{"id":815,"date":"2026-06-03T05:45:16","date_gmt":"2026-06-03T05:45:16","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/ai-qualitative-research-tools-2026\/"},"modified":"2026-06-03T05:45:16","modified_gmt":"2026-06-03T05:45:16","slug":"ai-qualitative-research-tools-2026","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/ai-qualitative-research-tools-2026\/","title":{"rendered":"AI Qualitative Research Tools: End-to-End vs. Analysis-Only"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2>Key Takeaways for Enterprise Research Leaders<\/h2>\n<ul>\n<li>\n<p>Enterprise teams choose between end-to-end AI platforms, analysis-only tools, and traditional agencies, and each path creates different outcomes for speed, insight depth, and operational burden.<\/p>\n<\/li>\n<li>\n<p>End-to-end platforms like Listen Labs are the only category that performs competitively across all nine enterprise evaluation criteria at the same time.<\/p>\n<\/li>\n<li>\n<p>Listen Labs compresses the full research lifecycle, from design through deliverables, into under 24 hours while maintaining SOC 2 Type II, GDPR, and ISO certifications.<\/p>\n<\/li>\n<li>\n<p>AI-moderated interviews with Emotional Intelligence capture tone, word choice, and micro-expressions, surfacing motivations and emotions that transcripts alone miss.<\/p>\n<\/li>\n<li>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\">Listen Labs replaces fragmented<\/a> workflows with a single compliant platform that delivers consultant-quality insights in hours rather than weeks.<\/p>\n<\/li>\n<\/ul>\n<h2>How This Comparison Frames the Research Choices<\/h2>\n<p>Three distinct categories compete for enterprise research budgets in 2026. End-to-end AI platforms handle study design, recruitment, moderation, analysis, and delivery within one system. Analysis-only tools such as Dovetail, ATLAS.ti, and NVivo organize and code research that has already been conducted elsewhere, and they do not recruit participants or conduct interviews. Traditional research agencies and consultancies provide full-service execution but rely on human moderators, manual analysis, and multi-vendor coordination.<\/p>\n<p>All three categories belong in the same evaluation because enterprises are not simply buying software, they are buying a research workflow. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/getperspective.ai\/blog\/the-future-of-focus-groups-with-ai-7-trends-reshaping-qualitative-research-in-2026\">Operating data from AI-native platforms shows that traditional qualitative synthesis often takes several weeks<\/a>, while the same depth of analysis on an AI-native platform can complete in a few hours. That compression does not come from layering an analysis tool onto a traditional recruitment and moderation workflow. It requires rethinking the entire pipeline. As the market shifts from project-based research cycles to continuous, qual-at-scale programs, end-to-end architecture becomes the only viable foundation for always-on customer intelligence.<\/p>\n<h2>Enterprise Evaluation Criteria for AI Qualitative Platforms<\/h2>\n<p>Nine criteria determine fit for enterprise use cases, and they work together as a single decision framework. Research speed measures the elapsed time from study brief to final deliverable and sets the pace for decision-making in fast-moving markets. Insight depth captures whether the platform surfaces motivations, emotions, and unexpected findings or only surface-level responses that fail to change strategy. Participant quality and sourcing cover verification rigor, fraud prevention, and access to niche segments, which directly shape whether insights reflect real users or professional survey-takers.<\/p>\n<p>Methodological flexibility addresses the range of study types supported natively, including IDIs, usability tests, concept tests, and diary studies, so teams can standardize on one platform instead of juggling point solutions. Global and language reach determine whether a single platform can execute multi-market programs without local vendor dependencies, which affects both cost and consistency. Analysis effort quantifies the human hours required to move from raw data to actionable findings, influencing team capacity planning and headcount needs.<\/p>\n<p>Reporting transparency establishes whether every insight is traceable to a specific response, timestamp, and reasoning chain, which matters for stakeholder trust and auditability. Governance and security cover certifications and data handling practices that satisfy IT, legal, and compliance stakeholders. Scalability measures whether the platform can run hundreds of simultaneous interviews without quality degradation. Total operational burden aggregates vendor management, scheduling, and coordination overhead, revealing how much time the research team spends running studies instead of interpreting results.