffee then writes those outputs directly to the CRM record. Deal stage updates, next activity dates, follow-up tasks, and BANT or MEDDIC qualification fields update automatically. The rep receives a draft follow-up email in Gmail for review and sending. No manual logging occurs at any stage.<\/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><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Get started with Coffee and remove post-call data entry from your team&#039;s workflow.<\/a><\/p>\n<h2>Three Compounding Benefits for Small-to-Mid Teams<\/h2>\n<p>For a 10- to 50-person sales team, the agent-first model delivers three compounding advantages beyond time savings. These benefits show up in pipeline visibility, demand capture, and tool consolidation.<\/p>\n<p>The first benefit is pipeline intelligence without spreadsheets. Because the Coffee Agent captures history in a built-in data warehouse, the Pipeline Compare feature visualizes week-over-week changes automatically. Leaders see progressed deals, stalled opportunities, and new additions without CSV exports or manual review prep.<\/p>\n<p>The second benefit is visitor identification. Most companies have no visibility into who browses their website. A single Coffee tracking pixel turns anonymous traffic into named, qualified prospects with name, title, email, and LinkedIn profile surfaced in real-time Slack notifications. Unlike standalone tools that surface only a company name or generic people lists, Coffee&#039;s Suggested Leads feature identifies the two or three specific individuals inside a visiting company who match the buyer persona and are ready for LinkedIn outreach or automatic enrollment in a Campaign sequence.<\/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>The third benefit is stack consolidation. Coffee covers the roles of a CRM, enrichment database, prospecting tool, meeting recorder, outreach sequencer, and forecasting layer in one agent.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678641499-bad085f8165f.gif\" alt=\"Building a company list with Coffee AI\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Building a company list with Coffee AI<\/em><\/figcaption><\/figure>\n<table>\n<thead>\n<tr>\n<th>Capability<\/th>\n<th>Legacy CRM Stack<\/th>\n<th>Agent-First System (Coffee)<\/th>\n<th>Source<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CRM data accuracy<\/td>\n<td>Less than half of data fully accurate per most CRM users<\/td>\n<td>Automated data entry reduces error rates from 4.2% to 0.17%<\/td>\n<td>US Tech Automations<\/td>\n<\/tr>\n<tr>\n<td>Revenue lost to poor data<\/td>\n<td><a href=\"https:\/\/portalpilot.io\/blog\/crm-data-cleanup-roi\" target=\"_blank\" rel=\"noindex nofollow\">Thirty-seven percent of organizations report direct revenue loss from poor CRM data<\/a><\/td>\n<td>Continuous automated monitoring catches and fixes issues in real time<\/td>\n<td><a href=\"https:\/\/portalpilot.io\/blog\/crm-data-cleanup-roi\" target=\"_blank\" rel=\"noindex nofollow\">PortalPilot<\/a><\/td>\n<\/tr>\n<tr>\n<td>Quota attainment<\/td>\n<td>Baseline CRM ROI of $8.71 per $1 invested<\/td>\n<td>Sellers partnering with AI tools are 3.7\u00d7 more likely to meet quota<\/td>\n<td><a href=\"https:\/\/dev.to\/coherence_ai\/ai-native-crm-vs-legacy-crm-the-architecture-decision-that-determines-your-sales-teams-future-2bm6\" target=\"_blank\" rel=\"noindex nofollow\">Gartner 2025 \/ Teamgate 2025<\/a><\/td>\n<\/tr>\n<tr>\n<td>Forecast variance<\/td>\n<td>Built on manual rep input and optimism bias<\/td>\n<td>Automatic activity capture can improve forecast accuracy by 20\u201330%<\/td>\n<td><a href=\"https:\/\/revalign.io\/best\/top-10-pipeline-visibility-tools-2026\" target=\"_blank\" rel=\"noindex nofollow\">RevOps 2026 benchmark<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>How AI Will Redefine CRM, Not Replace It<\/h2>\n<p>CRM as a category will not disappear under AI. It will be reshaped by AI. The passive database model is giving way to the autonomous agent model, while the need for a reliable system of record for customer relationships stays constant. By 2028, 60% of B2B seller work will be executed through conversational user interfaces via generative AI sales technologies, up from less than 5% in 2023.<\/p>\n<p>The human-as-data-entry-clerk model is the piece that disappears. <a href=\"https:\/\/elementum.ai\/blog\/crm-automation\" target=\"_blank\" rel=\"noindex nofollow\">By the end of 2026, 40% of enterprise applications will include task-specific AI agents, up from less than 5% in 2025<\/a>, which represents an eightfold increase in a single year. However, <a href=\"https:\/\/prnewswire.com\/news-releases\/validity-releases-state-of-crm-data-management-in-2026-report-revealing-marketers-trust-in-their-data-hasnt-caught-up-with-their-ai-goals-302858962.html\" target=\"_blank\" rel=\"noindex nofollow\">only 21% of marketers say their CRM data is very well prepared to support AI<\/a>. The agent inflection point therefore depends on solving the data-quality problem first, and an autonomous CRM agent is the mechanism that makes AI-ready data possible at scale.<\/p>\n<h2>Why You Still Need CRM in 2026<\/h2>\n<p>Teams still need CRM in 2026, but they do not need a CRM that relies on human maintenance. Many organizations use CRM for sales reporting and automation, and <a href=\"https:\/\/www.lebow.drexel.edu\/sites\/default\/files\/2026-01\/lebow-precisely-state-data-integrity-ai-readiness-2026.pdf\" target=\"_blank\" rel=\"noindex nofollow\">94% of organizations (excluding those not yet started) have initiated discovery, business case development, or approval processes to address data quality for AI<\/a>. A CRM that depends on manual entry cannot support either goal reliably.