산업별

Measures how employees experience booking, policy, and support for company travel — flights, hotels, expense reimbursement, and duty of care — with an AI follow-up that reconstructs exactly what went wrong on a recent trip instead of a vague complaint. Built for HR, travel management, and finance teams evaluating their travel program.

A flexible agree-disagree battery for measuring attitudes toward a product, policy, workplace practice, or service — built around a classic multi-statement rating grid. Swap in your own statements, then let the AI follow-up dig into the reasoning behind whichever statement swayed the respondent's opinion most, closed or neutral answers included.

Captures how graduating students rate their academics, career readiness, and campus life as they head out the door, plus their post-graduation plans and likelihood to recommend the school. An AI follow-up interview digs into the specific moment or decision behind their lowest-rated aspect instead of settling for a generic score. Built for institutional research and student affairs teams running exit polls each term.

Captures how clients experienced their initial consultation after an accident — clarity of legal or claims guidance, how well they felt heard, and whether they understood next steps. An AI follow-up interview digs into the specific moment that shaped their trust (or doubt) in the advice they received, going beyond a satisfaction score to what actually happened in the room.

Captures how satisfied customers are with a recent vehicle service or repair visit — covering communication, wait time accuracy, price transparency, and repair quality — plus an AI follow-up that reconstructs what really happened when an issue wasn't fixed the first time. Built for dealership service departments and independent repair shops tracking retention and trust.

Captures how clear, well-paced, and engaging a lecture was, plus which specific aspects (pacing, examples, Q&A, materials) students most want improved. An AI follow-up interview digs into a concrete moment the student found confusing or engaging, turning vague 'it was fine' ratings into specifics an instructor can act on.

Gauges why professionals are weighing whether to register for your seminar, which session topics and formats matter most, and what price feels fair — built for event, association, and marketing teams planning a conference, workshop, or webinar series. An AI follow-up interview digs into the real reason a likely-to-attend score is high or low, beyond a checkbox answer.

For tour operators and travel agencies to evaluate a specific guided tour or package — covering guide performance, itinerary pacing, value for money, and likelihood to recommend. An AI follow-up interview reconstructs the specific moment behind a traveler's score instead of settling for a generic rating.

Captures how residents really feel about safety, upkeep, amenities, and community spirit where they live, with an AI follow-up that digs into the one issue affecting quality of life most. Built for city planners, HOAs, and community organizations tracking neighborhood health over time.

Measures renewal intent, experience satisfaction, value drivers, price sensitivity, and barriers to renewal among current season ticket holders to inform retention strategy.

Measures awareness, support, and concerns among residents near a wind energy project — covering noise, visual impact, trust in the developer, and perceived local benefits. An AI follow-up interview digs into the real reason behind each respondent's level of support, giving developers and utilities the qualitative context behind the numbers before community meetings or permitting decisions.

A structured intake tool for clinicians, counselors, and case managers that maps a client's physical health, mood and coping patterns, and social/environmental context into one whole-person profile. An AI follow-up interview explores the client's self-identified top stressor in their own words, surfacing how biological, psychological, and social factors interact in daily life.

A pre-appointment intake for lash extension clients that captures allergy risk factors, eye health flags, style goals, and lifestyle habits that affect retention. Built for lash studios and freelance lash artists to prep safely and customize the service. The AI follow-up interview digs into the client's past lash experiences and true expectations so artists can set realistic looks and avoid repeat reactions.

Measures attendee satisfaction, engagement, and experience quality across venue, broadcast, and activities at gaming and esports events to identify improvement priorities for future events.

Measures how satisfied recent adopters are with your shelter or rescue's adoption process, pet-matching accuracy, and post-adoption support — for animal welfare organizations wanting to improve placement quality. An AI follow-up interview digs into the specific moment where expectations about the pet's health, temperament, or care needs diverged from reality.

Measures how readers or players experienced a branching narrative — engagement, satisfaction with the choices offered, and pull toward replaying alternate paths. Built for interactive fiction studios, narrative game teams, and branching-content publishers, with an AI follow-up that reconstructs a specific choice moment to reveal whether decisions actually felt like they mattered.

Explores how travelers choose, book, and experience vacation trips — from destination trade-offs and price sensitivity to satisfaction with the actual experience. An AI follow-up interview digs into the real story behind a recent trip, surfacing moments of friction or delight that closed-ended ratings miss. Built for travel brands, tour operators, and hospitality teams.

Captures how clients found, evaluated, and booked a photography session — from discovery channel and pricing perception to friction points in scheduling and contracts. An AI follow-up interview reconstructs the actual decision journey, surfacing what nearly made a client choose someone else and where the booking process could lose future bookings.

Captures how applicants experienced your grant application process — from finding guidelines to hearing back on a decision — for foundations, government agencies, and grant-making bodies. An AI follow-up interview digs into the exact stage that caused the most friction, so program teams know precisely what to fix before the next funding cycle.

Measures how donors, volunteers, board members, and program participants feel about a non-profit's transparency, impact, and communication — with an AI follow-up that digs into the real reasons behind loyalty or hesitation instead of surface-level satisfaction scores. Built for development, program, and communications teams.