<\/p>\n<p>Applying these nine criteria consistently across categories shows clear patterns. Analysis-only tools perform well on analysis effort and reporting transparency, but only for data that has already been collected, and they contribute nothing to speed, sourcing, moderation, or scalability. Traditional agencies perform well on insight depth and methodological flexibility but perform poorly on speed, cost, and scalability. End-to-end AI platforms are the only category that performs competitively across all nine dimensions at once.<\/p>\n<h2>Study Setup and Recruitment with End-to-End Platforms<\/h2>\n<p>Study design in traditional workflows involves briefing a research agency, iterating on a discussion guide over several days, and then waiting for a recruitment vendor to source participants. This process <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\">typically requires three to five weeks and costs $4,000\u2013$12,000 per 90-minute focus group session<\/a>. Analysis-only tools have no role in this phase, since they receive data only after recruitment and moderation are complete. This fragmentation is exactly what end-to-end platforms remove by unifying design, sourcing, and scheduling.<\/p>\n<p>Listen Labs compresses study setup to minutes by integrating these steps into a single workflow. AI-assisted co-design translates a natural-language research brief into structured objectives, questions, and probing context. Listen Atlas, the platform&#8217;s AI orchestration layer, then matches and recruits participants across a 30M+ verified network spanning 45+ countries, automatically bidding across multiple consumer and B2B panel partners. For hard-to-reach segments such as enterprise decision-makers, healthcare workers, and audiences below a 1% incidence rate, a dedicated recruitment operations team adds a human sourcing layer that commodity panels cannot replicate.<\/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>Fraud prevention functions as a structural differentiator in this setup. Quality Guard monitors every interview in real time across video, voice, content, and device signals, blocking fraudulent responses, AI-generated scripts, and mismatched profiles before they contaminate the dataset. Participants are capped at three studies per month, which removes the professional survey-taker problem that undermines commodity panel quality. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/coworker.ai\/blog\/best-ai-tools-for-enterprise-with-secure-data\">Enterprise IT leaders treat SOC 2 Type II and ISO 27001 compliance as mandatory rather than optional<\/a> when selecting AI platforms, and Listen Labs holds both certifications alongside GDPR, ISO 27701, and ISO 42001.<\/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<h2>Moderation Quality and Emotional Depth of Insights<\/h2>\n<p>The moderation layer is where end-to-end platforms and analysis-only tools diverge most sharply. Analysis-only tools receive transcripts or recordings and apply coding, theming, and synthesis, and they have no influence over how the interview was conducted or what follow-up questions were asked. The quality of their output is therefore bounded by the quality of the input data.<\/p>\n<p>Listen Labs&#8217; AI-moderated interviews conduct personalized, adaptive conversations with dynamic follow-up questions calibrated to each participant&#8217;s responses, which directly expands that input quality. The platform captures video, audio, text, and screen recordings simultaneously. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/ai-moderation-improves-comfort-and-honesty\">A study comparing AI and human moderation found that 92% of participants reported top comfort levels in both formats, and 32% explicitly stated they feel less judged with AI moderation<\/a>. That finding has direct implications for sensitive research topics where social desirability bias suppresses honest responses.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\">Listen Labs&#8217; Emotional Intelligence feature analyzes three simultaneous signal layers, tone of voice, word choice, and subconscious micro expressions, to surface emotions that transcripts alone miss<\/a>. The system is <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\">built on Ekman\u2019s universal emotions framework<\/a>, the same standard used in clinical psychology and UX research. Every emotional label is traceable to the exact timestamp, verbatim quote, and AI reasoning that produced it. This multimodal approach is unavailable in analysis-only tools, which process text or pre-recorded video without real-time adaptive moderation.<\/p>\n<h2>Mixed-Methods Analysis, Knowledge Reuse, and Deliverables<\/h2>\n<p>Mixed-methods capability separates mature platforms from point solutions. Listen Labs combines qualitative interview questions with quantitative formats such as Likert scales, NPS, MaxDiff, sliders, and grids within a single study, which removes the need for a separate survey tool and a separate analysis workflow.