Product & UX

Identifies onboarding friction points and time-to-value barriers in e-commerce apps. Use with new and recent users to pinpoint what accelerates or delays activation and first purchase.

Evaluates how users perceive and navigate website menu structures, measuring label clarity, mental models of information architecture, and findability of key content areas. Ideal for UX researchers and product teams validating navigation designs.

Measures new users' first-session experience, onboarding guidance effectiveness, and time-to-value discovery to identify activation friction and improve early retention.

Measures user satisfaction with push notification experiences across timing, frequency, perceived control, and content value. Designed for product and UX teams optimizing notification strategies for mobile or web applications.

Structured stakeholder or customer feedback on a design, mockup, or creative asset: first impressions, a click on what draws attention, ratings against the brief, and file upload for marked-up versions. The AI interviewer converts 'I just don't like it' into usable direction.

Measures how first-time users perceive, interact with, and evaluate pre-filled default settings during product onboarding. Use this to identify friction points and optimize the first-run setup experience.

Measures mobile commerce app usability across navigation, task ease, and checkout experience to identify friction points and prioritize UX improvements. Designed for current app users who have shopped within the past 30 days.

Captures user preferences for notification channels, frequency, granularity, default settings, and control tools across apps and devices. Designed for product teams seeking to optimize notification strategies, improve opt-in rates, and reduce notification-driven churn.

Measures how easily visitors accomplish what they came to do on your website, how the site rates on speed, navigation, design, and trust, and how likely they are to recommend it — with an AI follow-up that digs into the exact moment things got confusing or broke down. Built for product, UX, and marketing teams running periodic site health checks.

Evaluates the usability, transparency, and perceived compliance of email unsubscribe flows from the subscriber's perspective. Use with general consumers to benchmark opt-out friction, identify process pain points, and assess trust impact.

Captures how players actually experience difficulty tuning, progression pacing, monetization fairness, and moment-to-moment session enjoyment. Built for game designers and live-ops teams tracking post-launch sentiment, with an AI follow-up that digs into the specific moment a player felt cheated by difficulty or monetization instead of a generic complaint.

Measures user expectations and pain points for tab bars, gestures, menus, and feature discoverability in mobile apps. Designed for UX researchers seeking actionable insights to optimize mobile navigation patterns.

Measures user perceptions of map/GPS accuracy, update timeliness, and improvement priorities in navigation apps. Designed for product and UX teams seeking to benchmark trust, identify pain points, and prioritize fixes.

Measures how satisfied users are with your app, where they hit friction, and which improvements matter most — for product and mobile teams tracking release health. An AI follow-up interview digs into the story behind each user's satisfaction score, surfacing the specific moment or feature that shaped it.

Where do people click first when given a task on your design? First-click success is one of the strongest predictors of task completion. Upload your screenshot, set the task, and let the AI interviewer ask what they expected to happen — turning a click map into a usability diagnosis.

Identifies what actually pushes users toward 5-star versus 1-star app store reviews — crashes, speed, ads, pricing, support — and what specifically would change their rating. Built for product and growth teams; the AI follow-up reconstructs the real incident behind each respondent's rating instead of generic complaints.