<\/p>\n<p>For small-to-mid teams in 2026, the key decision is how to deploy an agent-first system. One option uses the agent as the standalone CRM. The other uses it as a companion layer on top of an existing Salesforce or HubSpot instance. Both paths deliver automated data quality. The standalone model fits teams that have outgrown spreadsheets and want a modern system without legacy overhead. The companion model fits teams with existing CRM investments, established workflows, and quota structures they cannot abandon, but that still need the data-in problem solved immediately.<\/p>\n<h2>How the Coffee Agent Runs Your Workflow<\/h2>\n<p>The operational flow from setup to outreach follows a clear sequence that keeps data accurate while reps keep selling.<\/p>\n<ol>\n<li>Connect Google Workspace or Microsoft 365. The Coffee Agent immediately scans emails and calendars to auto-create contacts and companies, enriching each record with job titles, funding data, and LinkedIn profiles via licensed data partners.<\/li>\n<li>Let the agent log last activity and next activity autonomously, which keeps deal state current without rep input.<\/li>\n<li>Have the AI Meeting Bot join scheduled calls, transcribe with speaker identification, and write post-call summaries, action items, and field updates directly to the CRM record.<\/li>\n<li>Use the Pipeline Compare feature to surface week-over-week deal changes automatically, which replaces manual pipeline review preparation.<\/li>\n<li>Install the Visitor Identification pixel so anonymous website traffic becomes named prospects. Suggested Leads then surfaces the specific individuals inside visiting companies who match the buyer persona.<\/li>\n<li>Build targeted prospect lists with Lead Finder using natural language commands. Lists live directly in the same system that enriches and tracks them.<\/li>\n<li>Enroll those lists in Campaigns, which sends AI-generated, multi-step email sequences from the rep&#039;s own connected mailbox, with stop-on-reply enabled by default.<\/li>\n<\/ol>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Get started with Coffee and run the full sequence from connection to outreach inside one agent.<\/a><\/p>\n<h2>2026 Market Signals Point to Agent-First CRM<\/h2>\n<p>Validity&#039;s State of CRM Data Management in 2026 report shows rapid adoption of autonomous AI agents, yet <a href=\"https:\/\/prnewswire.com\/news-releases\/validity-releases-state-of-crm-data-management-in-2026-report-revealing-marketers-trust-in-their-data-hasnt-caught-up-with-their-ai-goals-302858962.html\" target=\"_blank\" rel=\"noindex nofollow\">78% of C-suite executives and 92% of SVP\/VPs have acted on an AI recommendation they later suspected was wrong due to bad underlying data<\/a>. The gap between agent adoption and data readiness defines the current market.<\/p>\n<p><a href=\"https:\/\/completeaitraining.com\/news\/ai-in-crm-market-grows-to-1506-billion-in-2026-and-reaches\" target=\"_blank\" rel=\"noindex nofollow\">The AI-in-CRM market is projected to grow from $11.04 billion in 2025 to $15.06 billion in 2026 at a 36.4% compound annual growth rate, reaching $51.67 billion by 2030<\/a>. This growth is particularly strong in the SME segment, where CAGRs range from 9.54% to 14.9% across market reports. Smaller businesses cannot afford the productivity drag of manual systems, so they adopt automation faster. Yet <a href=\"https:\/\/elementum.ai\/blog\/crm-automation\" target=\"_blank\" rel=\"noindex nofollow\">a 2026 analysis confirms that poor data quality is the gating factor for AI-powered CRM automation, because automation scales bad data instead of fixing it<\/a>. The autonomous agent stands out as the only architecture that addresses this root cause.<\/p>\n<h2>How to Evaluate a Self-Updating CRM<\/h2>\n<p>Teams evaluating a self-updating CRM should use clear criteria before selecting a path so they avoid another partial solution.<\/p>\n<ul>\n<li><strong>Standalone versus companion:<\/strong> Teams with fewer than 20 employees and no existing CRM investment benefit most from a standalone agent-first system. Teams committed to Salesforce or HubSpot with established quota structures, required fields, and forecasting hierarchies need a companion model with deep integration knowledge, not a surface-level API connection.<\/li>\n<li><strong>Data warehouse architecture:<\/strong> The agent must store historical context in a warehouse instead of overwriting fields. Without a closed feedback loop, AI agents cannot track whether flagged risks materialized or suggested actions improved outcomes, which prevents models from calibrating over time.<\/li>\n<li><strong>Unstructured data handling:<\/strong> Confirm the system ingests email body text, call transcripts, and calendar context, not just structured field values.<\/li>\n<li><strong>Visitor identification and outreach loop:<\/strong> Check whether the system closes the loop from anonymous website traffic to named prospect to enrolled outreach sequence inside a single agent, or whether that still requires extra point solutions.<\/li>\n<li><strong>Security and compliance:<\/strong> Coffee is SOC 2 Type 2 and GDPR compliant, and data is not used to train public models.<\/li>\n<li><strong>Pricing model:<\/strong> Seat-based pricing with unlimited agent labor included avoids unpredictable metering costs on LLM usage or automated processes.