{"id":1160,"date":"2025-12-15T05:00:58","date_gmt":"2025-12-15T05:00:58","guid":{"rendered":"https:\/\/blog.coffee.ai\/alternatives-to-traditional-sales-crm-agents-ai-agent-for-sales\/"},"modified":"2026-06-21T05:05:10","modified_gmt":"2026-06-21T05:05:10","slug":"alternatives-to-traditional-sales-crm-agents-ai-agent-for-sales","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/alternatives-to-traditional-sales-crm-agents-ai-agent-for-sales","title":{"rendered":"AI Agent Alternatives to Traditional Sales CRM Platforms"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: June 20, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Sales and RevOps Teams<\/h2>\n<ul>\n<li>Traditional CRMs act as passive databases that rely on manual data entry. AI sales agents actively capture, enrich, and structure data from emails, calls, and website activity.<\/li>\n<li>Five practical evaluation criteria are automated data entry depth, unstructured data processing, deployment flexibility, pipeline intelligence, and clearly measured hours saved per rep.<\/li>\n<li>Coffee outperforms legacy platforms like Salesforce and HubSpot across all criteria, reclaiming 8\u201312 hours per rep per week with both standalone and companion deployment options.<\/li>\n<li>AI agents remove dependence on rep data discipline, improve forecasting accuracy, and reduce shadow CRM usage by handling data capture and maintenance automatically.<\/li>\n<li>Teams can <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">see Coffee\u2019s pricing and deployment options<\/a> to reclaim hours and automate their sales workflow.<\/li>\n<\/ul>\n<h2>Five Criteria for Evaluating AI Agent Platforms<\/h2>\n<p><strong>1. Depth of automated data entry and enrichment.<\/strong> The platform must auto-create contacts, log activities, and enrich records from live signals, not prompt reps to fill fields. Sales reps spend approximately 70% of their time on non-selling tasks including administrative work like manually entering customer notes. Automation depth becomes the primary lever for reclaiming capacity.<\/p>\n<p><strong>2. Ability to process unstructured data.<\/strong> Legacy relational databases cannot parse email threads or call transcripts. A qualifying platform must ingest unstructured inputs and surface structured, queryable outputs such as BANT scores, next steps, and deal risk signals without human reformatting.<\/p>\n<p><strong>3. Standalone versus companion deployment flexibility.<\/strong> Some teams need a full CRM replacement, while others are locked into Salesforce or HubSpot contracts and cannot justify the cost or disruption of migration. A platform that forces one deployment model eliminates half its addressable market by excluding either replacement buyers or enhancement buyers and creates unnecessary risk for teams that might need to shift models as they scale.<\/p>\n<p><strong>4. Pipeline intelligence without spreadsheets.<\/strong> Forecasting accuracy depends entirely on data quality. AI agents that analyze pipeline data can predict quota shortfalls if current trends continue and enable proactive reallocation. That level of insight requires underlying data captured by an agent instead of sporadic rep entry.<\/p>\n<p><strong>5. Measurable hours saved per rep per week.<\/strong> Any platform claiming productivity gains must produce a verifiable number. CRM data entry and pipeline updates alone can consume several hours of a rep&#8217;s week with zero direct revenue impact. The evaluation benchmark is how many of those hours the agent actually reclaims.<\/p>\n<p>With these five criteria established, the following comparison shows how each platform performs across this evaluation framework.<\/p>\n<h2>Side-by-Side Comparison Table (2026 Scores)<\/h2>\n<p>The table below highlights a clear pattern: Coffee is the only platform that delivers consistent time savings across all five criteria, while legacy CRMs depend on manual workarounds and newer tools trade off automation depth or deployment flexibility.