Top AI Moderated User Interview Platforms in 2026

The qualitative research market is undergoing a structural shift. Teams that once relied on scheduling five to eight human-moderated interviews per week — waiting weeks for synthesis — are now running dozens of AI-moderated sessions simultaneously and receiving structured findings the same day.

But not all AI moderated interview platforms are equal. Some automate the scheduling and transcription. Others add adaptive probing. A small number go further, layering in behavioral and emotional signals that capture what participants do and feel — not just what they say.

This guide covers eleven platforms, how to evaluate them using a three-signal-layer framework, and a decision guide to match your research needs to the right tool.

What is an AI moderated interview platform?

An AI moderated interview platform is software that uses conversational AI to conduct qualitative interviews — asking questions, probing follow-ups, transcribing, and synthesizing themes — at a scale human moderators cannot match.

Before going further, a terminology note: "AI moderation" in research means AI-led interviewing — the AI acts as the moderator, asking questions and following up dynamically. This is entirely different from "content moderation" in Trust & Safety, where AI flags harmful content on social platforms. If you've landed here from a Trust & Safety context, you want a different category of tools.

Compared to surveys, AI moderated interviews are exploratory. Surveys deliver fixed questions to many respondents; they capture what people choose to report, not the reasoning behind it. AI moderated interviews follow threads, ask "why," and surface the motivations surveys miss.

Compared to human-moderated interviews, AI moderated interviews scale. A skilled human moderator can conduct five to eight quality sessions per week. An AI moderator can run hundreds in parallel, with consistent question framing, no interviewer bias, and instant synthesis. The trade-off is depth: skilled human moderators can read a room, pick up on body language, and improvise in ways no AI has fully replicated — yet.

How AI moderated interviews work

The workflow across most platforms follows a similar arc:

1. Discussion guide upload — You input your research questions, screener criteria, and any stimulus materials (images, prototypes, concepts).

2. AI-led sessions — Participants join via a link. The AI moderator conducts the interview over text, audio, or video, depending on the platform.

3. Adaptive probing and laddering — The AI follows up on responses in real time. Good platforms use laddering: moving from a surface answer → the reasoning behind it → the underlying motivation. This is the quality marker that separates genuine AI moderation from a glorified chatbot running a survey script.

4. Transcription, coding, and theme extraction — Sessions are transcribed automatically. The AI identifies patterns, codes themes, and surfaces key quotes.

5. Deliverables — Outputs range from tagged transcripts and theme reports to full executive summaries, depending on platform maturity.

One emerging capability separates the more advanced platforms from the rest: capturing not just verbal responses but also behavioral and emotional signals. Verbal analysis is now table stakes. Behavioral tracking (what participants look at, where they hesitate, how long they engage) and emotional detection (facial expressions, voice modulation) represent the frontier — and the clearest differentiator between platforms in 2026.

How to evaluate AI moderation platforms: the three signal layers

When comparing platforms, a single framework makes the decision cleaner. Think of every AI moderated interview as generating data across three distinct signal layers.

Layer 1 — Verbal (say): The transcript. Themes. Sentiment analysis. Keyword frequency. This is the baseline — every platform on this list covers it. Verbal analysis tells you what participants said and how they described their experience.

Layer 2 — Behavioral (do): Screen recordings, gaze paths, attention heatmaps, hesitation signals, click behavior, engagement duration. A subset of platforms captures this layer. Behavioral data tells you what participants actually did — and where words diverged from actions.

Layer 3 — Emotional (feel): Facial expression coding, voice emotion AI, real-time emotional engagement curves. Very few platforms reach this layer. Emotional data tells you what participants felt, whether or not they chose to express it. This is where the Say-Do-Feel gap closes.

Most research decisions go wrong at the gap between Layer 1 and Layer 3. A participant says a packaging design "looks good." Behaviorally, they glanced at it for 0.4 seconds. Emotionally, their facial coding shows a micro-expression of confusion. The verbal layer alone would have greenlighted a product launch. The combined picture would not.

Five practical selection criteria:

1. Probing depth — Does the AI follow up dynamically based on what a participant says, or does it follow a fixed script regardless of the response? Laddering capability is the test.

2. Signal layers captured — Does the platform go beyond transcription to capture behavioral or emotional signals? For high-stakes research, this matters.

3. Panel and recruitment model — Does the platform include a participant panel, or do you bring your own? What geographies and audience segments does it cover?

