{"id":865,"date":"2026-06-09T05:05:05","date_gmt":"2026-06-09T05:05:05","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/brand-research-ai-vs-traditional\/"},"modified":"2026-06-09T05:05:05","modified_gmt":"2026-06-09T05:05:05","slug":"brand-research-ai-vs-traditional","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/brand-research-ai-vs-traditional\/","title":{"rendered":"Brand Research AI vs Traditional Methods: A 2026 Guide"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2>Key Takeaways<\/h2>\n<ul>\n<li>\n<p>AI-powered brand research compresses traditional 4\u20136 week cycles into under 24 hours, enabling real-time validation and decision-making.<\/p>\n<\/li>\n<li>\n<p>Listen Labs Emotional Intelligence captures tone, micro-expressions, and word choice simultaneously to surface genuine emotional signals that transcripts miss.<\/p>\n<\/li>\n<li>\n<p>Three-layer quality controls, including Listen Atlas matching, real-time Quality Guard monitoring, and strict frequency caps, remove professional respondents and fraud common in commodity panels.<\/p>\n<\/li>\n<li>\n<p>AI platforms deliver qual-at-scale at one-third the cost of traditional methods, so teams can run hundreds of interviews across 100+ languages without proportional budget increases.<\/p>\n<\/li>\n<li>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\">Schedule a Listen Labs demo<\/a> to see how its Research Agent and Mission Control turn weeks of work into minutes while maintaining enterprise-grade compliance.<\/p>\n<\/li>\n<\/ul>\n<h2>How This Guide Compares Brand Research Methods<\/h2>\n<p>This guide uses nine criteria to compare AI-powered and traditional brand research. The criteria are research cycle time, depth of insight including emotional signals, participant quality and fraud prevention, scalability without proportional cost increase, and methodological flexibility for brand tracking studies. The comparison also covers language and geographic coverage, analysis objectivity and bias reduction, deliverable speed and transparency, and long-term knowledge management.<\/p>\n<h2>Research Cycle Time<\/h2>\n<p>Traditional qualitative brand research typically runs a 4\u20136 week cycle from study design to final report. In large enterprises, internal prioritization queues, budget approvals, and research team backlogs can extend that timeline to six months. At that point, the business context has often shifted and the findings feel stale.<\/p>\n<p>AI-powered platforms compress the same cycle to under 24 hours. <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 1 million AI-powered customer interviews for companies including Microsoft, Perplexity, and Sweetgreen<\/a>. At Microsoft, the team used Listen Labs to collect global customer stories for the company&#8217;s 50th anniversary celebration within a single day, work that would have taken weeks through traditional channels. At Anthropic, 300+ user interviews surfaced churn drivers 5x faster than prior methods, identified where former Claude users migrate, and delivered a prioritized list of actionable product fixes within 48 hours.<\/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><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\"><strong>See how Listen Labs compresses your research cycle<\/strong><\/a> and compare a weeks-long process with a 24-hour turnaround.<\/p>\n<h2>Emotional Depth and Signal Quality in Brand Insights<\/h2>\n<p>Traditional moderated interviews and focus groups capture what participants say. Transcripts, self-reported ratings, and open-ended survey responses form the evidentiary base. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\">Traditional focus groups cost $4,000\u2013$12,000 per 90-minute session and take 3\u20135 weeks to complete<\/a>, yet they still produce only surface-level emotional data because moderators cannot track tone, micro-expression, and word choice across many participants at once.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\">Listen Labs Emotional Intelligence analyzes three simultaneous signal layers, tone of voice, word choice, and subconscious micro-expressions, to surface emotions that transcripts alone miss<\/a>. The methodology is <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\">built on Ekman&#8217;s universal emotions framework<\/a>, the same standard used in clinical psychology and peer-reviewed emotion-detection research. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC12745273\">Recent research confirms that neural networks can detect emotions in real-time voice recordings with promising accuracy for continuous emotion prediction<\/a>, and that <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC12745273\">word embeddings provide a theory-agnostic, data-driven method for uncovering emotional nuances beyond simple sentiment scoring<\/a>.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\">Every emotion is quantified per question and concept, with every label traceable to the exact timestamp, verbatim quote, and AI reasoning behind it<\/a>. For brand research, teams can see which creative asset triggers genuine delight versus flat or confused responses. Self-reported ratings often blur that distinction, while multimodal emotional data makes it explicit. The feature is available across 50+ languages and connects directly with the Research Agent for natural-language queries, charts, and highlight reels.<\/p>\n<h2>Participant Quality and Fraud Prevention<\/h2>\n<p>Commodity quantitative panels carry well-documented risks, including professional survey-takers focused on incentives, repeat respondents, and AI-generated answer scripts. These issues bias samples and undermine the entire research investment. Researchers then spend significant time on quality assurance before analysis can begin.