<\/p>\n<table>\n<thead>\n<tr>\n<th>Platform<\/th>\n<th>Automated Data Entry &amp; Enrichment<\/th>\n<th>Unstructured Data Processing<\/th>\n<th>Standalone + Companion Deployment<\/th>\n<th>Pipeline Intelligence (No Spreadsheets)<\/th>\n<th>Hours Saved \/ Rep \/ Week<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Coffee<\/strong><\/td>\n<td>Full agent automation, data warehouse architecture<\/td>\n<td>Emails, transcripts, calendars unified<\/td>\n<td>Both models supported natively<\/td>\n<td>Pipeline Compare, week-over-week agent tracking<\/td>\n<td><strong>8\u201312 hrs<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Salesforce + Einstein<\/td>\n<td>Partial, heavy manual field maintenance required<\/td>\n<td>Limited, relational DB loses historical context<\/td>\n<td>Standalone only, no companion layer<\/td>\n<td>Requires paid add-ons and CSV exports<\/td>\n<td>Minimal<\/td>\n<\/tr>\n<tr>\n<td>HubSpot AI<\/td>\n<td>Partial, bolted onto marketing tool architecture<\/td>\n<td>Limited, structured fields only at core<\/td>\n<td>Standalone only<\/td>\n<td>Basic, spreadsheet workarounds common<\/td>\n<td>Low<\/td>\n<\/tr>\n<tr>\n<td>Day.ai<\/td>\n<td>Productivity-focused, unstructured data emphasis<\/td>\n<td>Strong on unstructured, limited structured sync<\/td>\n<td>Standalone only<\/td>\n<td>Early-stage, limited forecasting depth<\/td>\n<td>Moderate<\/td>\n<\/tr>\n<tr>\n<td>Clarify CRM<\/td>\n<td>Modern UI, limited Salesforce\/HubSpot integration depth<\/td>\n<td>Moderate<\/td>\n<td>Standalone only<\/td>\n<td>Developing<\/td>\n<td>Moderate<\/td>\n<\/tr>\n<tr>\n<td>Pipedrive<\/td>\n<td>Low, rep-driven entry model<\/td>\n<td>Minimal<\/td>\n<td>Standalone only<\/td>\n<td>Basic visual pipeline, no agent layer<\/td>\n<td>Low<\/td>\n<\/tr>\n<tr>\n<td>Attio<\/td>\n<td>Modern UI skin, passive database logic underneath<\/td>\n<td>Minimal<\/td>\n<td>Standalone only<\/td>\n<td>Limited<\/td>\n<td>Low<\/td>\n<\/tr>\n<tr>\n<td>Close CRM<\/td>\n<td>Low, built-in calling but manual logging<\/td>\n<td>Minimal<\/td>\n<td>Standalone only<\/td>\n<td>Basic<\/td>\n<td>Low<\/td>\n<\/tr>\n<tr>\n<td>RB2B<\/td>\n<td>Visitor ID only, no CRM record creation<\/td>\n<td>N\/A<\/td>\n<td>Point tool, no CRM<\/td>\n<td>None<\/td>\n<td>N\/A<\/td>\n<\/tr>\n<tr>\n<td>Warmly<\/td>\n<td>Visitor ID + company-level data, no Suggested Leads<\/td>\n<td>N\/A<\/td>\n<td>Point tool, no CRM<\/td>\n<td>None<\/td>\n<td>N\/A<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>See how Coffee reclaims 8\u201312 hours per rep per week<\/strong><\/a><\/p>\n<h2>Setup and Onboarding Effort for AI CRM Agents<\/h2>\n<p>Successful AI CRM adoption typically follows a phased rollout beginning with a foundation assessment, data-quality audit, process mapping, and technology inventory rather than a full replacement on day one. Phase 1 usually runs four to six weeks. Phase 2 automation and predictive analytics often add another eight to twelve weeks.<\/p>\n<p>Coffee compresses this timeline. Connecting Google Workspace or Microsoft 365 triggers immediate contact auto-creation and activity logging. The Companion App authenticates against an existing Salesforce or HubSpot instance through a single OAuth flow and then begins writing enriched data back without field mapping sessions or dedicated integration teams.<\/p>\n<p>Salesforce and HubSpot deployments at Coffee are built with deep awareness of quotas, forecasting hierarchies, and required fields. Newer entrants like Clarify and Day.ai have not yet closed these gaps. Legacy platforms usually require the opposite sequence: administrators configure fields, reps receive training, and data quality degrades as adoption slips.<\/p>\n<p>AI automation with Coffee can cut manual data work substantially. Teams often reclaim hours previously spent on repetitive entry, duplicate detection, and record enrichment.<\/p>\n<h2>Data Capture and Maintenance Quality<\/h2>\n<p><a href=\"https:\/\/databar.ai\/blog\/article\/sales-productivity-with-clean-data-quantify-the-time-savings\" target=\"_blank\" rel=\"noindex nofollow\">Research shows sales reps spend roughly 27% of their working hours dealing with inaccurate CRM data, translating to 546 hours per representative per year on bad-data cleanup.<\/a> That figure assumes reps enter data consistently. When adoption drops, the CRM turns into a shadow system and spreadsheets become the real workspace.<\/p>\n<p>Coffee&#8217;s agent reads emails and calendar events to auto-populate contacts, companies, and activities, which establishes the foundational record. Call transcripts from Zoom, Teams, and Google Meet are then processed post-call to layer in qualification data. The agent extracts BANT, MEDDIC, or SPICED qualifications and writes structured outputs directly to that same record, so no human reformatting is required.