4. Analysis and reporting workflow — How much manual synthesis is still required? Does output plug into your existing research stack (Confluence, Slack, Dovetail, etc.)?

5. Enterprise governance — SOC2/ISO/GDPR compliance, single sign-on, role-based access, data residency options.

NN/G's AI interviewer study provides evidence-based guidance on when AI-led qualitative research performs comparably to human moderation and where limitations remain — a useful benchmark for teams deciding which study types to route to AI vs. human moderators.

Top 11 AI moderated user interview platforms

The platforms below are evaluated against the three-signal-layer framework. Each entry includes a "best for" phrase and a signal-layer classification to make comparison fast.

1. Decode (Decode by Entropik)

Best for: Teams that need insight beyond the transcript — CPG and FMCG consumer research, UX research at scale, BFSI customer experience research, and any study where the verbal answer is unlikely to tell the full story.

Signal-layer classification: All three — Verbal + Behavioral + Emotional.

AI Moderator by Decode is the only platform in this list that combines all three signal layers in a single research session. The AI moderator (Mira) conducts qualitative interviews with adaptive probing and real-time laddering. Simultaneously, the platform captures:

• Facial coding at 90%+ accuracy across 62 facial expressions, detecting micro-expressions that participants don't consciously report

• Eye tracking at 96% accuracy, showing attention, hesitation, and visual engagement on any stimulus

• Voice emotion AI analyzing tone, pace, and modulation for emotional signals that don't surface in the transcript

This combination directly addresses the Say-Do-Feel gap. When a CPG brand shows packaging concepts, Decode captures not just what participants say about them but where their eyes go first, how long they hold attention, and what their face and voice reveal about genuine emotional response.

Platform strengths:

• Only platform combining AI moderation + facial coding + eye tracking + voice emotion AI in one workflow

• 70+ languages supported — strong for APAC, MENA, LATAM multi-country research

• 150+ global brands trust the platform, including clients in CPG, BFSI, telecom, and retail

• 17 patents in emotion AI

• SOC2 Type II, ISO 27001, and GDPR compliant; desktop-optimized for higher facial signal reliability

• 100M+ panel reach via Cint and Dynata for built-in participant recruitment

• Connects to Insights Hub for longitudinal tracking across studies

2. Maze

Best for: Product and UX teams running concept and prototype feedback at speed, where sessions are tightly tied to design artifacts.

Signal-layer classification: Verbal only (+ usability behavioral metrics via prototype testing integration, not emotion).

Maze built its reputation on fast, unmoderated prototype testing. Its AI moderator extends that motion into conversational feedback — participants complete a task flow and the AI asks follow-up questions about their experience. The native integration between moderation and prototype testing is its clearest advantage; teams already using Maze for usability studies can add AI interview layers without changing their workflow.

Strengths:

• Native integration with existing Maze prototype testing workflows

• Low friction — good for teams without dedicated research ops

• Fast turnaround for early-stage concept validation

• Accessible to non-researchers on product and design teams

Limitations: Probing depth is limited compared to dedicated AI moderation platforms. Not designed for complex exploratory research or multi-stimulus concept testing. Primarily focused on digital product contexts.

See also: AI Moderated Interview Platforms — how to run your first study

3. Outset

Best for: High-volume scalable qualitative programs where teams need professional-grade AI moderation at volume across diverse research questions.

Signal-layer classification: Verbal + partial Behavioral.

Outset is one of the most sophisticated AI moderation tools in the market. Its AI moderator is context-aware — it can see the participant's screen during digital sessions and ask follow-up questions based on where they navigate, what they click, and how long they pause on a given interface element. This gives it a meaningful behavioral layer for screen-based UX testing. Outset has established enterprise adoption in technology and financial services and is frequently cited in analyst coverage of the AI research category.

Strengths:

• Strong adaptive probing with visual context (screen-aware AI)

• Clean, fast session setup suitable for high-volume programs

• Good synthesis and tagging for UX teams

• Growing enterprise adoption in tech and financial services

Limitations: No emotion AI or facial coding. Strong for digital product research; less suited for packaging, advertising, or concept research where non-screen stimuli matter.

4. Userology

Best for: UX researchers running moderated usability studies where vision-aware AI can observe participant behavior on-screen alongside the interview.

Signal-layer classification: Verbal + Behavioral.