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/research-agent\">The Research Agent handles the full analysis workflow from raw data to final output<\/a>, processing all interview data to identify patterns, themes, and insights across hundreds of responses without human confirmation bias. Researchers can query findings in natural language, generate statistical significance tests, build segmentation breakdowns, and produce <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/research-agent\">branded slide decks and downloadable reports in under a minute<\/a>. Video highlight reels automatically clip the most emotionally significant or thematically relevant moments.<\/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>Mission Control functions as the organization&#8217;s institutional knowledge layer. It enables cross-study queries and trend tracking so teams can answer questions from past research in seconds rather than re-running studies. Analysis-only tools offer repository functionality, but without the recruitment, moderation, and emotional intelligence layers, their repositories contain only what external workflows have produced. That limitation creates a structural ceiling on the depth and recency of available knowledge.<\/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&#8217; Research Agent quickly generates consultant-quality PowerPoint slide decks<\/em><\/figcaption><\/figure>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\">See the Research Agent generate a consultant-quality deliverable from live interview data in a live demo.<\/a><\/p>\n<h2>Where End-to-End Platforms Fit Best for Enterprise Teams<\/h2>\n<p>Consumer insights leaders at Fortune 500 companies face research backlogs that grow faster than team capacity. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/www.forbes.com\/sites\/iainmartin\/2026\/01\/14\/this-500-million-ai-startup-runs-customer-interviews-for-microsoft-and-sweetgreen\">Listen Labs has run over one million AI-powered customer interviews for companies including Microsoft, Perplexity, and Sweetgreen<\/a>. Microsoft used the platform to collect global customer stories for its 50th anniversary within a single day. A Director of Data Science at Microsoft highlighted the ability to reach hundreds of users at one-third of the cost of traditional methods. P&amp;G used Listen Labs to evaluate how men respond to new product claims, delivering 250+ interviews with quantified themes in hours rather than weeks and shaping product and brand strategy before market launch.<\/p>\n<p>UX research leads at mid-to-large tech companies benefit from screen-sharing and usability testing capabilities that support testing with 50\u2013100+ users per sprint cycle instead of the five to ten participants typical of manually scheduled sessions. Robinhood used Listen Labs to assess whether prediction markets feel on-brand, revealing that users who view the product as entertainment rather than income drive 2.4x higher weekly re-engagement. That insight informed integration flows that boosted uptake by 30\u201340%.<\/p>\n<p>Product managers and marketing leaders without dedicated research teams can describe goals in natural language and receive a complete study design, recruited participants, moderated interviews, and analysis without research methodology expertise. Skims validated a global campaign direction with thousands of high-income buyers overnight, eliminating weeks of panel sourcing and enabling board-level buy-in before launch. Anthropic used Listen Labs to conduct 300+ user interviews in 48 hours to surface Claude subscription churn drivers five times faster than previous methods.<\/p>\n<p>Consultancies and agencies operating on client timelines measured in days rather than weeks use Listen Labs to reach niche audiences such as enterprise decision-makers, engineers, and healthcare workers that general panels cannot reliably source.<\/p>\n<h2>Operational, Compliance, and Long-Term Platform Fit<\/h2>\n<p>Deploying any new research infrastructure requires stakeholder alignment across research, IT, legal, and procurement. Listen Labs maintains the full certification stack mentioned earlier, with customer data encrypted at 256-bit and never used for AI model training, which satisfies data sovereignty requirements in regulated industries.<\/p>\n<h2>Risks, Limitations, and Misconceptions to Watch<\/h2>\n<p>Shallow data risk becomes real when study design is weak. AI moderation amplifies the quality of a well-constructed discussion guide and compounds the weaknesses of a poorly constructed one. Listen Labs mitigates this through AI-assisted co-design and Auto-QA that flags issues before launch, but research teams should still treat study design as a strategic input, not a formality.