<\/p>\n<p>Listen Labs addresses this through three layered controls. Listen Atlas, the platform&#8217;s AI orchestration layer, matches participants on behavioral and intent data rather than self-reported demographics alone, drawing from a global network of 30 million verified respondents across 45+ countries. Quality Guard monitors every interview in real time across video, voice, content, and device signals to detect fraud, low-effort responses, and mismatched profiles. Participants are capped at three studies per month, which removes the professional survey-taker problem. A dedicated recruitment operations team adds human review for hard-to-reach segments, including enterprise decision-makers, healthcare workers, and audiences below 1% incidence rate that commodity panels cannot reliably source.<\/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>Scalability Without Proportional Cost Increase<\/h2>\n<p>Traditional qualitative research does not scale economically. Each additional interview requires more moderator time, recruitment fees, transcription, and analyst hours. A single large-scale qualitative study can cost hundreds of thousands of dollars, which limits most enterprise research teams to a small number of studies per year regardless of demand.<\/p>\n<p><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 no longer blocks growth<\/a>. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\">Platforms like Listen Labs layer on auto-recruiting, transcription, sentiment tagging, and insight summarization so teams move from question to findings in hours, not weeks<\/a>. The Microsoft team reached hundreds of users at one third of the cost of traditional methods. P&amp;G used Listen Labs to deliver 250+ interviews with quantified themes and verbatim proof in hours, directly shaping product and brand strategy. Skims validated campaign direction with thousands of high-income buyers overnight, removing weeks of recruiting and panel sourcing. Robinhood received insights 5x faster and identified integration flows that boosted uptake 30\u201340%.<\/p>\n<h2>Flexible Brand Tracking and Share of Model Measurement<\/h2>\n<p>Traditional brand tracking relies on fixed survey instruments administered at set intervals. Changing a question mid-tracker breaks longitudinal comparability, and adding a new metric requires a new study cycle. This rigidity makes it difficult to respond to emerging brand threats or test new positioning in real time.<\/p>\n<p>AI-assisted study design lets teams adapt instruments between waves while preserving comparability, combine qualitative and quantitative formats in a single study, and run rapid creative or messaging tests between scheduled tracking waves. In 2026, a new brand metric has entered enterprise measurement programs. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/yotpo.com\/blog\/llm-market-analysis-guide\">INSEAD researchers formally introduced Share of Model (SOM) in mid-2025 as a metric measuring how often, prominently, and favorably brands appear in AI-generated responses, demonstrating that brand visibility can vary significantly across models and requires dedicated auditing<\/a>. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/kantar.com\/Campaigns\/Marketing-Trends\">Kantar&#8217;s 2026 Marketing Trends report identifies Generative Engine Optimisation as the new SEO, where brands must create structured, machine-legible content to get cited and trusted by LLMs<\/a>. Listen Labs supports Share of Model audit studies as part of its flexible study design capabilities, so brand teams can measure AI-generated brand perception alongside traditional awareness and consideration metrics.<\/p>\n<h2>Global Coverage and Objective Analysis<\/h2>\n<p>Traditional research agencies typically operate in a limited number of markets and languages, which forces separate vendor relationships for multi-market studies. Human analysts are also subject to confirmation bias, often emphasizing findings that align with pre-existing hypotheses while underweighting unexpected signals.<\/p>\n<p>Listen Labs supports 100+ languages for interview moderation and covers 45+ countries across the Americas, Europe, APAC, and MEA through a single platform. AI analysis processes all interview data consistently, identifying patterns and themes across hundreds of responses without the confirmation bias that affects human-led analysis. The platform&#8217;s proprietary dataset, built from tens of thousands of completed studies, informs signal-from-noise separation in ways that general-purpose AI tools cannot match.<\/p>\n<h2>Deliverables, Transparency, and Knowledge Retention<\/h2>\n<p>Traditional research produces manual reports that take days or weeks to write after fieldwork closes. Findings usually arrive as static slide decks with limited ability to interrogate the underlying data. Institutional knowledge from past studies lives in scattered files and individual researchers&#8217; memories, which leads organizations to repeatedly re-research the same questions.<\/p>\n<p>Listen Labs&#8217; Research Agent generates consultant-quality slide decks, memos, video highlight reels, statistical charts, and segmentation breakdowns in under a minute. Every deliverable is traceable to the underlying interview data, and teams can ask follow-up questions in natural language without returning to the raw transcripts. Mission Control serves as a persistent organizational knowledge base. Each new study grows the repository, enabling cross-study queries and trend tracking so teams can answer questions from past research in seconds rather than commissioning new studies.<\/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><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\"><strong>Explore Mission Control and the Research Agent live<\/strong><\/a> with a walkthrough focused on your current reporting workflow.