{"id":447,"date":"2026-04-07T05:08:59","date_gmt":"2026-04-07T05:08:59","guid":{"rendered":"https:\/\/blog.listenlabs.ai\/best-enterprise-user-research-platforms\/"},"modified":"2026-07-04T05:30:36","modified_gmt":"2026-07-04T05:30:36","slug":"best-enterprise-user-research-platforms","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/best-enterprise-user-research-platforms\/","title":{"rendered":"Best Product Testing Platforms for Enterprise User Research"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs | Last updated: June 26, 2026<\/em><\/p>\n<h2>Key Takeaways for Enterprise Research Leaders<\/h2>\n<ul>\n<li>\n<p>Enterprise research teams need platforms that deliver qualitative depth at quantitative scale with verified participants and sub-24-hour turnaround.<\/p>\n<\/li>\n<li>\n<p>Listen Labs outperforms legacy human-moderated tools, panel-only solutions, and quantitative survey platforms across eight critical evaluation criteria.<\/p>\n<\/li>\n<li>\n<p>AI-moderated interviews with adaptive follow-ups, emotional signal capture, and automated analysis remove the traditional trade-off between speed and insight quality.<\/p>\n<\/li>\n<li>\n<p>Listen Labs combines a 30-million-respondent verified global panel, SOC 2 and ISO certifications, and one-click consultant-quality deliverables in a single enterprise workflow.<\/p>\n<\/li>\n<li>\n<p>See a live run-through of AI interviews and instant deliverables in a <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\">Listen Labs demo tailored to your research<\/a> use cases.<\/p>\n<\/li>\n<\/ul>\n<h2>Eight Criteria That Define Enterprise-Ready Product Testing Platforms<\/h2>\n<p>Enterprise research teams consistently evaluate product testing platforms on eight core criteria that determine whether a tool accelerates decisions or slows them down. These factors reflect procurement requirements at large organizations and the realities of running qualitative research at scale.<\/p>\n<p>Research speed measures the elapsed time from study brief to final deliverable, which determines whether insights arrive before or after a business decision. Qualitative depth at scale assesses whether a platform can run adaptive, conversational interviews with hundreds or thousands of participants at once, or whether teams must choose between sample size and richness.<\/p>\n<p>Participant quality covers fraud prevention, panel composition, and behavioral verification. Global reach encompasses the number of countries, languages, and audience segments that teams can access without custom recruitment operations.<\/p>\n<p>Enterprise security includes certifications such as SOC 2, GDPR, ISO 27001, ISO 27701, and ISO 42001. Analysis automation measures how much manual effort is required to move from raw interview data to findings that stakeholders can act on.<\/p>\n<p>Emotional signal capture evaluates whether a platform surfaces what participants feel, not just what they say. Total cost of ownership accounts for platform fees, per-participant costs, and the hidden expense of stitching together multiple disconnected vendors.<\/p>\n<h2>Study Design Capabilities Across Leading Platforms<\/h2>\n<p>Study design determines whether a platform can translate business questions into reliable research quickly. Legacy human-moderated platforms such as UserTesting rely on researchers manually constructing discussion guides, then scheduling and briefing individual moderators. This process introduces inconsistency across sessions and adds days to the pre-field timeline.<\/p>\n<p>Quantitative survey tools like SurveyMonkey and Qualtrics offer template libraries but restrict teams to pre-set question formats with no adaptive logic at the conversation level. Panel-only platforms such as Prolific and User Interviews handle recruitment but leave study design entirely to the researcher.<\/p>\n<p>Listen Labs approaches study design through AI-assisted co-design. Researchers describe their objectives in natural language and the platform drafts structured questions, probing context, and branching logic in seconds. The platform supports free-flowing in-depth interviews, semi-structured formats, diary studies, ethnographic approaches, and task-based UX testing.<\/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>Advanced stimuli such as images, video, audio, PDFs, live URLs, and prototypes can be embedded directly. Monadic and sequential randomization, quotas, skip logic, piping, and version control are all native. An auto-QA layer flags issues in the study guide before launch. The platform operates across <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\">100+ languages<\/a>, which enables multi-market studies without separate localization workflows.