{"id":8906,"date":"2026-09-06T05:01:06","date_gmt":"2026-09-06T05:01:06","guid":{"rendered":"https:\/\/www.coffee.ai\/articles\/salesforce-data-automation-alternatives"},"modified":"2026-09-06T05:01:06","modified_gmt":"2026-09-06T05:01:06","slug":"salesforce-data-automation-alternatives","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/salesforce-data-automation-alternatives","title":{"rendered":"Salesforce Data Automation: When Native Tools Stop Working"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for RevOps Leaders<\/h2>\n<ul>\n<li>Salesforce data automation struggles with unstructured inputs like call transcripts and email threads, which leaves CRM records incomplete and forecasts unreliable.<\/li>\n<li>Native tools such as Flow, Data Cloud, Agentforce, and MuleSoft each have limits with semantic or fuzzy data and require heavy maintenance or extra configuration.<\/li>\n<li>A companion agent layer automates ingestion of both structured and unstructured data and writes clean, enriched records back to Salesforce without manual entry.<\/li>\n<li>Companies using automated capture see activity completeness exceed 95%, compared to the 30\u201350% typical with manual entry, saving hundreds of person-days and tens of thousands in labor costs each year.<\/li>\n<li>Teams ready to eliminate manual data entry can <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">see Coffee pricing and start a trial<\/a> to reclaim selling hours and improve pipeline accuracy.<\/li>\n<\/ul>\n<h2>Salesforce Data Automation in 2026: Core Options and Tradeoffs<\/h2>\n<p>Salesforce data automation uses software tools to capture, transform, and synchronize data into and out of a Salesforce org without human typing. Native options include Flow for record-triggered logic, Data Cloud for unified data ingestion, Agentforce for AI-driven decision layers, and MuleSoft for API-based integration. A companion agent layer sits above these tools to handle unstructured inputs such as emails, transcripts, and calendar events that native tools cannot reliably parse, then writes enriched structured records back to Salesforce automatically.<\/p>\n<p>The table below compares the five primary approaches on dimensions that matter most to mid-market RevOps teams planning a 2026 deployment.<\/p>\n<table>\n<thead>\n<tr>\n<th>Tool<\/th>\n<th>Structured Data Handling<\/th>\n<th>Unstructured Data Handling<\/th>\n<th>2026 Total Cost of Ownership Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Salesforce Flow<\/td>\n<td>Native, record-triggered logic on CRM fields<\/td>\n<td><a href=\"https:\/\/nandann.com\/blog\/salesforce-flow-losing-ground-python-agents\" target=\"_blank\" rel=\"noindex nofollow\">Fails on semantic or fuzzy inputs, limited to exact SOQL field matches<\/a><\/td>\n<td><a href=\"https:\/\/automationatlas.io\/answers\/salesforce-flow-review-2026\" target=\"_blank\" rel=\"noindex nofollow\">Dedicated admin salary $80K\u2013$120K per year, implementation $15K\u2013$200K+, 100-user mid-market deployment can exceed $250K per year all-in<\/a><\/td>\n<\/tr>\n<tr>\n<td>Data Cloud<\/td>\n<td>Unified ingestion across structured sources<\/td>\n<td>Supports PDFs, transcripts, and email threads via vector embeddings and RAG, requires chunking strategy and governance configuration<\/td>\n<td>Additional SKU cost on top of core Salesforce licenses, implementation complexity and governance work increase total cost of ownership<\/td>\n<\/tr>\n<tr>\n<td>Agentforce<\/td>\n<td><a href=\"https:\/\/sfapps.info\/agentforce-implementation\" target=\"_blank\" rel=\"noindex nofollow\">Decision layer over existing Flows and Apex, relies on structured field completeness<\/a><\/td>\n<td><a href=\"https:\/\/sfapps.info\/agentforce-implementation\" target=\"_blank\" rel=\"noindex nofollow\">Incomplete or inconsistent fields limit effectiveness, unstructured handling requires Data Cloud integration<\/a><\/td>\n<td>Basic MCP access included in Enterprise Edition, Flex Credits apply for Agentforce actions<\/td>\n<\/tr>\n<tr>\n<td>MuleSoft<\/td>\n<td>API-based integration across any structured system<\/td>\n<td>Passes through unstructured payloads, semantic processing requires an additional AI layer<\/td>\n<td><a href=\"https:\/\/techparrot.io\/blog\/salesforce-support-maintenance-cost\" target=\"_blank\" rel=\"noindex nofollow\">US onshore retainers $3K\u2013$8K per month for single-cloud orgs under 200 users, separate platform license suited to very high-volume pipelines<\/a><\/td>\n<\/tr>\n<tr>\n<td>Companion Agent (e.g., Coffee)<\/td>\n<td>Auto-creates and enriches contacts, companies, and