<\/p>\n<h2>Best-Fit Enterprise Use Cases for AI and Traditional Methods<\/h2>\n<p>AI-powered brand research delivers the strongest return when the evaluation criteria favor speed, scale, emotional signal capture, and hard-to-reach audiences. Insights teams with growing backlogs need to multiply research output without adding headcount, which directly connects to scalability without proportional cost increase. Rapid creative validation before campaign launch benefits from emotional signal data alongside stated preference, aligning with the depth-of-insight criterion.<\/p>\n<ul>\n<li>\n<p>Insights teams with growing backlogs that need to multiply research output without adding headcount, matching the scalability and cost criteria.<\/p>\n<\/li>\n<li>\n<p>Rapid creative validation before campaign launch, where emotional signal data is required alongside stated preference to choose winning concepts.<\/p>\n<\/li>\n<li>\n<p>Product concept feedback across multiple markets simultaneously, requiring consistent methodology, broad language coverage, and fast turnaround.<\/p>\n<\/li>\n<li>\n<p>Share of Model audits measuring brand presence and sentiment in AI-generated responses across multiple LLMs, linked to methodological flexibility.<\/p>\n<\/li>\n<li>\n<p>Agency and consultancy engagements where client timelines demand results in days rather than weeks, emphasizing research cycle time and deliverable speed.<\/p>\n<\/li>\n<li>\n<p>Niche-audience studies requiring recruitment below 1% incidence rate that commodity panels cannot reliably serve, connecting to participant quality controls.<\/p>\n<\/li>\n<\/ul>\n<p>Traditional methods retain advantages in highly sensitive ethnographic work, longitudinal studies requiring decade-scale comparability with existing instruments, and contexts where regulatory frameworks mandate human-moderated data collection.<\/p>\n<h2>Operational Considerations and Compliance<\/h2>\n<p>Transitioning from traditional to AI-powered research requires stakeholder alignment across research, legal, IT, and procurement functions. Many non-adopters of generative AI cite lack of suitability, legal or data-privacy concerns, and insufficient internal skills as reasons for not adopting, which shows that compliance and change management remain material adoption barriers.<\/p>\n<p>Listen Labs addresses these compliance and change-management barriers through enterprise-grade certifications and workflow integration. The platform holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, which directly address the legal and data-privacy concerns that non-adopters cite. Customer data is never used for AI model training, and enterprise SSO is supported to meet IT security requirements. The platform is designed as a force multiplier for existing research teams, enabling them to run more studies with the same headcount while preserving research expertise rather than replacing it.<\/p>\n<h2>Risks and Limitations of AI-Powered Brand Research<\/h2>\n<p>AI-powered research carries specific risks that enterprise teams should evaluate honestly. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/laweconcenter.org\/resources\/ai-productivity-and-labor-markets-a-review-of-the-empirical-evidence\">Research on AI in professional workflows describes a &#8220;jagged technological frontier&#8221; where AI substantially improves performance on tasks within its capability boundary but can reduce performance on tasks just beyond it due to overreliance on plausible but incorrect outputs<\/a>. Human oversight therefore remains essential for interpreting findings in novel cultural contexts, validating unexpected results, and making high-stakes strategic decisions.<\/p>\n<p>Purely automated approaches also carry risk in longitudinal studies where instrument consistency over many years is required, in regulatory environments that mandate human-moderated data collection, and in studies where the research question itself is ambiguous and needs iterative human refinement. A hybrid model works best in these cases, with deliberate task allocation between automated and human-led components based on the specific research objective.<\/p>\n<h2>Decision Framework for Matching Goals to Methods<\/h2>\n<p>This decision framework helps align brand research goals, timelines, budgets, and capabilities with the appropriate method or combination of methods. When the primary constraint is time, with results needed in hours or days rather than weeks, AI-powered platforms are the only viable option at meaningful sample sizes. When the primary constraint is cost and the team needs to run more studies per quarter without budget increases, AI platforms that deliver results at a third of traditional cost address that constraint directly.<\/p>\n<p>When emotional signal data is required alongside stated preference for creative testing, concept comparison, or brand perception studies, platforms with multimodal emotional intelligence capabilities become necessary because transcript-only methods miss that data. When the study requires Share of Model measurement alongside traditional brand tracking KPIs, the research design must accommodate both AI-generated response audits and survey-based awareness and consideration metrics. When geographic or linguistic coverage spans multiple markets simultaneously, a platform supporting 100+ languages and 45+ countries through a single workflow removes the coordination overhead of multi-vendor approaches. When the study involves a sensitive population, a regulatory requirement for human moderation, or a longitudinal instrument with decade-scale comparability requirements, traditional methods or a hybrid design with human oversight at key stages fit better.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How quickly can AI-powered brand research deliver results compared with traditional timelines?<\/h3>\n<p>The speed difference between AI-powered and traditional research, under 24 hours versus 4\u20136 weeks, fundamentally changes what research can support. Instead of relying on retrospective analysis, teams can validate creative, test concepts, and track brand perception in near real time. The Microsoft team collected global customer stories within a single day, and Anthropic received 300+ user interviews with prioritized findings within 48 hours, which illustrates how compressed timelines unlock new use cases.