Qualitative Research at Scale

Qualitative Depth at Quantitative Scale — No Tradeoff Required

Run 1,000+ in-depth interviews per week with AI moderation. Every conversation goes 5–7 levels deep using structured laddering methodology — giving you statistically meaningful qualitative data in days, not months.

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1,000+ interviews per week
30+ min depth each
Enterprise rigor at every scale
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Tell me about the moment you decided to switch providers.

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Trust and transparency are the #1 decision drivers across all segments.

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Definition

What Is Qualitative Research at Scale?

Qualitative research at scale means running hundreds or thousands of in-depth interviews simultaneously using AI moderation — without sacrificing the depth, nuance, and follow-up probing that makes qual research valuable. It eliminates the false tradeoff between depth and sample size that has defined research for decades.

Traditional qualitative research forces a painful choice: depth or scale. You can interview 12 people deeply over 6 weeks, or survey 1,000 people superficially in a week. User Intuition eliminates this tradeoff entirely.

The AI conducts 30+ minute conversations with each participant using structured laddering methodology — probing 5-7 levels deep into motivations, emotions, and decision drivers. And it does this with hundreds of participants simultaneously, across any timezone, on any device.

The result isn't just more interviews. It's statistically meaningful qualitative data — enough conversations to identify patterns with confidence, segment findings by cohort, and make decisions with both the richness of qual and the confidence of quant.

How AI Makes Qualitative Research Scalable

AI-moderated interviews remove the human bottleneck that has always limited qualitative research. By conducting hundreds of deep conversations simultaneously — each with 30+ minutes of adaptive probing — teams get the rich, nuanced insights of qual research at sample sizes previously only possible with surveys.

Does scale sacrifice depth?

No. Every interview uses the same structured laddering methodology — 5-7 levels deep. The AI doesn't fatigue, doesn't skip probes, and doesn't develop confirmation bias. Interview #500 gets identical rigor to Interview #1.

How is this different from surveys?

Surveys ask fixed questions and accept surface-level answers. AI-moderated interviews adapt in real-time, follow unexpected threads, and probe until they reach root motivations. The depth difference is 5-7 levels vs. zero follow-up.

What sample sizes are possible?

200-300 conversations completed in 48-72 hours is typical. Studies can scale to 1,000+ interviews per week. Large enough to segment by cohort, geography, or behavior with statistical confidence.

The Qual Research Bottleneck

Qualitative research has been trapped in an artisanal model for decades. The constraints aren't methodological — they're operational.

1

Tiny Sample Sizes

Traditional qual studies interview 8-12 people. That's enough to generate hypotheses but not enough to validate them. Teams make million-dollar decisions on a handful of conversations.

2

Weeks of Lead Time

Recruiting, scheduling, moderating, and analyzing 12 interviews takes 4-6 weeks. By the time insights arrive, the product has shipped, the campaign has launched, and the decision window has closed.

3

Cost Limits Everything

At $15K-$27K per study, most teams can only afford a handful of qual projects per year. Agencies charge $50K+ for a single comprehensive study. So teams default to surveys — and miss the 'why.'

4

The Survey Fallback

When qual can't scale, teams substitute surveys. But 3% of devices now complete 19% of all surveys, and AI bots pass survey quality checks 99.8% of the time. The quantitative alternative is collapsing.

Results

Representative Outcomes

What teams measure after switching to AI-moderated research.

Conversations in 48-72 hrs
200-300

Enough to segment by cohort with confidence

Cost reduction
93-96%

vs. traditional qualitative research

Time reduction
95%

From 4-8 weeks to 48-72 hours

Levels of depth
5-7

Consistent laddering across every interview

Compare

Qual at Quant Scale vs. Traditional Qual
vs. Quantitative Surveys

Dimension Qual at Quant Scale (User Intuition) Traditional Qual Quantitative Surveys
Sample size 200-1,000+ per study 8-12 per study 1,000+ per study
Depth per response 5-7 levels of laddering 3-5 levels (varies) Surface-level, no follow-up
Time to insights 48-72 hours 4-8 weeks 1-2 weeks
Cost (20 participants) Starting from $200 $15,800-$27,200 $500-$2,000
Follow-up probing Dynamic, adaptive per response Depends on moderator None — static questions
Data quality AI + multi-layer fraud prevention High (but small n) Declining (bot contamination)
Segmentation confidence High (large n × deep data) Low (too few for subgroups) High on metrics, no 'why'
Richness of findings Emotions, motivations, verbatim Emotions, motivations, verbatim Percentages, ratings, rankings
Process

How Qual at Quant Scale Works

Design → AI interviews at scale → analyze patterns across conversations.

