{"id":751,"date":"2026-05-27T05:03:05","date_gmt":"2026-05-27T05:03:05","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/userinterviews-alternative-discussio\/"},"modified":"2026-05-27T05:03:05","modified_gmt":"2026-05-27T05:03:05","slug":"userinterviews-alternative-discussio","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/userinterviews-alternative-discussio\/","title":{"rendered":"The Best User Interviews &amp; Discuss.io Alternative"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2 id=\"key-takeaways\">Why Enterprise Teams Choose Listen Labs Over User Interviews + Discuss.io<\/h2>\n<ul>\n<li>Traditional User Interviews + Discuss.io workflows create bottlenecks with separate vendors, data handoffs, and fragmented quality controls that stretch projects to 4\u20136 weeks or longer.<\/li>\n<li>Listen Labs delivers end-to-end AI research in under 24 hours by combining AI-assisted study design, global panel recruitment, AI-moderated interviews, and automated analysis and deliverables.<\/li>\n<li>The platform breaks the usual depth-versus-scale trade-off, supporting 50\u2013300+ participant studies with Emotional Intelligence analysis of tone, micro-expressions, and verbatim insights that human-moderated sessions cannot match at scale.<\/li>\n<li>Built-in Quality Guard, Listen Atlas behavioral matching, and SOC 2\/GDPR\/ISO certifications reduce fraud risk and close compliance gaps that appear when teams manage two separate vendors.<\/li>\n<li>Enterprise teams evaluating research platforms should <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">see a Listen Labs demo<\/a> to experience sub-24-hour research cycles that replace fragmented stacks with a single integrated platform.<\/li>\n<\/ul>\n<h2>Research Speed: From 4\u20136 Weeks to Less Than 24 Hours<\/h2>\n<p>Traditional User Interviews + Discuss.io projects move slowly because every step happens in sequence. <a href=\"https:\/\/driveresearch.com\/market-research-company-blog\/what-are-idis-in-depth-interviews-market-research\" target=\"_blank\" rel=\"noindex nofollow\">Qualitative recruiting alone can take a week for general consumers and several weeks for hard-to-reach audiences<\/a>, and that clock does not start until the study guide is finalized. These recruiting delays compound in <a href=\"https:\/\/cleverx.com\/blog\/mixed-methods-research-complete-guide-to-integrating-qualitative-and-quantitative-methods\" target=\"_blank\" rel=\"noindex nofollow\">sequential mixed-methods designs that typically require 2\u20134 months end-to-end, with each phase adding 4\u20138 weeks<\/a>. Even simpler IDI-only projects carry significant overhead: <a href=\"https:\/\/skimle.com\/blog\/qualitative-research-sample-size\" target=\"_blank\" rel=\"noindex nofollow\">each interview can require 1\u20132 hours of preparation, 60 minutes of fieldwork, and 1\u20133 hours of post-interview analysis, adding up to roughly 200 total hours of work across a standard project<\/a>.<\/p>\n<p>Listen Labs compresses this entire sequence into a parallel workflow. AI-assisted study design drafts objectives and questions from a natural-language brief in seconds. Listen Atlas, the platform&#8217;s global panel orchestration layer, recruits from 30M verified respondents across 45+ countries at the same time. AI-moderated interviews run in parallel instead of one by one. The Research Agent then processes all responses and generates deliverables automatically.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098461736-796a7724447a.png\" alt=\"Screenshot of researcher creating a study by simply typing &quot;I want to interview Gen Z on how they use ChatGPT&quot;\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Our AI helps you go from idea to implemented discussion guide in seconds.<\/em><\/figcaption><\/figure>\n<p>The result is a cycle measured in hours, not weeks, which becomes critical when sample sizes or stakeholder expectations are high. When Microsoft needed to collect global customer video stories for its 50th anniversary celebration, the team gathered and delivered those stories within a single day, reaching hundreds of users at roughly one third of the usual cost. This speed advantage is available to any enterprise team that replaces sequential workflows with parallel execution on a single platform.<\/p>\n<p>Ready to see what a sub-24-hour research cycle looks like for your team? <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">See Listen Labs in action<\/a>.<\/p>\n<h2>Depth Versus Scale: Qualitative Insight With 50\u2013300+ Participants<\/h2>\n<p><a href=\"https:\/\/driveresearch.com\/market-research-company-blog\/what-are-idis-in-depth-interviews-market-research\" target=\"_blank\" rel=\"noindex nofollow\">Market research firms typically recommend 8\u201314 in-depth interviews per study<\/a>, a ceiling driven by moderator availability, scheduling logistics, and analysis bandwidth rather than methodology. Human-moderated live sessions on platforms like Discuss.io are inherently sequential, with one moderator, one participant, and one time slot. That structure makes scaling beyond 15\u201320 participants in a reasonable timeframe operationally impractical for most enterprise teams.