<\/p>\n<h3>How does Listen Labs ensure participant quality through Quality Guard and frequency limits?<\/h3>\n<p>Listen Labs applies three layers of quality control. First, the platform works exclusively with high-quality, non-commodity panel sources, so professional survey-takers and incentive-driven respondents are excluded at the sourcing stage. Second, Quality Guard monitors every interview in real time across video, voice, content, and device signals, detecting fraud, low-effort responses, AI-generated scripts, and mismatched demographic profiles before they enter the analysis dataset. Third, participants are limited to three studies per month across the platform, which removes panel fatigue and professional respondent problems. A dedicated recruitment operations team adds human review for hard-to-reach segments, including enterprise decision-makers, healthcare workers, and audiences below 1% incidence rate.<\/p>\n<h3>What emotional intelligence methodology does Listen Labs use for brand research?<\/h3>\n<p>Listen Labs Emotional Intelligence analyzes three simultaneous signal layers, tone of voice, word choice, and subconscious micro-expressions. The methodology is built on Ekman&#8217;s universal emotions framework, the same standard used in clinical psychology and peer-reviewed emotion-detection research, and it tracks emotions including anger, anticipation, disgust, fear, joy, sadness, trust, and surprise. Every emotion is quantified per question and concept, and every label is traceable to the exact timestamp, verbatim quote, and AI reasoning behind it. For brand research, teams can pinpoint which specific moment in a creative asset or brand narrative triggers confusion, hesitation, or genuine delight, distinctions that self-reported ratings and transcript-only analysis routinely miss. The feature is available across 50+ languages and integrates with the Research Agent for natural-language queries and highlight reel generation.<\/p>\n<h3>Which security certifications support enterprise brand research on Listen Labs?<\/h3>\n<p>Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications. The platform uses 256-bit encryption, supports enterprise SSO, and never uses customer data for AI model training. These certifications address the legal and data-privacy concerns that surveys consistently identify as primary barriers to enterprise AI adoption in research workflows. For procurement and legal teams evaluating platform compliance, Listen Labs&#8217; certification portfolio covers information security management (ISO 27001), privacy information management (ISO 27701), and AI management systems (ISO 42001).<\/p>\n<h3>How does Listen Labs integrate with existing research workflows and Share of Model tracking?<\/h3>\n<p>Listen Labs is designed as a force multiplier for existing research teams rather than a replacement. Teams can bring their own participants from internal user bases at reduced cost, integrate with existing panel providers, and export deliverables in standard formats for use in current reporting workflows. For Share of Model tracking, the 2026 metric measuring how often, prominently, and favorably brands appear in AI-generated responses, Listen Labs supports dedicated audit study designs that can run alongside traditional brand tracking waves. Mission Control, the platform&#8217;s cross-study knowledge base, lets teams query findings from Share of Model audits alongside traditional awareness, consideration, and emotional signal data, building a unified view of brand health across both human and AI-mediated consumer touchpoints.<\/p>\n<h2>Conclusion: Choosing the Right Brand Research Approach in 2026<\/h2>\n<p>The depth-versus-scale trade-off that defined brand research for decades has effectively been resolved. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\">AI-powered qual-at-scale removes the barrier between large sample sizes and rich, nuanced insight<\/a>. The practical question for enterprise brand and insights teams in 2026 is how to allocate research objectives across AI-led and human-led methods based on the specific criteria outlined in this guide.<\/p>\n<p>Listen Labs delivers emotional intelligence, Share of Model measurement capability, participant quality controls, and operational scale that traditional methods cannot match at equivalent speed and cost, while remaining fully compatible with the hybrid research programs that leading brands are building. <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\">As Listen Labs CEO Alfred Wahlforss has stated, &#8220;Companies use it for all kinds of large decisions. This AI interviewer means that you can have hundreds of one-on-one interviews run at scale.&#8221;<\/a><\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/listenlabs.ai\/book-my-demo\"><strong>Ready to clear your research backlog?<\/strong><\/a> Book a demo to map Listen Labs&#8217; capabilities to your brand research program and see emotionally nuanced insights at enterprise scale.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI shrinks brand research from weeks to hours at \u2153 the cost. See Listen Labs&#8217;s 2026 breakdown and find out why top research teams are switching.<\/p>\n","protected":false},"author":52,"featured_media":864,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-865","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\/865","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=865"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/865\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/864"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=865"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=865"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=865"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}