User Intuition
AI-moderated interviews for customer intelligence
User Intuition is an AI-moderated research platform that conducts natural voice, video, and chat interviews to uncover why customers buy, leave, switch, and stay. The platform uses structured laddering methodology to probe 5-7 levels deep into participant responses, delivering enterprise-grade qualitative insights in 48-72 hours at a 93-96% cost reduction compared to traditional qualitative research. Every conversation builds a permanent, searchable Customer Intelligence Hub -- delivering compounding institutional knowledge rather than one-off reports that disappear into slide decks.
The platform was built to solve a systemic intelligence-infrastructure failure: companies have more customer data than ever but less understanding of what drives decisions. An estimated 30-40% of online survey data is now compromised by AI bots and professional survey-takers, while over 90% of valid insights disappear within 90 days. User Intuition replaces this broken model with continuous, verified, compounding consumer intelligence grounded in real conversations -- not surveys, not synthetic personas, not LLM-hallucinated data.
Teams source participants flexibly: import their own customer lists from CRM for deep experiential insight, recruit from a global vetted panel of 4M+ consumers for independent validation, or blend both in a single study. Multi-layer fraud prevention (bot detection, duplicate suppression, professional respondent filtering) ensures data quality regardless of source. The AI interviewer adapts dynamically to each participant -- pursuing unexpected threads, following up on emotional signals, and calibrating probing depth based on the richness of each response.
Features
AI-Moderated Interviews
The methodology engine. Live voice, video, and chat conversations that probe 5-7 levels deep using structured laddering methodology refined through Fortune 500 consulting engagements (McKinsey heritage). The AI adapts dynamically -- it pursues unexpected threads, follows up on emotional signals, and calibrates probing depth per response. It does not follow a static script. Supports three modalities (voice, video with screen-sharing, and chat) so participants engage in whatever format is natural, on any device, at any time, across any timezone. Delivers consistent depth across hundreds of interviews without fatigue, bias, or leading questions. 98% participant satisfaction because participants feel less social pressure and judgment.
Qual at Quant Scale
The scale engine. Run 1,000+ in-depth interviews per week with AI moderation. Every conversation goes 5-7 levels deep using structured laddering -- giving statistically meaningful qualitative data in days, not months. Eliminates the historical false tradeoff between depth and sample size. 200-300 conversations in 48-72 hours is typical; can scale to 1,000+ per week. Large enough to segment by cohort, geography, or behavior with statistical confidence. Interview #500 gets identical rigor to Interview #1. 95% time reduction (from 4-8 weeks to 48-72 hours). Research methodology derived from Fortune 500 consulting (McKinsey heritage).
Customer Intelligence Hub
The compounding knowledge system. Every conversation passes through a multi-stage pipeline: intent extraction, emotional scoring, competitive detection, jobs-to-be-done mapping, and evidence-based synthesis. Raw narratives become structured, machine-readable insight via a proprietary consumer ontology (e.g., "The checkout made me panic" becomes {Emotion: Anxiety, Trigger: Checkout Friction, Competitive Reference: Amazon}). Teams can query the hub conversationally across all historical research. Cross-study pattern recognition surfaces trends no single study could reveal. Every finding traces back to specific verbatim quotes from real participants -- explainable, auditable, and commercially defensible. Connects via MCP (Model Context Protocol) and API to ChatGPT, Claude, data warehouses, CRMs, and automation platforms.
Use Cases
- Consumer Insights
- Market Intelligence
- Shopper Insights
- Brand Health Tracking
- Concept Testing
- Product Innovation Research
- Win-Loss Analysis
- Churn & Retention Research
- UX Research
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