Userology is a vision-aware AI moderation platform that has gained consistent citation across the AI interview SERP, particularly in usability research contexts. Its differentiator is the AI's ability to observe what participants are looking at and doing on-screen during moderated sessions — layering behavioral context onto the interview transcript in a way that supports usability-focused research workflows. For teams building out AI moderation capabilities within UX research programs, Userology is worth evaluating alongside Outset.

Strengths:

• Vision-aware AI that observes screen activity during moderated sessions

• Well-suited to structured usability studies and task-based research

• Gaining visibility among UX researcher communities

Limitations: Less suited to consumer research contexts (packaging, advertising, brand perception) where non-screen stimuli are the focus. No structured emotion AI or facial coding layer.

5. Conveo

Best for: European research teams and multinational brands requiring GDPR-native AI moderated interview infrastructure with video-first data capture.

Signal-layer classification: Verbal + partial Emotional (voice and facial signal capture via video).

Conveo is a European-first AI moderated interview platform built on a video-first architecture. It captures voice and facial signals as a byproduct of video sessions — providing partial emotional context alongside transcripts, though without the structured facial coding and emotion AI of dedicated emotion platforms. Its GDPR-native data residency controls make it the default choice for European buyers managing compliance requirements across EU and multinational research programs. It is growing its US presence in 2026 with brands that need unified compliance guarantees across regions.

Strengths:

• European data residency with GDPR-native architecture

• Video-first format captures voice and facial signals alongside transcripts

• Good probing depth for exploratory qualitative research

• Competitive on multilingual capabilities for EU markets

Limitations: Smaller participant panel than US-headquartered platforms. Facial signal capture is a video byproduct, not structured emotion AI. Behavioral signal depth is limited compared to screen-tracking platforms.

6. GetWhy

Best for: CPG, FMCG, and retail brands running video-based consumer insights studies with enterprise human validation alongside AI analysis.

Signal-layer classification: Verbal only (video-based sessions with human review layer).

GetWhy runs AI-led video interviews with consumers and layers human validation into the analysis process — a hybrid model that appeals to enterprise buyers who want the speed of AI moderation with a human quality check on findings. Its strength is in visual and packaging research, where the video format captures informal behavioral and emotional cues, though GetWhy does not offer systematic emotion AI or facial coding. The human validation layer is a meaningful differentiator for brands whose research programs require sign-off confidence on qualitative findings.

Strengths:

• Enterprise AI qual with human validation as a quality layer

• Strong for packaging, brand perception, and retail shelf research

• Consumer-grade recruitment panel for CPG verticals

• Video output makes findings compelling for stakeholder presentations

Limitations: No structured facial coding or emotion AI — emotional signals are informal and unstructured. Not designed for the volume and speed use cases where AI-only moderation is the point. Higher cost model than AI-only platforms given human involvement.

7. Listen Labs

Best for: Market research teams that need an end-to-end AI qualitative workflow — from recruitment through moderation to a searchable cross-study repository.

Signal-layer classification: Verbal only.

Listen Labs focuses on building a complete end-to-end qualitative workflow: participant recruitment, AI-moderated interviews (text or voice-based), automated synthesis, and a cross-study insight repository that accumulates organizational knowledge over time. For research teams running continuous discovery programs, the repository layer is a meaningful differentiator — teams can query across hundreds of past interviews to find relevant themes and quotes without re-reading transcripts. The platform has strong capacity for high-volume text-based AI interviews.

Strengths:

• End-to-end workflow covering recruitment, moderation, synthesis, and repository

• Cross-study insight repository enables longitudinal qualitative knowledge management

• High volume capacity — can run thousands of AI interviews simultaneously

• Good for FMCG and CPG brand research at scale

• Fast synthesis turnaround with searchable output

Limitations: Text and voice-based moderation; no structured behavioral or emotional signal capture. The repository value compounds over time — newer teams won't see its full benefit immediately.

8. Genway

Best for: Research programs where verbal analysis alone is insufficient and teams need AI-measured speech emotion and facial expression data from moderated sessions.

Signal-layer classification: Verbal + Emotional (speech emotion recognition and facial expression detection).

Genway occupies a distinct position in the category — it explicitly applies speech emotion recognition and facial expression detection to AI-moderated research sessions. For teams that need emotional signal data without implementing a full multimodal platform like Decode, Genway provides a meaningful step up from verbal-only analysis. The platform is particularly relevant for consumer research contexts where understanding emotional response to stimuli is part of the research objective.