1
5 min

Design Your Study

Define objectives, screening criteria, and sample size. Use templates or our AI research agent. Choose voice, video, or chat modality.

2
48-72 hrs

AI Conducts Interviews at Scale

Hundreds of simultaneous 30+ minute conversations. Each interview probes 5-7 levels deep. Participants engage on any device, any timezone.

3
Seconds

Analyze Patterns Across Conversations

Themes, sentiment, competitive mentions, and recommendations — synthesized across your entire sample. Segment by cohort, geography, or behavior.

Use Cases

Every Solution Benefits from Scale

See how teams apply qualitative depth across research challenges.

Win-Loss Analysis

Scale buyer interviews across won and lost deals.

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Churn & Retention

Interview churned customers at volume to find patterns.

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Consumer Insights

Deep-dive into purchase motivations across segments.

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UX Research

Test prototypes with hundreds of users, not dozens.

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Concept Testing

Validate concepts with qual depth at quant sample sizes.

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Shopper Insights

Map shopper missions across demographics and channels.

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Explore all solutions →
Methodology & Trust

Rigor That Scales, Not Rigor That Breaks

The quality of qualitative research has always depended on the moderator. AI removes that variability — delivering consistent, enterprise-grade methodology across every conversation, whether you run 20 or 2,000.

Why AI Maintains Rigor at Scale

  • Identical laddering methodology for every interview
  • No fatigue — Interview #1,000 is as rigorous as #1
  • No confirmation bias or leading questions
  • Dynamic probing calibrated against research standards
  • Every finding includes evidence trails and verbatim citations
  • Methodology refined through Fortune 500 consulting engagements

What Makes This Different from 'AI Surveys'

  • 30+ minute adaptive conversations, not multiple-choice questions
  • 5-7 level laddering, not 'rate on a scale of 1-5'
  • Emotional signal detection and empathetic follow-up
  • Structured consumer ontology turns narratives into machine-readable insight
  • Multi-layer fraud prevention beyond what surveys can achieve
  • Results you can cite with confidence at board level

Research methodology derived from Fortune 500 consulting (McKinsey heritage).

FAQs

Frequently Asked Questions

Qualitative research at scale means conducting hundreds or thousands of in-depth, adaptive interviews simultaneously using AI moderation — without sacrificing the depth, nuance, and follow-up probing that defines qualitative methodology. It eliminates the historical tradeoff between rich insights and statistical confidence.
User Intuition typically completes 200-300 conversations in 48-72 hours and can scale to 1,000+ interviews per week. Each conversation is a full 30+ minute adaptive interview with 5-7 levels of laddering depth.
No. Every interview uses identical structured laddering methodology. The AI doesn't fatigue, doesn't develop confirmation bias, and doesn't skip probes. Interview quality is consistent whether you run 20 or 2,000 — something human moderation cannot achieve.
Focus groups suffer from groupthink, dominant voices, and social desirability bias. AI-moderated interviews are one-on-one conversations where participants share honest, unfiltered perspectives. You also get 5-7 levels of depth per person instead of surface-level group consensus.
User Intuition delivers 93-96% cost reduction compared to traditional qualitative research. A 20-participant study starts from as low as $200, compared to $15,800-$27,200 for traditional approaches. Scaling to hundreds of interviews costs a fraction of what a single traditional qual study costs.
Yes. The AI handles win-loss analysis, churn interviews, UX research, concept testing, shopper insights, and consumer research. For highly sensitive topics requiring real-time human emotional judgment or deep domain expertise, we recommend human moderation — and we're transparent about those boundaries.
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