Industry-specific survey templates

A structured intake for auditor candidates that captures experience level, certifications, technical proficiency, and role priorities, then uses an AI follow-up interview to probe a real audit challenge the candidate has handled. Built for audit firm recruiters and hiring managers who need comparable, behavior-based screening data before a live interview.

Captures how audiences rate a movie's story, acting, visuals, and pacing right after watching it, plus their likelihood to recommend it — with an AI follow-up that digs into the specific scene or moment that actually shaped their overall opinion. Built for studios, distributors, and festivals collecting post-screening feedback.

Captures how a group or team performed on a shared project — coordination, workload fairness, communication, and overall output quality — for managers, educators, or project leads reviewing team dynamics after a collaboration ends. The AI follow-up interview digs into the specific moment or decision that most helped or hurt the group's performance, going beyond generic ratings.

Captures how smooth or frustrating candidates found the process of scheduling their job interview — from first contact to joining the call or arriving on-site. Built for talent acquisition and recruiting ops teams who want to spot friction before it costs them strong candidates, with an AI follow-up that digs into the specific moment scheduling went wrong (or right).

Measures how well a mentorship relationship is working for the mentee — guidance quality, meeting consistency, and career impact — with an AI follow-up that digs into a specific moment mentorship helped or fell short, revealing what to fix before the next matching cycle.

A pre-appointment intake for mental health practices that captures presenting concerns, symptom frequency over the last two weeks, treatment history, and safety screening before the first visit — with an AI follow-up interview that reconstructs the story behind the symptoms (onset, triggers, what's changed) so clinicians walk in with context instead of a blank checklist.

A structured self-evaluation for performance review cycles, covering goal achievement, competency ratings, challenges, and development priorities. An AI follow-up interview digs into the specific evidence behind the employee's self-rating instead of accepting vague self-praise or self-criticism at face value. Built for HR and people managers running review or check-in cycles.

Measures how satisfied sponsors and partners are with the visibility, value, and support they received — covering perceived ROI, benefit prioritization, and renewal intent — with an AI follow-up that surfaces the specific reasons behind a sponsor's likelihood to renew or recommend, beyond a single satisfaction score.

Captures how guests experienced arrival and check-in — speed, staff interaction, and technology options — for hotels, resorts, and boutique properties. The AI follow-up interview digs into any friction points guests mention, reconstructing exactly what slowed them down or went wrong so front-of-house teams can fix root causes, not just symptoms.

Assesses core work-style traits—organization, sociability, adaptability, decision-making, and stress response—through rating scales, a values ranking, and a trade-off allocation exercise. Built for hiring, team formation, or coaching contexts. An AI follow-up interview asks about one real recent situation to check whether stated preferences match actual behavior under pressure.

A flexible opinion-poll template for gauging where people stand on a policy, event, or public issue — covering awareness, position, reasoning, and priority concerns. An AI follow-up interview digs into the personal experience or belief behind each respondent's stance instead of just recording a for/against label. Swap in your specific issue and options before launch.

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.

A structured intake tool for social workers and case managers to capture a new client's presenting concerns, living situation, safety, and support network at first contact. An AI follow-up interview digs into the client's most urgent need in concrete detail so staff can prioritize referrals and flag anything requiring immediate safety follow-up.

Captures how brokers experience working with your carrier, MGA, or brokerage — quoting speed, commissions, support, and technology — for teams managing broker partnerships. An AI follow-up interview digs into the specific reason behind a broker's lowest rating or recommendation score, surfacing the friction that's actually costing you placed business.

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 flexible feedback survey for workshops, trainings, retreats, or team activities — covering attendance, facilitator quality, content relevance, and likelihood to recommend, plus an AI follow-up that digs into the specific moment behind each recommendation score so organizers know exactly what to fix or repeat.