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Get started with Coffee and compare the agent against your current stack on simple, seat-based pricing.<\/a><\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is an autonomous CRM agent?<\/h3>\n<p>An autonomous CRM agent is software that actively captures, structures, and writes customer interaction data to a CRM system without requiring human input. Traditional CRM software behaves as a passive database that stores only what a rep manually enters. An autonomous agent connects to email, calendar, and call recording systems to ingest both structured data, such as contact fields and deal stages, and unstructured data, such as email text, call transcripts, and meeting notes. It then uses natural language processing and orchestration logic to extract relevant facts, including budget mentions, decision-maker involvement, and next steps, and writes them to the correct CRM fields in real time. Coffee&#039;s agent operates this way whether deployed as a standalone CRM or as a companion layer on top of Salesforce or HubSpot.<\/p>\n<h3>How does a self-updating CRM handle historical context?<\/h3>\n<p>Legacy CRMs overwrite field values when they are updated, which permanently discards the prior state. A self-updating CRM built on a data warehouse architecture preserves every version of a record alongside the interaction that produced the change. The system can then surface week-over-week pipeline movement, detect when a deal has stalled relative to its historical velocity, and provide a complete interaction timeline without asking a rep to reconstruct context from memory or scattered notes. Coffee&#039;s Pipeline Compare feature comes directly from this warehouse-backed history and visualizes deal progression, regression, and new additions automatically so pipeline reviews become strategic discussions.<\/p>\n<h3>Can an AI CRM that updates itself integrate with Salesforce or HubSpot?<\/h3>\n<p>Yes. Coffee&#039;s companion app model is designed for teams that are committed to Salesforce or HubSpot and do not want to migrate their system of record. A simple authentication allows the Coffee Agent to read from and write back to the existing CRM instance. It enriches contacts, logs activities, writes call summaries, and updates deal fields while the primary CRM remains the system of record. This goes beyond surface-level integrations. Coffee has deep knowledge of Salesforce and HubSpot architecture, including quota structures, required fields, forecasting hierarchies, and custom objects, which many newer AI CRM alternatives lack. Additional integrations beyond Google Workspace and Microsoft 365 are available via Zapier, and deeper native integrations are on the roadmap.<\/p>\n<h3>Is an agent-first CRM secure for small-to-mid teams?<\/h3>\n<p>Coffee is SOC 2 Type 2 certified and GDPR compliant, which meets the security standards required by most small-to-mid-sized US companies. Data processed by the Coffee Agent is not used to train public AI models, which addresses the most common concern about enterprise data exposure in AI systems. Teams in heavily regulated industries such as healthcare or finance that require multi-year security reviews or custom compliance frameworks may need a different solution. For most SaaS, professional services, and technology companies in the 10-to-200-employee range, Coffee&#039;s existing compliance posture covers standard procurement requirements.<\/p>\n<h2>Conclusion: Move From Manual CRM to an Autonomous Agent<\/h2>\n<p>The core problem with legacy CRM systems is architectural. They were built as passive databases that depend on human beings to maintain data quality, and human beings become unreliable data-entry clerks when they have quotas to hit. The result is a compounding failure, with more than a third of organizations experiencing direct revenue loss from data quality issues and fewer than one in four marketers confident their CRM data can support AI initiatives, at the exact moment when AI-driven decisions are becoming standard practice.<\/p>\n<p>The autonomous CRM agent fixes this at the source by making good data entry automatic, which makes good data output reliable. Whether a team needs a standalone agent-first CRM or a companion layer that cleans and enriches an existing Salesforce or HubSpot instance, the evaluation criteria stay the same. Look for real-time capture from email, calendar, and calls, a data warehouse that preserves historical context, strong unstructured data handling, and a closed loop from anonymous visitor to named prospect to enrolled outreach sequence.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Get started with Coffee and replace manual data entry with an autonomous agent that keeps your pipeline accurate without human intervention.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stop losing hours to manual CRM updates. Coffee&#8217;s autonomous agent logs calls, fixes data, and runs your workflow automatically. See how it works.<\/p>\n","protected":false},"author":11,"featured_media":8919,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-8920","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\/8920","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=8920"}],"version-history":[{"count":0,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/8920\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/8919"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=8920"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=8920"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=8920"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}