<\/p>\n<p><a href=\"https:\/\/solace.com\/blog\/real-time-data-enrichment-ai-agents-micro-integrations\" target=\"_blank\" rel=\"noindex nofollow\">The root architectural issue for enterprise AI is that agents consuming stale data answer questions using records updated yesterday or orders cancelled an hour ago<\/a>. Coffee&#8217;s data warehouse architecture addresses this problem by maintaining a continuous, timestamped history that relational databases overwrite on every field update.<\/p>\n<h2>Frontline Usability and Rep Adoption<\/h2>\n<p>Rep adoption remains the single largest implementation risk for any CRM. <a href=\"https:\/\/klu.so\/blog\/manual-crm-updates-sales-productivity\" target=\"_blank\" rel=\"noindex nofollow\">A salesperson spends three to six hours every week on manual data entry into CRMs such as HubSpot, Pipedrive, and Attio<\/a>. That time registers as friction, not value, from the rep&#8217;s perspective and encourages shadow CRMs like Notion pages, personal spreadsheets, and Slack threads.<\/p>\n<p>Coffee inverts this dynamic. The agent handles the busywork, and reps interact with a &#8220;Today&#8221; page that surfaces meeting briefings, attendee context, and next steps. After each call, the agent drafts follow-up emails in Gmail for one-click review. Reps serve the deal, not the database.<\/p>\n<p>Platforms like Attio offer modern UI skins but retain passive database logic underneath. Adoption on those tools still depends on rep discipline instead of agent automation.<\/p>\n<h2>Manager Visibility and Forecasting Accuracy<\/h2>\n<p>Companies using AI agents for lead generation report higher conversion rates and reduced manual work on repetitive tasks such as research, qualification, scoring, and routing. Forecasting accuracy sits downstream of data quality. When agents capture every interaction, pipeline reviews reflect reality rather than rep memory.<\/p>\n<p>Coffee&#8217;s Pipeline Compare feature visualizes week-over-week deal movement, including progressed opportunities, stalled deals, and new additions, without CSV exports or manual roll-ups. Salesforce and HubSpot usually require paid forecasting add-ons and dedicated RevOps bandwidth to reach similar visibility, which creates disproportionate overhead for 10\u201350 person teams.<\/p>\n<h2>Integration Complexity with Existing Stacks<\/h2>\n<p>Integration with Salesforce and HubSpot in 2026 involves far more than field mapping. Quota hierarchies, required fields, validation rules, and forecast categories must all be respected for data written by an external agent to survive without sync errors. Coffee&#8217;s Companion App is built with explicit awareness of these constraints.<\/p>\n<p>Clarify and Day.ai are architected primarily as standalone systems and often encounter friction when writing back to established Salesforce or HubSpot instances with complex configurations. For teams not yet committed to a legacy platform, Coffee&#8217;s Standalone CRM removes this complexity entirely.<\/p>\n<p><a href=\"https:\/\/yourgpt.ai\/blog\/general\/ai-agent-playbook-support-sales-success\" target=\"_blank\" rel=\"noindex nofollow\">Integration depth is evaluated by confirming deep read\/write access and workflow-triggering capability in core systems, rather than read-only catalogue listings.<\/a> Coffee&#8217;s current third-party integrations route through Zapier, and deeper native connections sit on the roadmap.<\/p>\n<h2>Long-Term Scalability and Deployment Paths<\/h2>\n<p>Standalone deployment fits teams of one to twenty that have outgrown spreadsheets but cannot justify Salesforce&#8217;s administrative overhead. Companion deployment fits teams of twenty to fifty already invested in Salesforce or HubSpot that need better data quality without a rip-and-replace migration.<\/p>\n<p>Coffee is the only platform in this analysis that supports both models from a single product. A company can start standalone and layer in a legacy CRM later, or start with a companion model and move to standalone, without switching vendors.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>Explore Coffee&#8217;s deployment options<\/strong> and choose the model that fits your stack today.<\/a><\/p>\n<h2>Visitor Identification and Outbound Loop Workflows<\/h2>\n<p>Most sales teams lack visibility into anonymous website traffic. Coffee&#8217;s visitor identification installs through a single tracking pixel in the site&#8217;s <code>&lt;head&gt;<\/code> tag. The agent infers visitor identity such as name, title, email, and LinkedIn profile along with company, pages visited, time on site, and visit frequency.