<\/p>\n<h2>Participant Sourcing and Global Reach for Enterprise Studies<\/h2>\n<p>Study design capabilities only create value when teams can reach the right participants at the right scale. Panel-only platforms solve the sourcing problem in isolation but do not connect recruitment to moderation or analysis. Traditional research agencies maintain proprietary panels that are often limited in geographic breadth and rely on manual recruitment operations that add weeks to the timeline.<\/p>\n<p>Commodity quantitative panels carry well-documented risks, including professional survey-takers, incentive-driven responses, and fraudulent profiles that undermine data integrity. These issues become more severe as sample sizes grow.<\/p>\n<p>Listen Labs operates Listen Atlas, a global panel of 30 million verified respondents across 45+ countries. An AI orchestration layer automatically matches and bids across multiple consumer and B2B panel partners, including specialized networks like NewtonX, alongside Listen Labs\u2019 proprietary database.<\/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>For audiences below 1% incidence rate, such as enterprise decision-makers, healthcare workers, and highly specialized consumer segments, a dedicated recruitment operations team manages sourcing through niche communities and micro-creator networks. Organizations can also self-recruit from their own user base at reduced cost. Listen Labs has conducted AI-powered customer interviews for companies including <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\/\">Microsoft, Perplexity, and Sweetgreen<\/a>, which demonstrates the operational scale of this infrastructure.<\/p>\n<h2>Moderation Approach: Human-Led and AI-Moderated Trade-offs<\/h2>\n<p>Moderation strategy determines whether qualitative research can keep pace with product and marketing cycles. Human moderation produces high-quality individual sessions when conducted by experienced researchers, but it does not scale. A team of five moderators running two sessions per day reaches ten participants daily, which makes large-sample qualitative research economically and logistically prohibitive.<\/p>\n<p>UserTesting\u2019s human-dependent moderation model reflects this constraint and produces slower turnaround with limited parallelism. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\">Traditional focus groups take 3\u20135 weeks and cost $4,000\u2013$12,000 per 90-minute session<\/a>, which restricts most enterprise teams to a handful of studies per quarter.<\/p>\n<p>Listen Labs\u2019 AI conducts thousands of adaptive video interviews simultaneously. Each session includes dynamic follow-up questions triggered by the participant\u2019s actual responses, which replicates the probing behavior of a trained human interviewer without scheduling overhead. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\">Switching to Listen Labs AI-moderated interviews let Chubbies capture hundreds of candid, one-to-one conversations overnight<\/a>, a result that human-only moderation cannot match.<\/p>\n<p>The platform collects video, audio, text, and screen recordings, including mobile screen recording on iOS. It supports mixed-methods designs that combine qualitative interviews with Likert scales, NPS, sliders, grids, and MaxDiff.<\/p>\n<h2>Data Quality Controls and Fraud Prevention at Scale<\/h2>\n<p>Data quality determines whether research findings can influence high-stakes decisions with confidence. Quantitative survey platforms often apply minimal fraud controls beyond basic duplicate-IP detection. Panel-only platforms vary widely, and commodity panels are particularly vulnerable to professional survey-takers and AI-generated responses. Legacy moderated platforms reduce fraud risk through human oversight but cannot monitor at scale.<\/p>\n<p>Listen Labs\u2019 Quality Guard operates across three layers. The platform works exclusively with high-quality, non-commodity panel sources, and excludes commodity panels entirely. Quality Guard then applies real-time AI monitoring across video, voice, content, and device signals to detect fraudulent responses, low-effort answers, AI-generated scripts, and mismatched profiles during the interview itself.<\/p>\n<p>A participant frequency cap of three studies per month per respondent removes the professional survey-taker dynamic. A reputation scoring system compounds across every interview conducted on the platform. As Listen Labs\u2019 client base grows, audience quality becomes stronger and more refined, creating a flywheel that panel-only competitors cannot replicate.<\/p>\n<h2>Analysis Workflow and Automation for Faster Decisions<\/h2>\n<p>High-quality participant data only creates impact when teams can analyze it quickly and consistently. Manual qualitative analysis is the primary bottleneck in traditional research cycles. A human analyst reviewing 50 interview transcripts needs days of work, and the process is vulnerable to confirmation bias, as analysts may unconsciously weight findings that align with pre-existing hypotheses.