<\/p>\n<p>This operational ceiling has led the industry to rethink what qualitative research can achieve at scale. <a href=\"https:\/\/kantar.com\/north-america\/inspiration\/agile-market-research\/ai-in-qualitative-research-5-essential-practices-for-quality-at-scale\" target=\"_blank\" rel=\"noindex nofollow\">Kantar defines &#8220;Qual at Scale&#8221; as the ability to engage 50, 100, or even hundreds of participants simultaneously while preserving qualitative depth<\/a>. Listen Labs delivers this through AI-moderated interviews that run in parallel, each with dynamic follow-up questions that probe short or interesting answers the same way a trained human interviewer would.<\/p>\n<p>The platform&#8217;s Emotional Intelligence layer adds another dimension of depth by <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">analyzing tone of voice, word choice, and subconscious micro expressions<\/a>. These signals never appear in a transcript from any moderation platform, human or AI, so traditional workflows lose them entirely.<\/p>\n<p>Anthropic used this model to understand why Claude users were canceling subscriptions. Listen Labs completed 300+ user interviews in 48 hours, surfaced churn drivers five times faster than traditional methods, and delivered a prioritized list of ten must-fix items. That combination of depth and sample size is structurally unavailable to a human-moderated live-session workflow on the same timeline.<\/p>\n<h2>Participant Quality and Fraud Prevention Across the Full Funnel<\/h2>\n<p>Commodity panels introduce familiar risks: professional survey-takers, incentive-driven responses, and mismatched profiles that inflate incidence rates. <a href=\"https:\/\/tremendous.com\/blog\/academic-research-method-trends\" target=\"_blank\" rel=\"noindex nofollow\">AI tools can accelerate fraud prevention workflows during recruiting, fielding, and analysis<\/a>, but they only work when quality controls sit inside the platform architecture instead of as a post-field cleanup step.<\/p>\n<p>When User Interviews and Discuss.io operate as separate vendors, quality assurance responsibilities split across systems. Recruitment quality is managed in one tool, interview behavior is observed in another, and neither system sees the other&#8217;s signals. Listen Labs replaces this fragmentation with three integrated fraud prevention layers that work together as a single system.<\/p>\n<p>First, Listen Atlas uses behavioral and intent matching rather than self-reported demographics to identify the right participants, filtering out mismatched profiles before interviews begin. Second, Quality Guard monitors every interview in real time across video, voice, content, and device signals, catching fraudulent behavior, AI-generated scripts, and low-effort responses as they occur. Third, participants are capped at three studies per month, which prevents professional survey-takers from gaming incentives over time.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098685817-eaceb6089d9a.png\" alt=\"Listen Labs finds participants and helps build screener questions\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs finds participants and helps build screener questions<\/em><\/figcaption><\/figure>\n<p>A dedicated recruitment ops team adds a human review layer for audiences below 1% incidence rate, including enterprise decision-makers, healthcare workers, and highly specialized consumer segments across 45+ countries and 100+ languages. Together, these controls create a continuous quality gate from recruitment through fieldwork, rather than a patchwork of disconnected checks.<\/p>\n<h2>Moderation Approach: Live Human Calls Versus AI-Adaptive Interviews<\/h2>\n<p>Discuss.io&#8217;s live moderation model depends on scheduling alignment between a trained moderator and each participant. No-show rates introduce sample bias, and moderator availability limits how many sessions can run per day. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">Traditional moderated sessions often cost $4,000\u2013$12,000 per 90-minute session and take 3\u20135 weeks to complete<\/a>, and live moderation adds the risk of interviewer bias, where moderator tone, phrasing, or reaction shapes participant responses.<\/p>\n<p>Listen Labs conducts AI-led video interviews that run asynchronously, which removes scheduling dependencies and no-show bias. The AI probes intelligently on short or unexpected answers, supports screen sharing for usability tasks, and operates in 100+ languages with automatic translation and transcription. The Emotional Intelligence layer, <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">built on Ekman&#8217;s universal emotions framework<\/a>, quantifies emotions per question and concept, with every label traceable to the exact timestamp, verbatim quote, and reasoning behind it. No live moderation platform currently offers this level of emotional signal tracking at scale.