Strengths:

• Speech emotion recognition applied to moderated interview audio

• Facial expression detection adds emotional signal layer to sessions

• Relevant for consumer research requiring emotional response measurement

• Differentiating emotional AI coverage compared to transcript-only platforms

Limitations: Emotional signal coverage is narrower than platforms built from the ground up on multimodal emotion AI (such as Decode, with 62 facial expressions, 90%+ facial coding accuracy, and 96% eye tracking accuracy). Panel access and global language coverage are more limited than enterprise platforms.

9. Strella

Best for: Marketing and insights teams running fast concept screening and early-stage validation where speed matters more than signal depth.

Signal-layer classification: Verbal only.

Strella prioritizes speed and ease of use above depth. Its platform is designed for teams that need quick qualitative direction — a "should we pursue this concept?" check before investing in larger studies — rather than a deep multi-signal analysis. The low-friction setup makes it accessible to teams without dedicated research operations.

Strengths:

• Very low setup friction

• Good for early-stage concept screening and fast directional feedback

• Accessible to non-research team members

• Speed from study launch to findings

Limitations: Probing depth is basic. Not suited for complex, multi-stimulus, or multi-country studies. Output depth may not satisfy stakeholders requiring rigorous qualitative evidence for high-stakes decisions.

10. Marvin

Best for: Research ops teams managing large interview libraries who need AI-assisted synthesis and moderation connected to an organizational knowledge base.

Signal-layer classification: Verbal only.

Marvin occupies an interesting position — it's as much a research repository as it is a moderation tool, and was included in NN/G's AI interviewer study as one of the tested platforms. Teams use it to run AI-assisted interviews and store, tag, and retrieve insights across a growing body of qualitative data. Its strength is the connected intelligence layer: query Marvin across 200 past interviews and it surfaces relevant clips and themes. For research ops teams building institutional knowledge, this accumulated utility is the key value proposition.

Strengths:

• Best-in-class research repository and knowledge management

• AI interviews integrate naturally with the synthesis and retrieval layer

• Good for cross-functional teams that need to share insights with product, design, and strategy

• Longitudinal value compounds as the research library grows

Limitations: AI moderation depth is secondary to synthesis and repository. No behavioral or emotional signal capture. Teams prioritizing rich, signal-layered moderation should evaluate platforms where moderation is the primary design focus.

11. UserTesting

Best for: Enterprise UX and insights teams adding AI moderation to a mature platform with one of the largest participant panels in the market.

Signal-layer classification: Verbal only (AI analysis layer over video feedback).

A clarification is important here: UserTesting's core product is video feedback — participants narrate their experience while using a product or viewing a concept. AI analysis layers on top to tag themes, surface clips, and generate summaries. The AI does not conduct the interview in the same sense as purpose-built AI moderation platforms; it primarily analyzes pre-recorded sessions. UserTesting is the incumbent enterprise generalist adding AI moderation capability to a large, established platform — relevant for buyers already in the UserTesting ecosystem or those prioritizing panel reach above moderation sophistication.

Strengths:

• Very large enterprise panel with strong North American coverage

• Mature AI synthesis and theme tagging on video feedback

• Established enterprise compliance and security framework

• Familiar to large UX research teams with existing workflows

Limitations: The "AI moderator" is principally an analysis overlay on video feedback, not a true conversational AI conducting adaptive sessions. Teams looking for AI-led adaptive probing and laddering will find this distinction significant. No behavioral or emotional signal capture beyond informal video observation.

When to use AI moderators vs. human moderators

This is the question every research team grapples with as AI moderation matures. The honest answer is not "one replaces the other" — it's about routing the right study type to the right moderation approach.

Where AI moderation fits best:

• Scale and consistency — Parallel execution of dozens to hundreds of interviews with identical question framing. Human moderators cannot achieve this without introducing variance across sessions.

• Multilingual programs — AI platforms supporting 70+ languages can run simultaneous studies across markets without the logistics and cost of multilingual human moderators in each geography.

• Structured research designs — Concept testing, ad testing, packaging research, and product feedback where the discussion guide is defined and adaptive follow-up operates within a bounded frame.

• Screening and initial discovery — Running a large initial wave of AI interviews to map the landscape before bringing in human moderators for the strategic deep-dive sessions.

• Speed-sensitive decisions — When time to insight is the constraint and the research question can be answered with verbal (and behavioral/emotional, where platforms support it) analysis.