activities from email and calendar<\/td>\n<td>Ingests call transcripts, email threads, and meeting notes, then writes structured summaries back to Salesforce<\/td>\n<td>Seat-based pricing that consolidates enrichment, recording, and pipeline intelligence into one agent and reduces point-solution stack cost<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>The Hidden Cost of Native Salesforce Automation<\/h2>\n<p><a href=\"https:\/\/scallar.in\/resources\/sales-automation-statistics\" target=\"_blank\" rel=\"noindex nofollow\">Seventy-one percent of sales reps say they spend too much time on data entry<\/a>, which leaves only a fraction of the workweek for actual selling. Salesforce\u2019s 5th edition State of Sales report shows reps spend only 28% of their week selling, with the rest consumed by administrative tasks they consistently deprioritize. The downstream effect is predictable. <a href=\"https:\/\/weflow.ai\/blog\/sales-forecasting-accuracy\" target=\"_blank\" rel=\"noindex nofollow\">With manual data entry, Salesforce activity completeness typically sits at 30\u201350%<\/a>, and <a href=\"https:\/\/www.validity.com\/wp-content\/uploads\/2024\/05\/The-State-of-CRM-Data-Management-in-2024.pdf\" target=\"_blank\" rel=\"noindex nofollow\">24% of CRM admins report that less than half of their organization\u2019s data is accurate and complete<\/a>, with missing close dates, blank next-step fields, and stale contacts each degrading forecast quality.<\/p>\n<p>These data quality issues often trace back to the tools themselves. The maintenance cost of keeping native automation functional compounds the problem by consuming resources that could otherwise support rep adoption and data hygiene. <a href=\"https:\/\/automationatlas.io\/answers\/salesforce-flow-review-2026\" target=\"_blank\" rel=\"noindex nofollow\">In one mid-market SaaS deployment of 14 Salesforce Flows for 180 sales reps, implementation cost $28,000 over five weeks, with the consultant spending 40% of project time debugging flow versioning conflicts.<\/a> <a href=\"https:\/\/valintry360.com\/blogs\/how-much-does-salesforce-consulting-cost-2026-pricing-breakdown\" target=\"_blank\" rel=\"noindex nofollow\">US boutique onshore consultancies typically bill $125\u2013$250 per hour for Salesforce services including support and maintenance<\/a>, which prices a modest 20-hour monthly retainer at $3,000\u2013$5,000.<\/p>\n<p>Gartner estimates that poor data quality costs organizations an average of $12.9 million per year through misallocated resources and missed opportunities. For a $10M ARR SaaS company, that figure represents a material drag on growth.<\/p>\n<p>Teams can eliminate much of that tax by automating data entry with Coffee and similar companion agents, then <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">see pricing and start a trial<\/a> when they are ready to quantify the savings.<\/p>\n<h2>Handling Call Transcripts and Email Threads Salesforce Cannot Parse<\/h2>\n<p><a href=\"https:\/\/nandann.com\/blog\/salesforce-flow-losing-ground-python-agents\" target=\"_blank\" rel=\"noindex nofollow\">Flow is designed primarily for structured data and record-based automation<\/a>. It can return exact matches from Salesforce objects but fails when the answer hides in unstructured text or requires semantic understanding. <a href=\"https:\/\/nandann.com\/blog\/salesforce-flow-losing-ground-python-agents\" target=\"_blank\" rel=\"noindex nofollow\">Flow processes records sequentially and resets variables between steps<\/a>, which makes it unable to maintain full conversation history or shared context across multiple agents when handling unstructured inputs like emails and transcripts.<\/p>\n<p>Data Cloud addresses some of these gaps by providing an unstructured data layer. Salesforce Data Cloud parses unstructured material, divides it into smaller meaningful chunks, converts those chunks into vector embeddings, and stores them in a searchable index so that users or autonomous AI agents can retrieve the most relevant passages via vector retrieval. This architecture requires deliberate configuration. Effective unstructured data processing requires choosing chunking strategies that reflect content structure, retaining useful metadata such as titles and source URLs, and applying governance policies including access controls, data classification, consent, and retention throughout the content lifecycle. For a 50-person SaaS company without a dedicated Salesforce architect, that configuration overhead often becomes prohibitive.