<\/p>\n<h2>Analysis Effort, Bias, and Deliverable Generation With Research Agent<\/h2>\n<p>Manual coding of qualitative transcripts takes significant time and introduces confirmation bias, where analysts unconsciously favor findings that match existing hypotheses. <a href=\"https:\/\/skimle.com\/blog\/qualitative-research-sample-size\" target=\"_blank\" rel=\"noindex nofollow\">AI-assisted analysis tools can reduce per-interview analysis time by 70\u201380%<\/a>, but only when analysis is integrated with the interview platform instead of applied to exported transcripts in a separate tool.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Research Agent handles the full analysis workflow from raw data to final output<\/a>. Capabilities include automated theme extraction, natural-language queries, statistical significance testing, segmentation breakdowns, and one-click generation of branded slide decks, memo-style reports, and video highlight reels. <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Every insight links back to the underlying response data<\/a>, which keeps findings auditable instead of opaque.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098910279-d16bc544a32e.png\" alt=\"Listen Labs auto-generates research reports in under a minute\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs auto-generates research reports in under a minute<\/em><\/figcaption><\/figure>\n<p>This combination of automation and auditability enables sample sizes and turnaround speeds that manual analysis cannot match. P&amp;G used this capability to evaluate how men respond to new product claims, delivering 250+ interviews with quantified themes and verbatim proof in hours, a timeline that would require weeks of manual coding in a traditional workflow. Skims used it to validate campaign direction with thousands of high-income buyers overnight, generating qualitative clarity that secured board-level buy-in.<\/p>\n<p>See how Research Agent turns hundreds of interviews into stakeholder-ready deliverables in under a minute. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Watch Research Agent in action<\/a>.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773099063654-7132de546a42.png\" alt=\"Listen Labs&apos; Research Agent quickly generates consultant-quality PowerPoint slide decks\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs&#039; Research Agent quickly generates consultant-quality PowerPoint slide decks<\/em><\/figcaption><\/figure>\n<h2>Global Reach, Compliance, and Operational Load for Enterprise Teams<\/h2>\n<p><a href=\"https:\/\/fuelcycle.com\/ebook\/2026-market-research-insights-trends-report\" target=\"_blank\" rel=\"noindex nofollow\">Leading insight teams are shifting from fragmented research operations to intelligent insight systems that reduce handoffs between teams<\/a>. A two-vendor stack increases compliance surface area at every handoff. Data transferred between a recruitment platform and a moderation platform must satisfy the security requirements of both systems, and any gap in that chain creates enterprise risk.<\/p>\n<p>Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, with 256-bit encryption and a policy of never using customer data for AI model training. Mission Control serves as a cross-study repository where every completed study contributes to an organizational knowledge base. Researchers can run natural-language queries across past research in seconds.<\/p>\n<p><a href=\"https:\/\/fuelcycle.com\/ebook\/2026-market-research-insights-trends-report\" target=\"_blank\" rel=\"noindex nofollow\">Organizations that can remember their insights move twice as fast as those that have to relearn them<\/a>. A fragmented two-vendor stack cannot provide that level of institutional memory because data and context remain scattered across tools.<\/p>\n<h2>Best-Fit Use Cases Across Insights, UX, and Agencies<\/h2>\n<p>Consumer insights leaders at Fortune 500 enterprises running continuous global programs gain the most from Listen Labs&#8217; combination of panel scale, multilingual moderation, and Mission Control&#8217;s longitudinal tracking. The Anthropic churn study and P&amp;G product claims evaluation both illustrate this pattern: large sample sizes, fast turnaround, and deliverables that feed directly into strategic decisions.<\/p>\n<p>UX research leads evaluating concepts or testing prototypes benefit from screen-sharing support, 50\u2013100+ participant samples instead of 5\u201310, and Emotional Intelligence&#8217;s ability to surface hesitation and friction that participants do not verbalize. Non-researcher product and marketing leaders gain from AI-assisted study design that converts a natural-language brief into a structured study guide without requiring methodology expertise.<\/p>\n<p>Agencies and consultancies, such as McKinney, fit a third profile. McKinney <a href=\"https:\/\/listenlabs.ai\/case-studies\/mckinney\" target=\"_blank\">used Listen Labs to gather feedback and complete analysis rapidly<\/a>, meeting client timelines that a traditional recruitment-plus-moderation stack could not satisfy. Together, these user types show how the same platform supports strategic insights, UX testing, and client-service work without separate tools.