Industry-Specific

Measures how project teams actually use their construction project extranet for document control, RFIs, and drawing markup, and where version confusion or slow approvals cost time. An AI follow-up interview reconstructs a specific recent incident to surface the real workflow friction behind the ratings.

Uncovers which retirement housing features — cost, location, care access, privacy, community life — actually drive decisions, using trade-off exercises and price sensitivity questions instead of simple ratings. Built for senior living operators, developers, and planners, with an AI follow-up that probes what respondents would sacrifice to secure their top priority.

Qualifies and recruits candidates for a focus group by screening for category usage, conflicts of interest, prior research participation, and scheduling fit, then capturing core demographics. An AI follow-up interview asks candidates to describe their real experience with the topic in their own words, surfacing rehearsed 'professional respondent' answers and gauging how articulate and engaged they'll be in the room.

A guessing-game survey for partners, family, friends, or teammates to test how accurately they can predict someone's habits, preferences, and values — with an AI follow-up that unpacks the reasoning behind their most confident (and shakiest) guesses. Built to be customized with the actual subject's traits before sending.

Captures how shoppers choose, evaluate, and switch between grocery stores and channels — price sensitivity, loyalty behavior, and channel mix — for retail and CPG researchers. An AI follow-up interview digs into the real story behind a shopper's most recent store choice or channel switch, beyond what a closed question can capture.

Benchmarks your store against named competitors on price, selection, service, and convenience, then uses an AI follow-up to reconstruct the specific moment a shopper chose one store over another. Built for retail insights teams tracking share-of-wallet and competitive positioning.

Measures how often people actually buy organic food, which certifications and claims really drive the decision, trust in organic labeling, and price sensitivity — with an AI follow-up interview that reconstructs the real story behind a recent organic purchase (or skip) instead of a generalized attitude.

A relationship health check that scores satisfaction, trust, intimacy, communication, and love languages, then uses an AI follow-up interview to dig into whichever dimension scored lowest. Built for dating apps, matchmakers, and relationship coaches who need more than a vague compatibility score.

Uncovers what actually drives shoppers to pick and stay loyal to a supermarket — price, freshness, convenience, or rewards — using a best-worst trade-off exercise, a satisfaction battery, and an AI follow-up interview that reconstructs the real story behind a recent switch or near-switch. Built for grocery retail and category insights teams.

Measures how satisfied employees are with the substance of their job — the tasks, autonomy, variety, skill fit, and workload — separate from pay or manager quality. Built for HR and people-ops teams running role-design reviews, with an AI follow-up that digs into the specific task or moment behind their lowest-rated aspect of the work.

Measures how often employees experience or witness gender-based discrimination — in pay, promotion, meetings, and everyday treatment — and how safe they feel speaking up. An AI follow-up interview reconstructs a specific incident in the respondent's own words, revealing what actually happened and what response would have felt fair, for HR and DEI teams benchmarking climate and prioritizing fixes.

Maps how shoppers browse, search, and decide across in-store, online, and marketplace channels — where product searches succeed or break down, which purchase factors carry the most weight, and what an AI follow-up interview uncovers about the last time someone couldn't find what they wanted. Built for retail, e-commerce, and merchandising teams optimizing search, navigation, and discovery.

Measures how productive, focused, and supported employees feel while working remotely, and pinpoints the specific barriers eating into their day. An AI follow-up interview digs into the single biggest barrier each person identifies, reconstructing a real recent workday instead of relying on generalized complaints.

A point-factor job evaluation questionnaire for HR and compensation teams grading or re-grading a role — covering decision authority, supervisory scope, effort, and working conditions, with an AI follow-up that pressure-tests the scoring against a real high-stakes task the job has actually handled.

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.

Surfaces what people genuinely value most — career, family, health, growth, community, freedom — and how closely their daily choices match those priorities. Useful for coaches, HR teams, and researchers studying wellbeing or culture fit. The AI follow-up interview digs into the story behind a respondent's top value and any gap between stated priorities and lived time.

Assesses how prepared people actually felt, what emergency communications and procedures worked, and where gaps showed up during a real event or drill. Built for emergency management teams, facilities, and HSE departments benchmarking readiness, with an AI follow-up that reconstructs exactly what happened during the respondent's most recent disaster or drill instead of relying on general perceptions.

Measures how students experience a professor's teaching across clarity, engagement, feedback, availability, and fairness, plus overall satisfaction and likelihood to recommend. Built for academic departments and course coordinators running end-of-term reviews, with an AI follow-up interview that digs into the specific moment or assignment that most shaped a student's rating.

Captures how patients experience a physician practice end-to-end — scheduling ease, wait times, physician communication, and likelihood to recommend — with an AI follow-up that digs into the 'why' behind recommendation scores, especially from patients who were lukewarm or dissatisfied. Built for practice managers and physician groups tracking patient experience.

Measures how often people drink energy drinks, which brands and formats they choose, what drives that choice, and what they'd pay for a new product. Built for beverage brand and category teams, with an AI follow-up interview that reconstructs the real story behind a recent brand switch or purchase decision instead of relying on stated preference alone.