<\/p>\n<p>Agentforce introduces an AI decision layer on top of this stack. <a href=\"https:\/\/sfapps.info\/agentforce-implementation\" target=\"_blank\" rel=\"noindex nofollow\">Agentforce functions as a decision layer that sits on top of existing Salesforce workflows, using Topics, Instructions, and Actions to evaluate data and select the appropriate automation path, but all business actions are executed through existing Flows, Apex, or integrations rather than by the agents themselves.<\/a> When the underlying CRM fields are incomplete, <a href=\"https:\/\/sfapps.info\/agentforce-implementation\" target=\"_blank\" rel=\"noindex nofollow\">incomplete or inconsistent fields and incorrect mappings can lead to incorrect decisions or outputs, which limits effectiveness on unstructured inputs.<\/a> A companion agent layer resolves this by handling ingestion and structuring of unstructured data before it reaches Salesforce, so the fields Agentforce reads stay accurate and complete.<\/p>\n<h2>How a Companion Agent Changes Daily Salesforce Workflows<\/h2>\n<p>A companion agent like Coffee changes daily work by taking over the repetitive capture and structuring of customer interactions. The operational flow begins at authentication. Connecting Google Workspace or Microsoft 365 gives the agent immediate access to emails, calendar events, and meeting recordings. From that point, the agent works autonomously through four stages that remove manual data entry at every customer touchpoint.<\/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>The agent scans emails and calendars to auto-create contacts, companies, and activity logs in Salesforce, associating every interaction with the correct record without rep input.<\/li>\n<li>An AI meeting bot joins Zoom, Teams, or Google Meet calls to record and transcribe in real time.<\/li>\n<li>After each call, the agent generates structured summaries aligned to sales methodologies such as BANT, MEDDIC, or SPICED, drafts follow-up emails for rep review, and writes all outputs back to the corresponding Salesforce opportunity.<\/li>\n<li>The Pipeline Compare view surfaces week-over-week changes, including progressed deals, stalled opportunities, and new additions, without requiring a CSV export or manual pipeline scrub.<\/li>\n<\/ol>\n<p><a href=\"https:\/\/weflow.ai\/blog\/sales-forecasting-accuracy\" target=\"_blank\" rel=\"noindex nofollow\">Automated capture tools raise Salesforce activity completeness from the typical 30\u201350% achieved with manual entry to 95% or higher.<\/a> This completeness improvement, the 95%+ figure mentioned earlier, comes from capturing every email, meeting, and follow-up automatically rather than relying on rep memory. <a href=\"https:\/\/revenuegrid.com\/blog\/revenue-forecasting-in-salesforce\" target=\"_blank\" rel=\"noindex nofollow\">Vapotherm captured 110,000+ emails and 27,000 calendar events in year one via automated activity capture, saving 761 person-days of manual entry and more than $175,000 in labor costs.<\/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<h2>Why Traditional Salesforce Approaches Fall Short<\/h2>\n<p>The hidden labor cost of native Salesforce automation rarely appears on a license invoice. It accumulates in admin salaries, consultant retainers, and the opportunity cost of reps who update records minutes before pipeline reviews rather than throughout the week. <a href=\"https:\/\/revenuegrid.com\/blog\/revenue-forecasting-in-salesforce\" target=\"_blank\" rel=\"noindex nofollow\">The average pipeline review takes more than 53 minutes, most of it spent listening to reps narrate deal status that activity data could verify in seconds.<\/a><\/p>\n<p>A second failure mode is the loss of historical context. Legacy CRM architectures rely on basic relational databases where, when fields are updated, the prior state is overwritten and lost. Without a data warehouse that captures the history of every deal change, Pipeline Compare views and trend analyses remain impossible to produce without manual spreadsheet work.<\/p>\n<p>The decision between doubling down on native tools and adding a companion agent layer comes down to three questions.<\/p>\n<ul>\n<li>Does the team have a dedicated Salesforce admin or implementation partner available to maintain and refactor Flows as the org evolves?<\/li>\n<li>Are call transcripts and email threads currently captured and written back to opportunity records in a structured format?<\/li>\n<li>Is forecast accuracy above 80%, or does the pipeline review regularly surface surprises that activity data should have flagged weeks earlier?