Where human moderation stays stronger:

• Exploratory research — When the research question is genuinely open-ended and the interviewer may need to follow unexpected threads no discussion guide anticipated.

• Sensitive topics — Research involving health, loss, financial distress, or other high-stakes personal experiences where participant trust and rapport are prerequisites for honest disclosure.

• Relationship-dependent research — Longitudinal diary studies, co-creation sessions, or studies where the researcher-participant relationship is itself part of the research design.

• Novel hypothesis generation — When the goal is to uncover something unexpected, skilled human moderators are more likely to recognize and follow the anomaly the AI might normalize.

• High-stakes strategic decisions — Where the organization requires not just a finding but the confidence that a human judgment reviewed the data.

The practical hybrid pattern: Most mature research programs are moving toward an AI-first approach for volume and speed studies, reserving human moderation for strategic, sensitive, or exploratory phases. AI moderation handles the tactical majority; human moderation handles the work where relationship and improvisation matter most.

Quality signals to track in AI studies: Completion rate (below 70% may indicate friction in session design), average session length (very short sessions can mean questions were too narrow), response depth per probe (thin answers suggest the laddering isn't engaging the participant), and spot-checking AI coding against researcher interpretation for the first few waves of a new study type.

How much do AI moderated interview platforms cost?

Pricing across this category is largely opaque — most platforms do not publish list prices and operate on custom enterprise contracts. That said, understanding the pricing model helps buyers evaluate the right approach for their volume and workflow.

Three common pricing models:

1. Per-study or credit-based — You purchase a set number of interviews or "credits" that deduct as sessions run. Common for lower-volume teams or platforms targeting research teams running occasional studies. Platforms like Strella and Marvin lean toward subscription models with usage-based components.

1. Monthly or annual subscription — A flat or tiered fee covering a defined number of seats, studies, or sessions per period. Suitable for teams running research on a regular cadence. Maze operates primarily on a subscription model.

1. Custom enterprise contracts — The dominant model for platforms serving research-intensive enterprise buyers. Pricing is negotiated based on volume, number of research seats, panel access requirements, and compliance features. Decode by Entropik, Outset, Conveo, GetWhy, Listen Labs, and UserTesting all operate in this tier.

Cost drivers beyond the platform fee:

• Panel and participant incentives — If the platform does not include a built-in panel, recruiting and incentivizing participants is a significant additional cost. Platforms with built-in panels (Decode's 100M+ reach via Cint/Dynata, GetWhy's consumer panel, UserTesting's enterprise panel) factor participant access into their pricing; bring-your-own-audience platforms do not.

• Analysis seats — Some platforms charge per researcher or analyst accessing the synthesis and repository layer.

• Volume thresholds — Most enterprise platforms tier pricing by interview volume; per-interview costs typically decrease at scale.

• Professional services — For teams outsourcing study design and analysis interpretation, professional services are an additional cost layer.

Why AI moderated interviews are becoming standard

The shift from "interesting experiment" to standard research practice is happening faster than most research teams anticipated. Several converging forces are driving this.

• Scale economics. The cost per qualitative data point has dropped significantly as AI moderation removes the per-session labor cost of human moderation. Teams that once ran 10 interviews per quarter are now running 100 — and getting proportionally richer signal on decisions that previously relied on small-sample inference.

• Speed to insight. AI synthesis compresses the timeline from data collection to findings. What once required days of manual coding and theme extraction now returns in hours. For fast-moving product and marketing teams, this change in latency is as important as the scale change.

• Consistency at volume. Human moderators vary. Across 50 sessions conducted by multiple researchers, question framing shifts, probing depth varies, and synthesis reflects individual interpretive styles. AI moderation applies the same discussion guide with the same probing logic across every session — making it easier to compare findings across study waves.

• Analyst and market signals: Forrester research on AI agent adoption in enterprise workflows points toward AI-led research tools becoming standard practice in insight functions within the next two years. The McKinsey Business Value of Design research provides context on the ROI of investing in research rigor — AI moderation is one of the levers research leaders are pulling to increase study volume without proportional headcount increases.

• The next wave — signal depth. The category is not just getting faster; it's getting deeper. Transcript-only AI moderation is now table stakes. The competitive frontier is what platforms can measure beyond the transcript — behavioral patterns, emotional engagement, the divergence between what a participant says and what their face and voice reveal. NCSolutions and behavioral economics research consistently show that emotional response is a stronger predictor of purchase intent than stated preference. AI moderated interview platforms that can measure emotion directly — rather than inferring it from verbal sentiment — will define the next standard for qualitative research quality.