<\/p>\n<p>Hidden recruitment complexity often surprises teams switching from analysis-only tools. Sourcing verified participants for niche segments does not get solved by software alone. It requires a combination of AI orchestration, panel partnerships, and human recruitment operations, which Listen Labs provides through Listen Atlas and its dedicated operations team.<\/p>\n<p>Fraud exposure remains a persistent risk on commodity panels. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/tremendous.com\/blog\/academic-research-method-trends\">AI can accelerate fraud prevention workflows during recruiting, fielding, and analysis<\/a>, but only platforms with real-time multimodal monitoring, not post-hoc text screening, can catch sophisticated fraud patterns before they enter the dataset.<\/p>\n<p>The assumption that speed automatically improves quality is incorrect, because speed and quality are independent variables, and teams can have one without the other. The key lies in understanding how to achieve both at the same time. Listen Labs does this by parallelizing recruitment, moderation, and analysis rather than cutting methodological corners, which shows that independence does not mean incompatibility. Teams should evaluate platforms on the rigor of their quality controls, not on turnaround time alone.<\/p>\n<h2>Decision Framework: Matching Tools to Enterprise Goals<\/h2>\n<p>The selection decision reduces to a single core test. A platform either covers the full research lifecycle or requires external dependencies that reintroduce the delays, costs, and quality risks the platform was meant to remove.<\/p>\n<p>Analysis-only tools work as supplementary repositories for teams that have already invested in separate recruitment and moderation infrastructure and need to organize historical findings. They do not work as primary research platforms for teams that need to generate new insights quickly.<\/p>\n<p>Traditional agencies remain viable for highly specialized research contexts such as complex medical discussions, ethnographic fieldwork, or studies requiring deep cultural interpretation where human moderator judgment is genuinely irreplaceable. For the vast majority of enterprise research needs, the speed, cost, and scalability penalties are prohibitive.<\/p>\n<p>End-to-end AI platforms are the right choice for any team that needs to run more research with the same or smaller headcount, reach verified global audiences without managing multiple vendors, capture emotional and behavioral signals beyond what transcripts reveal, and deliver findings to stakeholders in hours rather than weeks. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\">The depth-vs-scale trade-off that defined qualitative research for decades is no longer a structural constraint<\/a>. It has become a platform selection problem, and Listen Labs is the platform that removes it.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How quickly can AI qualitative research tools deliver results compared with traditional timelines?<\/h3>\n<p>Traditional qualitative research cycles run four to six weeks from study design to final report, and in large enterprises with internal prioritization backlogs, the process can extend to six months. AI-native platforms compress this dramatically. Listen Labs delivers the complete research lifecycle, including study design, participant recruitment, AI-moderated interviews, analysis, and deliverables, in under 24 hours. The compression comes from parallelizing steps that are sequential in traditional workflows. Recruitment runs concurrently with study finalization, interviews run simultaneously across hundreds of participants, and analysis begins as soon as the first interview completes. For teams running continuous research programs rather than one-off projects, this speed difference compounds into a structural competitive advantage.<\/p>\n<h3>Where do end-to-end platforms source verified participants and maintain sample quality?<\/h3>\n<p>Listen Labs sources participants through Listen Atlas, an AI orchestration layer that matches and recruits across a 30M+ verified respondent network spanning 45+ countries. The system bids across multiple consumer and B2B panel partners alongside Listen Labs&#8217; proprietary database, selecting participants based on behavioral and intent data rather than self-reported demographics alone. Quality Guard monitors every interview in real time across video, voice, content, and device signals to detect fraud, low-effort responses, AI-generated scripts, and mismatched profiles. Participants are limited to three studies per month, which eliminates the professional survey-taker problem endemic to commodity panels. For hard-to-reach segments such as enterprise decision-makers, healthcare workers, and audiences below a 1% incidence rate, a dedicated recruitment operations team handles sourcing through niche communities, micro-creators, and specialized networks.