<\/li>\n<\/ul>\n<p>A \u201cno\u201d answer to any of these questions signals that native tooling alone is not sufficient. Traditional Salesforce automation using Flow and Apex relies on explicit branches and predictable deterministic logic, which becomes strained when exposed to the ambiguity of user intent in agentic systems that introduce nondeterminism from generative AI.<\/p>\n<h2>How Coffee\u2019s Companion Agent Performs in the Field<\/h2>\n<p><strong>Reduced admin burden.<\/strong> Reps using automated activity capture in Salesforce save several hours per week on admin work. Coffee\u2019s agent handles contact creation, activity logging, and data enrichment, including job titles, funding data, and LinkedIn profiles, without rep 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>That time savings translates directly into cleaner pipeline data. <strong>Cleaner pipeline data.<\/strong> <a href=\"https:\/\/getgangly.com\/blog\/sales-forecasting-accuracy-statistics\" target=\"_blank\" rel=\"noindex nofollow\">Companies using AI for automated CRM updates reduce missing-field rates from 41% to under 8% within 90 days, improving downstream forecast accuracy by 7 to 12 percentage points.<\/a> Because Coffee writes structured post-call summaries directly to Salesforce opportunity records, next-step fields, stakeholder counts, and competitive status are populated automatically after every call. <a href=\"https:\/\/www.gong.io\/blog\/spot-these-four-red-flags-to-boost-forecast-accuracy-and-revenue-predictability\" target=\"_blank\" rel=\"noindex nofollow\">Gong analyses discuss next-step discussions, stakeholder involvement, and competitive mentions as deal signals but do not identify four specific CRM fields most correlated with forecast accuracy in any 2025 study<\/a>, which highlights how much value sits in consistent field completion.<\/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>Cleaner data then enables faster reviews. <strong>Faster weekly reviews.<\/strong> The Pipeline Compare feature visualizes week-over-week deal movement and replaces manual CSV exports and the interrogation dynamic that characterizes most pipeline calls. Reviews shift from status narration to strategic discussion.<\/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<p>Those smoother reviews support higher adoption. <strong>Higher rep adoption.<\/strong> Reps adopt tools that reduce their workload rather than add to it. When the agent handles data entry, the CRM becomes a resource reps consult instead of a database they resent maintaining.<\/p>\n<p>Teams that want to reclaim those selling hours can <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">explore Coffee\u2019s pricing and deployment options<\/a> and map the agent\u2019s impact to their current admin burden.<\/p>\n<h2>Neutral Evaluation Framework for Salesforce Data Automation<\/h2>\n<p>Mid-market RevOps teams evaluating any Salesforce data automation solution can use a simple sequence of checks before committing.<\/p>\n<p>Start with integration depth. Confirm that the solution writes enriched, structured data back to native Salesforce fields such as opportunities, contacts, and activities, rather than storing insights in a separate silo that reps must consult separately.<\/p>\n<p>Once native integration is clear, review security and compliance. SOC 2 Type 2 certification and GDPR compliance form the baseline. Teams should also confirm explicitly that customer data is not used to train public AI models.<\/p>\n<p>With security established, examine data-source breadth. The solution needs to ingest both structured sources such as CRM fields and enrichment databases and unstructured sources such as email threads, call transcripts, and calendar events to address data quality issues at the root.<\/p>\n<p>Next, assess implementation effort. For a team without a dedicated Salesforce admin, any implementation that requires weeks of consultant time or Flow refactoring reintroduces the cost it was meant to eliminate. Time-to-value should feel short and predictable.<\/p>\n<p>Finally, review the maintenance model. Seat-based pricing with no metering on AI usage or process volume is easier to budget and scale than consumption-based models that penalize adoption as usage grows.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Is the data quality from a companion agent comparable to a dedicated enrichment tool like ZoomInfo?