<\/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>Real-time Slack notifications surface high-fit visitors, and one click adds the prospect to Coffee with enrichment pre-filled. The differentiating feature is Suggested Leads. RB2B and Warmly surface either company-level data or undifferentiated people lists.<\/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>Coffee uses the buyer persona to recommend the two or three specific individuals inside the visiting company most likely to convert, with LinkedIn profiles ready for immediate outreach or auto-enrollment into a drip campaign. AI-powered chatbots for real-time website lead qualification can then deliver more qualified leads and higher conversion rates while operating around the clock.<\/p>\n<h2>Meeting Orchestration and Automated Follow-Ups<\/h2>\n<p>Coffee&#8217;s agent supports the full meeting lifecycle. Before the meeting, it generates a briefing that covers attendee roles, prior interactions, and open action items. During the call, the AI Meeting Bot joins Zoom, Teams, or Google Meet to record and transcribe.<\/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>After the call, the agent produces a structured summary, extracts next steps, and drafts a follow-up email in Gmail for rep review before sending. AI-driven qualification reduces response times from hours or days to minutes by instantly scoring and routing leads instead of relying on manual review. The same logic applies to follow-up latency, since an agent that drafts the email immediately after the call closes the gap between conversation and outreach that often costs deals.<\/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>Decision Framework: Matching Platforms to Team Profiles<\/h2>\n<p>Use the following checklist to match platform type to your current context and growth plans.<\/p>\n<p><strong>Choose Coffee Standalone<\/strong> if your team has one to twenty people, you currently work in spreadsheets or Notion, and you want an agent-managed system of record without Salesforce or HubSpot overhead.<\/p>\n<p><strong>Choose Coffee Companion<\/strong> if your team has twenty to fifty people, you are committed to Salesforce or HubSpot, and your primary problem is low CRM adoption and bad data quality rather than platform selection.<\/p>\n<p><strong>Choose a legacy platform with AI add-ons<\/strong> if your organization exceeds 200 seats, has custom workflow requirements built over years, and can staff a dedicated RevOps function to manage data quality manually.<\/p>\n<p><strong>Choose a point tool (RB2B, Warmly)<\/strong> if visitor identification is your only requirement and you already have a functioning CRM with clean data, while accepting that these tools do not close the loop to outbound without manual steps.<\/p>\n<p>Coffee is the only platform in this analysis that meets all five evaluation criteria, including automated data entry, unstructured data processing, dual deployment, pipeline intelligence, and measurable hours saved, for both standalone and companion use cases. Reclaiming several hours of CRM admin time each week can add multiple extra weeks of selling time per year for a full-time rep, and Coffee&#8217;s agent targets eight to twelve hours weekly.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does it take to implement Coffee?<\/h3>\n<p>For the Standalone CRM, most teams become operational within a single session. Connecting Google Workspace or Microsoft 365 triggers immediate contact auto-creation and activity logging, with no field-mapping workshops or data migration projects required for net-new deployments.<\/p>\n<p>For the Companion App layered onto Salesforce or HubSpot, a single OAuth authentication initiates the sync. Coffee&#8217;s agent begins enriching and writing data back to the existing system within hours, not weeks. Teams that previously attempted AI CRM implementations with other vendors typically find Coffee&#8217;s onboarding faster because the agent handles configuration tasks that other platforms delegate to administrators.<\/p>\n<h3>What is the migration effort if we are moving from Salesforce or HubSpot?<\/h3>\n<p>Teams migrating to Coffee Standalone from a legacy CRM can import existing contact and company records via standard CSV or direct connector. Coffee&#8217;s agent immediately begins enriching and updating those records from live email and calendar signals, so stale imported data is corrected automatically instead of requiring a manual cleanup sprint before go-live.