<\/p>\n<p>Analysis tools like Dovetail organize and tag research that has already been conducted elsewhere, but they do not conduct new research or automate theme extraction from raw interview data. This gap leaves teams with a heavy manual lift after fieldwork ends.<\/p>\n<p>Listen Labs\u2019 Research Agent processes all interview data automatically and identifies patterns, themes, and insights across hundreds of responses. The analysis engine is trained on proprietary data from tens of thousands of completed studies, which gives it the ability to separate signal from noise with more specificity than general-purpose LLMs.<\/p>\n<p>Researchers can query findings in natural language and receive answers, charts, statistical tests, and segmentation breakdowns in seconds. One-click deliverables, including slide decks, memos, highlight reels, and custom reports, are generated in under a minute.<\/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>Listen Labs\u2019 Emotional Intelligence feature adds a layer of analysis that survey platforms and legacy moderation tools do not provide. Built on Ekman\u2019s universal emotions framework, it analyzes tone of voice, word choice, and subconscious micro-expressions to quantify emotions such as joy, trust, surprise, fear, disgust, anticipation, sadness, and anger at the question and concept level.<\/p>\n<p>Every emotional label is traceable to the exact timestamp, verbatim quote, and the reasoning behind the classification. This capability is available across 50+ languages and connects directly with the Research Agent for natural-language queries and highlight reels of emotionally significant moments.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\">Join a live session to watch the Research Agent and Emotional Intelligence move from raw interviews to finished deliverables.<\/a><\/p>\n<h2>Reporting Capabilities and Enterprise-Ready Deliverables<\/h2>\n<p>Reporting quality shapes how easily stakeholders can absorb and act on research. Traditional research agencies produce polished reports, but the manual writing process adds one to two weeks to the post-field timeline. Survey platforms generate charts and cross-tabs but cannot produce narrative synthesis or video evidence. Legacy moderated platforms typically deliver transcripts and basic highlight clips, which leaves synthesis to the client team.<\/p>\n<p>Listen Labs delivers consultant-quality outputs automatically. The platform generates structured PowerPoint slide decks, memo-style reports, video highlight reels, statistical charts, segmentation breakdowns, and custom reports from natural-language queries. These outputs arrive minutes after fieldwork completes.<\/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>Mission Control serves as the organization\u2019s persistent knowledge base. Every study conducted on the platform grows a searchable repository that supports cross-study queries, trend tracking, and institutional knowledge retention. Teams can retrieve answers from past research in seconds without digging through archived slide decks.<\/p>\n<h2>Best-Fit Use Cases for Enterprise Teams<\/h2>\n<p>Continuous research programs at Fortune 500 consumer insights teams gain the most from Listen Labs\u2019 end-to-end infrastructure. The platform replaces a fragmented vendor stack of separate tools for recruitment, scheduling, moderation, transcription, analysis, and reporting with a single workflow. This consolidation removes handoff delays and quality loss that accumulate across disconnected systems.<\/p>\n<p>UX research groups at mid-to-large technology companies can test with 50\u2013100+ participants per study instead of the 5\u201310 that human-moderated budgets typically allow. Screen-sharing and mobile screen recording support usability testing at scale, and the sub-24-hour turnaround aligns with sprint cycles that traditional research timelines cannot accommodate.<\/p>\n<p>Product and marketing teams without dedicated research staff can describe their objectives in natural language and receive a complete study design, recruited participants, moderated interviews, and synthesized findings. This workflow removes the need for in-house research methodology expertise. Agencies and consultancies conducting due diligence or client research benefit from Listen Labs\u2019 speed and global reach, especially for niche audiences that commodity panels cannot reliably source.<\/p>\n<h2>Operational Considerations and Change Management for Adoption<\/h2>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\">Qual-at-scale is ideal when research requires large sample sizes or broad geographic reach, as AI tools can engage hundreds or thousands of participants remotely and asynchronously<\/a>, but adopting an end-to-end platform still requires alignment across research, IT, and procurement. Listen Labs supports enterprise SSO and holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, which satisfy the security and governance requirements that Fortune 500 procurement processes mandate.