<\/p>\n<h2>Operational and Long-Term Considerations for Platform Adoption<\/h2>\n<p>Moving from a two-vendor workflow to a single platform requires alignment across research operations, IT security, and procurement. The compliance certifications Listen Labs holds (SOC 2, GDPR, ISO 27001\/27701\/42001) address the security review requirements that enterprise procurement teams typically raise. The platform functions as a force multiplier for existing research teams rather than a replacement. <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">With AI-moderated interviews, talking to users at scale is no longer the hard part<\/a>. Research Agent addresses the challenge of interpreting what users mean so researchers can focus on strategic interpretation.<\/p>\n<p>As noted in the analysis section, this shift frees researchers from logistics and manual coding. More broadly, it allows teams to spend time on stakeholder communication and study design refinement instead of operational tasks. For ongoing global programs, researchers now treat AI capabilities as critical when choosing vendors, and the move toward always-on research infrastructure favors platforms with built-in longitudinal tracking and cross-study querying over point solutions that require manual synthesis across studies.<\/p>\n<h2>Risks and Limitations to Watch For<\/h2>\n<p>Rigid study designs that do not allow for adaptive follow-up produce shallow data on any platform. Research programs that rely on pre-set question sequences without probing logic will underperform relative to their sample size. Manual analysis workflows introduce turnaround delays and consistency risks that compound as study volume increases.<\/p>\n<p>Hidden recruitment complexity, especially for sub-1% incidence audiences, can extend timelines significantly when sourcing is handled by a general-purpose panel provider without dedicated ops support. <a href=\"https:\/\/kantar.com\/north-america\/inspiration\/agile-market-research\/ai-in-qualitative-research-5-essential-practices-for-quality-at-scale\" target=\"_blank\" rel=\"noindex nofollow\">Without a human editing layer, AI-moderated qualitative outputs can be too literal and miss emotional nuance<\/a>. Listen Labs addresses this through the Emotional Intelligence layer and the in-house research team&#8217;s continuous methodology refinement.<\/p>\n<p>Automation accelerates research output, yet overestimating what automation can deliver without human strategic oversight remains a real risk for any platform deployment. Teams still need experts to frame questions, interpret findings, and connect insights to business decisions.<\/p>\n<h2>Decision Framework for Choosing Between Stacks<\/h2>\n<p>Teams with infrequent, low-volume research needs and existing moderator relationships may find the User Interviews + Discuss.io stack adequate for isolated projects. The friction of managing two vendors, two quality frameworks, and manual analysis becomes most costly when research volume is high, timelines are short, or global reach is required.<\/p>\n<p>Teams running more than a handful of studies per quarter, operating across multiple markets, or needing to scale qualitative sample sizes beyond 15 participants will encounter structural limits in a two-vendor workflow that a single integrated platform resolves. Budget-constrained teams should weigh the combined cost of two vendor subscriptions, recruitment ops overhead, moderator fees, transcription, and manual analysis against a single platform subscription. <a href=\"https:\/\/www.forbes.com\/sites\/iainmartin\/2026\/01\/14\/this-500-million-ai-startup-runs-customer-interviews-for-microsoft-and-sweetgreen\/\" target=\"_blank\">Listen Labs has run over 1 million AI-powered customer interviews for enterprises including Microsoft, Perplexity, and Sweetgreen<\/a>, with clients like Microsoft reporting the cost and speed advantages described earlier. Teams that need emotional signal data, multilingual moderation, or cross-study institutional memory have requirements that the User Interviews + Discuss.io stack does not address.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does a typical User Interviews + Discuss.io project take end-to-end?<\/h3>\n<p>A standard IDI project using separate recruitment and live moderation tools typically runs 4\u20136 weeks from study design to final deliverables, as discussed earlier. This timeline covers study guide development, participant recruitment, scheduling coordination, sequential live moderation sessions, transcription, manual coding, and report writing. Complex multi-market or mixed-methods projects can extend to 2\u20134 months. Listen Labs compresses this sequence to less than 24 hours by running recruitment, AI-moderated interviews, analysis, and deliverable generation in parallel on a single platform.<\/p>\n<h3>Does AI moderation reduce insight depth compared with live human interviews?<\/h3>\n<p>AI moderation on Listen Labs maintains insight depth relative to live human moderation, and in several dimensions it increases depth. The AI probes short or unexpected answers dynamically, similar to a trained human interviewer, without the scheduling constraints or moderator bias that affect live sessions. The Emotional Intelligence layer adds depth that live moderation cannot provide at scale. Tone of voice, word choice, and subconscious micro expressions are analyzed per question and concept, with every emotional label traceable to the exact timestamp and verbatim quote.