<\/p>\n<h3>What security and compliance standards should enterprises require?<\/h3>\n<p>Enterprise procurement teams should require, at minimum, SOC 2 Type II certification, GDPR compliance, and ISO 27001 certification as baseline standards. SOC 2 Type II demonstrates ongoing operational security controls through independent audits, not just a point-in-time assessment. ISO 27001 establishes a systematic information security management framework. For AI-specific governance, ISO 42001 addresses AI management systems and is increasingly relevant as enterprises face regulatory scrutiny of AI-generated research outputs. ISO 27701 extends privacy information management to cover personal data processing. Listen Labs holds all five certifications and encrypts customer data at 256-bit. Customer data is never used for AI model training, which is a critical data sovereignty requirement for enterprises in regulated industries.<\/p>\n<h3>How do AI-moderated interviews differ from analysis-only tools in depth and effort?<\/h3>\n<p>Analysis-only tools receive pre-existing data and apply coding, theming, and synthesis. They have no influence over how interviews were conducted, what follow-up questions were asked, or whether participants gave candid responses, so the depth of their output is bounded by the depth of the input. AI-moderated interviews, by contrast, conduct adaptive conversations in real time, probing short answers, following unexpected threads, and adjusting question framing based on each participant&#8217;s responses. Listen Labs adds a further layer through Emotional Intelligence, which analyzes tone of voice, word choice, and micro expressions simultaneously to capture what participants feel, not only what they say. Every emotional signal is traceable to a specific timestamp and verbatim quote. The combined effect is that end-to-end platforms generate richer raw data and then analyze it more thoroughly, while analysis-only tools can only improve the analysis of whatever data arrives from external sources.<\/p>\n<h3>Can these platforms scale multilingual research while preserving emotional nuance?<\/h3>\n<p>Listen Labs supports 100+ languages for interview moderation with automatic translation and transcription across all supported languages. Emotional Intelligence is available across 50+ languages, applying the same Ekman-framework multimodal signal analysis regardless of the language being spoken. This capability matters because emotional expression has both universal and culturally specific dimensions. A platform that translates text but does not analyze vocal tone and micro expressions in the original language loses the emotional signal that makes qualitative research valuable. For multi-market programs, Listen Labs&#8217; 45+ country coverage and dedicated recruitment operations team remove the need for local panel vendors in each market, consolidating global research into a single workflow with consistent quality controls and a unified analysis layer.<\/p>\n<h2>Conclusion: Choosing the Platform That Removes the Trade-Off<\/h2>\n<p>The choice between end-to-end AI platforms, analysis-only tools, and traditional methods determines whether the research infrastructure can keep pace with the speed of business decisions. Analysis-only tools solve one step of a multi-step problem. Traditional agencies solve the full problem but at a speed and cost that make continuous research programs operationally impossible for most teams.<\/p>\n<p>Listen Labs is the end-to-end solution that collapses the research cycle from weeks to hours without sacrificing methodological rigor. A 30M+ verified global panel, AI-moderated interviews with multimodal Emotional Intelligence, bias-reduced automated analysis, and one-click consultant-quality deliverables operate within a single enterprise-certified platform. Enterprises including Microsoft, Google, Anthropic, P&amp;G, Skims, and Robinhood have replaced fragmented research workflows with Listen Labs and measured the results in hours, not weeks.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\">Schedule a walkthrough to see how Listen Labs delivers the depth, scale, and speed your research program requires.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Compare top AI qualitative research tools for 2026. Listen Labs delivers end-to-end research in under 24 hours with SOC 2, GDPR &amp; ISO compliance.<\/p>\n","protected":false},"author":52,"featured_media":814,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-815","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\/815","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=815"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/815\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/814"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=815"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=815"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}