<\/p>\n<p>Customer data is never used for AI model training, and all data is protected with 256-bit encryption. These controls allow legal and security teams to approve the platform for sensitive workstreams.<\/p>\n<p>The platform is designed for continuous research programs rather than one-off projects. Mission Control\u2019s cross-study query capability means that each new study compounds the value of previous ones and builds institutional knowledge instead of producing isolated reports.<\/p>\n<p>This architecture supports the shift from episodic research to always-on customer intelligence that enterprise organizations increasingly require.<\/p>\n<h2>Risks and Limitations of Current Research Platform Categories<\/h2>\n<p>Each legacy platform category carries structural limitations that matter for enterprise teams. Quantitative survey platforms produce structured data efficiently but cannot uncover the motivations, emotional responses, or unexpected findings that emerge from open-ended conversation. Decisions based on survey data alone risk optimizing for stated preferences rather than actual behavior.<\/p>\n<p>Legacy human-moderated platforms introduce scheduling dependencies, moderator variability, and turnaround times that conflict with fast product development cycles. Panel-only platforms solve sourcing but leave moderation, analysis, and reporting to the client team, which reintroduces the fragmentation and delay that enterprises are trying to eliminate.<\/p>\n<p>A common misconception holds that faster research tools automatically produce shallower insights. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\">With qual-at-scale, the old trade-off between depth and scale is no longer a barrier<\/a>. The real risk comes from choosing a platform that achieves speed by sacrificing participant quality, adaptive moderation, or analytical rigor.<\/p>\n<p>Evaluating platforms on all eight criteria at once, rather than optimizing for a single dimension, gives teams a more reliable basis for selection.<\/p>\n<h2>Decision Framework and Checklist for Platform Selection<\/h2>\n<p>Enterprise teams can apply a simple sequence to narrow the platform field. Start by defining the timeline constraint. If the research cycle must compress from weeks to hours, only end-to-end AI platforms with integrated recruitment and moderation qualify.<\/p>\n<p>Within that group, sample size becomes the next filter. If the study requires more than 20 participants for statistical confidence, human-moderated platforms are structurally excluded because of scheduling bottlenecks.<\/p>\n<p>Audience complexity adds a third layer. If the audience includes hard-to-reach segments below 1% incidence rate, panel-only platforms without dedicated recruitment operations will fail to deliver.<\/p>\n<p>Geographic and language coverage then come into play. If the organization operates across multiple markets and languages, the platform must support localization natively rather than through manual translation workflows.<\/p>\n<p>Security and compliance form the next screen. If enterprise security certifications are a procurement requirement, the platform must hold SOC 2, GDPR, ISO 27001, ISO 27701, and ISO 42001, not just one or two of these.<\/p>\n<p>Program structure follows. If the research program is continuous rather than episodic, the platform must support cross-study knowledge retention. Emotional measurement is the final layer. If emotional signal capture is required for creative testing, concept comparison, or brand research, the platform must analyze tone, word choice, and facial micro-expressions, not just transcripts.<\/p>\n<p>Listen Labs satisfies every condition in this checklist.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How does AI moderation compare to human moderation for qualitative research quality?<\/h3>\n<p>AI moderation through Listen Labs maintains the same methodological rigor as an experienced in-house research team. The platform\u2019s AI conducts adaptive interviews with dynamic follow-up questions and probes short or ambiguous answers in the same way a trained human moderator would.<\/p>\n<p>The underlying methodology was developed by a team with 50+ years of combined research expertise and is continuously refined based on data from tens of thousands of completed studies. For most enterprise research needs, including concept testing, usability studies, brand perception, and churn analysis, AI moderation delivers comparable qualitative depth at much greater speed and scale.<\/p>\n<p>Human moderation retains an advantage in highly sensitive or complex clinical contexts. For product testing and consumer insights, the quality gap is negligible and the speed advantage is decisive.<\/p>\n<h3>What participant quality controls does Listen Labs use?