<\/h3>\n<p>For most mid-market use cases, data quality is comparable. A companion agent like Coffee enriches contact and company records with job titles, funding data, and LinkedIn profiles via licensed data partners, which covers the majority of enrichment needs without a separate subscription. The main advantage over a standalone enrichment tool is that the data lives in the same system that captures activity, runs outreach, and tracks pipeline, which removes the manual export-import cycle that degrades data freshness between tools.<\/p>\n<h3>How does Coffee handle data security, and is customer data used to train AI models?<\/h3>\n<p>Coffee is SOC 2 Type 2 certified and GDPR compliant, and customer data is not used to train public AI models. These requirements now serve as table stakes for any tool handling sales communications and CRM records. Teams in regulated-adjacent industries should request documentation of both certifications before deployment.<\/p>\n<h3>Does Coffee integrate with tools outside Salesforce, and how does that compare to native Salesforce connectors?<\/h3>\n<p>Coffee currently supports integrations via Zapier, with deeper native connectors on the product roadmap. For the core use case of connecting Google Workspace or Microsoft 365 to capture emails, calendar events, and call recordings and write them back to Salesforce, the integration is handled natively through a simple authentication flow, not through Zapier. Zapier connectivity then extends the agent\u2019s reach to other tools in the stack without requiring custom development.<\/p>\n<h3>How is Coffee priced, and what is included in a seat?<\/h3>\n<p>Coffee uses seat-based pricing. Each human seat includes the agent\u2019s full labor, including automatic data entry, meeting recording and summarization, pipeline intelligence, and enrichment, with no separate metering on AI usage, process volume, or LLM calls. This model keeps cost predictable as the team scales and avoids the consumption-based surprises common in enterprise AI platforms.<\/p>\n<h3>What does migration look like for a team already using Salesforce with existing data?<\/h3>\n<p>Coffee\u2019s Companion App deploys as an agent layer on top of an existing Salesforce instance rather than replacing it. The authentication process connects Coffee to the Salesforce org and to Google Workspace or Microsoft 365. The agent then scans historical emails and calendar events to backfill contact and activity records and enriches existing records rather than requiring a data migration. Existing Salesforce configurations, including custom fields, required fields, forecasting hierarchies, and quota structures, remain intact because the agent writes into the existing schema rather than imposing a new one.<\/p>\n<h2>Conclusion: Turning Salesforce into a Reliable Revenue System<\/h2>\n<p>Native Salesforce automation tools deliver real value for structured, deterministic workflows. They stop delivering value when the data that matters most, such as call transcripts, email threads, and meeting notes, lives outside the fields they can read. The result is a predictable pattern of incomplete records, unreliable forecasts, and reps who spend their week serving the CRM instead of their customers.<\/p>\n<p>The diagnostic framework stays simple. If the team lacks a dedicated admin to maintain Flows, if unstructured data from calls and emails is not being written back to opportunity records, or if forecast accuracy sits below 80%, native tooling alone is not the answer. A companion agent layer that ingests both structured and unstructured data, enriches records automatically, and writes clean outputs back to Salesforce resolves systemic data quality problems without adding headcount or requiring a platform migration.<\/p>\n<p>Teams that want Salesforce to function as a reliable source of pipeline intelligence can <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">review Coffee pricing and launch a companion agent pilot<\/a> tailored to their current stack.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Struggling with Salesforce data gaps? Coffee automates CRM updates, parses transcripts, and keeps your pipeline clean. See how it works.<\/p>\n","protected":false},"author":11,"featured_media":8905,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-8906","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\/8906","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=8906"}],"version-history":[{"count":0,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/8906\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/8905"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=8906"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=8906"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=8906"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}