<\/p>\n<p>Teams that prefer to keep Salesforce or HubSpot as the system of record can deploy Coffee as a Companion App instead and avoid migration entirely. This dual-model flexibility keeps the decision to migrate commercial and strategic rather than technically forced.<\/p>\n<h3>Is Coffee SOC 2 Type 2 and GDPR compliant?<\/h3>\n<p>Yes. Coffee is SOC 2 Type 2 certified and GDPR compliant. Customer data is not used to train public AI models. For teams in regulated-adjacent industries such as fintech or legal tech, this certification usually satisfies the security review requirements that procurement and legal teams raise during vendor evaluation.<\/p>\n<p>Coffee is not currently positioned for heavily regulated industries such as healthcare or financial services that require multi-year security reviews or custom data residency arrangements.<\/p>\n<h3>How does Coffee&#8217;s pricing model work?<\/h3>\n<p>Coffee uses seat-based pricing. Each human user on the team occupies one seat, and the agent&#8217;s labor for data entry, enrichment, meeting orchestration, pipeline tracking, visitor identification, and follow-up drafting is included without additional metering on LLM usage, API calls, or automated processes.<\/p>\n<p>This model keeps costs predictable as deal volume and lead volume scale, unlike usage-based alternatives where automation costs rise in proportion to the activity the agent performs.<\/p>\n<h3>How do we evaluate whether Coffee is the right fit before committing?<\/h3>\n<p>The most direct evaluation method is to connect Coffee to your Google Workspace or Microsoft 365 environment and observe what the agent captures in the first 48 hours without manual input. The volume and accuracy of auto-created contacts, logged activities, and enriched records provide a concrete baseline for the hours-saved calculation.<\/p>\n<p>For teams evaluating the Companion App, the equivalent test is authenticating against a Salesforce or HubSpot sandbox and reviewing what Coffee writes back after a week of normal sales activity. Coffee&#8217;s pricing page then provides the starting point for scoping seat counts and deployment model.<\/p>\n<h2>Conclusion: Why AI Agents Are Replacing Passive CRMs<\/h2>\n<p>Traditional CRMs remain passive databases that produce accurate forecasts only when humans reliably enter accurate data, a condition that rarely holds at scale. Reps spend the majority of their time on administrative work rather than selling, and CRM data entry alone consumes several hours weekly with no direct revenue impact. AI agent platforms break this dependency by automating capture, enrichment, and orchestration at the source.<\/p>\n<p>Evaluated against the five criteria in this guide, including automated data entry, unstructured data processing, deployment flexibility, pipeline intelligence, and measurable hours saved, Coffee is the only platform that qualifies fully in both standalone and companion configurations. It runs on a data warehouse architecture that preserves historical context, processes unstructured inputs that relational databases cannot handle, and integrates with Salesforce and HubSpot at the depth that mid-market RevOps teams require.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><strong>Start automating your sales workflow with Coffee<\/strong> and replace manual data entry with an agent that works while your reps sell.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Coffee replaces manual CRM data entry with autonomous AI agents that research leads, run outreach, and save reps 8\u201312 hrs\/week. See how it compares.<\/p>\n","protected":false},"author":11,"featured_media":1150,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1160","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\/1160","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=1160"}],"version-history":[{"count":3,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/1160\/revisions"}],"predecessor-version":[{"id":7846,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/1160\/revisions\/7846"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/1150"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=1160"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=1160"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=1160"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}