<\/h3>\n<p>Listen Labs applies three layers of quality control. The platform works exclusively with high-quality, non-commodity panel sources and excludes professional survey-taker pools that inflate commodity panels. Quality Guard monitors every interview in real time across video, voice, content, and device signals and detects fraud, low-effort responses, AI-generated scripts, and profile mismatches as they occur.<\/p>\n<p>Participants are capped at three studies per month, which removes incentive-driven repeat respondents. A dedicated recruitment operations team adds a human review layer for hard-to-reach segments. The reputation scoring system compounds across every interview on the platform, so participant quality improves continuously as the client base grows.<\/p>\n<h3>How quickly can Listen Labs deliver results, and what does the output include?<\/h3>\n<p>Listen Labs compresses the full research cycle, including study design, recruitment, moderation, analysis, and deliverables, to under 24 hours. Output includes automated key findings and theme analysis, consultant-quality PowerPoint slide decks, memo-style reports, video highlight reels, statistical charts, segmentation breakdowns, and custom reports generated from natural-language queries.<\/p>\n<p>The Research Agent produces these deliverables in under a minute after interviews are complete. Mission Control retains all findings in a searchable knowledge base, which enables cross-study queries and trend tracking without manual archiving.<\/p>\n<h3>What security certifications does Listen Labs hold?<\/h3>\n<p>Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications. The platform supports enterprise SSO, uses 256-bit encryption, and does not use customer data for AI model training. These certifications satisfy the security and governance requirements of Fortune 500 procurement processes across the Americas, Europe, APAC, and MEA.<\/p>\n<h3>Can Listen Labs support ongoing research programs rather than one-off studies?<\/h3>\n<p>Listen Labs is designed for continuous research programs. Mission Control serves as a persistent organizational knowledge base, and every study conducted on the platform adds to a searchable repository that supports cross-study queries, trend tracking over time, and institutional knowledge retention.<\/p>\n<p>Teams can retrieve answers from past research in seconds. This architecture means that each new study compounds the value of previous ones and supports the shift from episodic research projects to always-on customer intelligence programs that enterprise organizations increasingly require.<\/p>\n<h2>Conclusion: How Platform Categories Compare for 2026 Enterprise Needs<\/h2>\n<p>The comparison across study design, participant sourcing, moderation approach, data quality controls, analysis workflow, reporting capabilities, and operational fit reveals a structural divide. Legacy human-moderated tools cannot scale beyond small daily participant counts. Panel-only platforms solve sourcing in isolation while leaving moderation and analysis to the client.<\/p>\n<p>Quantitative survey tools sacrifice the conversational depth that uncovers unexpected insights. End-to-end AI-moderated platforms remove the depth-versus-scale trade-off by automating moderation and analysis while preserving adaptive questioning.<\/p>\n<p>Organizations evaluating this category should prioritize platforms with verified global panels, real-time fraud detection, emotional signal capture, and enterprise security certifications. These capabilities separate operational research infrastructure from point solutions.<\/p>\n<p>Within this category, the platform used by Microsoft, Perplexity, and Sweetgreen offers a 30-million-respondent verified panel, Quality Guard fraud prevention, Emotional Intelligence signal capture, and sub-24-hour turnaround backed by SOC 2, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\">See the end-to-end workflow in a live demo, from study design through automated deliverables in under 24 hours.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Top product testing platforms for enterprise research. Listen Labs delivers AI-moderated interviews, verified panels &amp; instant insights. Book a demo.<\/p>\n","protected":false},"author":52,"featured_media":230,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-447","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\/447","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"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/comments?post=447"}],"version-history":[{"count":2,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/447\/revisions"}],"predecessor-version":[{"id":1047,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/447\/revisions\/1047"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/230"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=447"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=447"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=447"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}