<\/p>\n<p>The platform&#8217;s in-house research team, with 50+ years of combined expertise, continuously refines the methodology so adaptive conversations capture nuance instead of literal, surface-level responses. This combination of adaptive probing and expert oversight keeps qualitative richness high while expanding sample size.<\/p>\n<h3>How does Listen Labs prevent the participant fraud common in separate recruitment panels?<\/h3>\n<p>Listen Labs uses three integrated layers of fraud prevention. First, Listen Atlas matches participants on behavioral and intent data rather than self-reported demographics, filtering out profiles that do not match the target audience before an interview begins. Second, Quality Guard monitors every interview in real time across video, voice, content, and device signals, detecting fraudulent responses, AI-generated scripts, low-effort answers, and mismatched profiles as they occur.<\/p>\n<p>Third, participants are capped at three studies per month, which eliminates professional survey-takers who optimize for incentives. A dedicated recruitment ops team adds a human review layer for hard-to-reach segments. This architecture differs from a commodity panel provider, where fraud detection usually appears as a post-field reconciliation step instead of a real-time control.<\/p>\n<h3>Can Listen Labs replace an existing research team or only augment it?<\/h3>\n<p>Listen Labs is designed as a force multiplier for existing research teams, not a replacement. The platform handles the logistical and operational layers of research, including recruitment, scheduling, moderation, transcription, coding, and deliverable generation. As noted earlier, this frees researchers to focus on strategic interpretation, stakeholder communication, and study design refinement.<\/p>\n<p>Teams that previously ran 10\u201315 studies per year due to bandwidth constraints can run significantly more with the same headcount. The Research Agent generates consultant-quality slide decks, highlight reels, and reports automatically, while strategic judgment about what questions to ask and how to act on insights remains with the research team.<\/p>\n<h3>What multilingual and compliance capabilities support global enterprise programs?<\/h3>\n<p>Listen Labs supports interview moderation in 100+ languages with automatic translation and transcription, and the Emotional Intelligence layer operates across 50+ languages. The global panel covers 45+ countries across the Americas, Europe, APAC, and MEA, with dedicated recruitment ops for hard-to-reach audiences in any market.<\/p>\n<p>On the compliance side, the platform holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, with 256-bit encryption and a policy of never using customer data for AI model training. Enterprise SSO is supported. These certifications address the security and privacy requirements that global enterprise procurement and legal teams typically require before approving a new research platform.<\/p>\n<h2>Conclusion: Replacing Fragmented Stacks With a Single AI Research System<\/h2>\n<p>Across every evaluation criterion, including research speed, depth at scale, participant quality, moderation approach, analysis rigor, deliverable generation, global reach, security, and operational burden, the User Interviews + Discuss.io stack introduces fragmentation that a single integrated platform resolves. The combined workflow requires managing two vendor relationships, two quality frameworks, sequential execution, and manual synthesis steps that add weeks to every project cycle.<\/p>\n<p>Listen Labs replaces both tools with one end-to-end platform that sources participants from a 30M verified global panel, conducts AI-moderated adaptive interviews with Emotional Intelligence, automates analysis through Research Agent, and stores institutional knowledge in Mission Control. The platform delivers results in less than 24 hours at enterprise scale. Microsoft, Anthropic, P&amp;G, Skims, and Robinhood have each validated this architecture for high-stakes research decisions.<\/p>\n<p>Teams evaluating whether a single integrated platform can replace their current stack can review the evidence directly. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Schedule a full-platform walkthrough<\/a> to see Listen Labs end-to-end.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Skip the two-vendor headache. Listen Labs replaces User Interviews + Discuss.io with end-to-end AI research in under 24 hours. Start today.<\/p>\n","protected":false},"author":52,"featured_media":750,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-751","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/751","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/users\/52"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/comments?post=751"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/751\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/750"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=751"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=751"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=751"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}