How Decode by Entropik fits into your research stack

Decode by Entropik is a unified human insights platform built around the premise that the most important signals in consumer and user research are often the ones people don't consciously report.

Its AI Moderator (Mira) conducts qualitative interviews with adaptive probing and real-time laddering. In the same session, Decode's Emotion AI layer — 17 patents, 90%+ facial coding accuracy, 96% eye tracking accuracy, 62 facial expressions tracked — captures the behavioral and emotional context the verbal layer misses.

Studies run in 70+ languages, with participant access through a 100M+ panel (Cint and Dynata). Outputs connect to Insights Hub for longitudinal tracking across research programs. The platform is SOC2 Type II, ISO 27001, and GDPR compliant, with enterprise governance built for global research teams.

For teams running AI creative testing alongside moderated interviews, Decode's AI Creative Insights product connects creative performance data to the same emotional and behavioral signals — closing the loop from research insight to campaign performance.

Decode is built for the complete three-signal picture: what participants say, what they do, and what they feel. For research teams where that distinction matters, it's the only platform in this category that delivers all three in a single study workflow.

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Frequently asked questions

1. What is the best AI moderated interview platform?

The best AI moderated interview platform depends on the signals your research requires. For verbal insights at scale, Outset and Listen Labs are strong choices. For European GDPR requirements, Conveo. For fast UX prototype testing, Maze. For teams that need the full picture — what participants say, do, and feel — Decode by Entropik is the only platform combining AI moderation with facial coding, eye tracking, and voice emotion AI in a single session.

2. Which AI interview platforms analyze emotions?

Three platforms in this list include structured emotion signal capture: Decode by Entropik (facial coding at 90%+ accuracy across 62 expressions, eye tracking at 96% accuracy, and voice emotion AI — all three layers), Genway (speech emotion recognition and facial expression detection), and Conveo (partial voice and facial signals captured via video sessions, without structured emotion AI). All other platforms on this list are verbal-layer only.

3. How do AI moderated interview platforms compare to survey tools?

Surveys deliver fixed questions and capture stated preferences — they scale easily but miss reasoning, motivation, and emotional context. AI moderated interview platforms are exploratory; the AI follows threads, probes follow-ups, and surfaces the "why" behind stated answers. The two tools are complementary: surveys for measurement, AI moderated interviews for depth and discovery.

4. Can AI moderated interview platforms replace human researchers?

Not fully — and the better platforms are designed to augment rather than replace researchers. AI moderators excel at consistency, scale, and synthesis. Human researchers excel at design judgment, stakeholder communication, building participant trust in sensitive topics, and interpreting ambiguous findings. The most effective research programs use AI moderation to run volume studies quickly and reserve human moderation for complex, high-stakes, or nuanced research contexts.

5. What should I look for in an AI moderated interview platform?

Evaluate platforms on five dimensions: (1) probing depth — does the AI ladder from surface answers to motivations? (2) signal layers — does it capture behavioral or emotional data beyond the transcript? (3) panel model — does it include recruitment, or do you bring your own participants? (4) analysis workflow — how much manual synthesis remains, and does output integrate with your existing tools? (5) enterprise governance — SOC2/ISO/GDPR compliance, data residency, and access controls.

6. Are AI moderated interviews accurate?

Accuracy varies by signal layer. Transcription accuracy on modern platforms is typically above 90% for major languages. AI synthesis quality — theme extraction, sentiment analysis — has improved significantly since 2023 but benefits from researcher review before final reporting. Emotion AI accuracy (where available) is measured separately: Decode by Entropik reports 90%+ facial coding accuracy and 96% eye tracking accuracy across 62 facial expressions. For any high-stakes study, treat AI output as a strong first draft requiring researcher interpretation, not a final deliverable.

7. How much do AI moderated interview platforms cost?

Most platforms in this category operate on custom enterprise pricing. Three pricing models are common: per-study/credit-based (pay per interview), monthly or annual subscription (fixed fee for a defined number of seats or sessions), and custom enterprise contracts (negotiated based on volume, panel access, and compliance needs). Decode by Entropik offers a first study free for teams evaluating the platform. For any serious evaluation, request a pilot before committing to an annual contract.

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