A modernized version of the classic Proust Questionnaire — used by media teams, HR and culture leads, and creative agencies to build candid personality profiles for onboarding pages, podcast intros, or team retrospectives. Closed questions capture the classic prompts verbatim; the AI follow-up interview digs into whichever answer sounds most emotionally loaded to surface the real story behind it.

Gathers structured, behavior-specific feedback from colleagues who watched a presentation — covering clarity, delivery, visuals, pacing, and Q&A — plus a trade-off ranking of what most needs work. An AI follow-up interview digs into the specific moment that stood out, turning vague 'good job' feedback into a concrete example the presenter can act on.

A pre-first-session intake for therapy and counseling practices, covering presenting concerns, symptom frequency, daily-life impact, treatment history, and goals for care. An AI follow-up interview explores the story behind the client's main concern — what changed recently, what they've tried, and what a good outcome looks like — so clinicians arrive at session one already oriented.

Measures how employees actually experience company culture — psychological safety, values alignment, manager reinforcement, and day-to-day behavior — not just stated policy. An AI follow-up interview digs into the story behind the most telling rating, surfacing specific moments that a scale alone can't capture.

Captures how customers or clients actually experience your product or service quality — defect frequency, consistency, and the real cost of quality failures — with an AI follow-up that digs into the root cause and impact of their most significant quality issue instead of a generic complaint.

Captures a reader's honest reaction to a specific book — pacing, characters, writing quality, and overall satisfaction — plus an AI follow-up that digs into the moment or decision that actually shaped their rating. Built for publishers, authors, and book clubs who want more than a star rating.

Measures whether a coaching engagement actually moved the needle — session consistency, coach behaviors, goal progress, and likelihood to continue or recommend — for coaching practices and internal L&D teams evaluating a coach or program. An AI follow-up interview digs into the specific breakthrough moment and the biggest gap the coach didn't address.

Captures how job candidates experienced your recruiting and interview process, from job posting through offer or rejection, so talent acquisition teams can spot friction points. An AI follow-up interview reconstructs the specific moment that shaped the candidate's overall impression, surfacing concrete fixes that satisfaction scores alone miss.

Tracks how candidates experienced communication, status visibility, and tools throughout your hiring pipeline, from application to final decision. Built for talent acquisition teams auditing their applicant tracking process, with an AI follow-up that digs into the specific moment communication broke down for lower-scoring candidates.

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.

Captures how patients or clients experienced the nursing care they received — communication, responsiveness, pain management, and discharge preparation — for hospitals, clinics, and home health teams. An AI follow-up interview reconstructs the specific moment that most shaped their overall rating, surfacing concrete coaching moments beyond a satisfaction score.

Identifies which character strengths employees draw on most, how often they get to use them day to day, and what gets in the way of strengths-based work. Built for HR, L&D, and coaching teams running strengths-based development programs — the AI follow-up interview digs into a specific recent moment someone used their top strength, turning self-report scores into concrete, usable examples.

A pre-visit intake for chiropractic practices that captures the presenting complaint, pain history, functional impact, and safety-relevant health background — plus an AI follow-up interview that reconstructs the mechanism of injury and treatment history in the patient's own words before they ever sit in the chair.

Captures structured feedback from interviewers right after a candidate interview — competency ratings, hiring recommendation, and top strengths — plus an AI follow-up that pulls concrete evidence and specific moments behind the recommendation instead of just a gut-feel score. Built for hiring managers and recruiting teams standardizing interviewer feedback across panels.

Measures how residents experience day-to-day property management — maintenance responsiveness, communication, common-area upkeep, and value for rent — plus renewal likelihood. An AI follow-up interview digs into the real story behind their most recent maintenance request or renewal decision, surfacing specifics that closed-ended ratings miss.

Captures how sellers experienced the pre-market or 'coming soon' phase before their home went live on the open MLS — buyer interest, pricing signals, and agent support — with an AI follow-up that reconstructs why offers did or didn't materialize during that window. Built for real estate agents and brokerages deciding whether to keep offering a pre-market strategy.

Gathers employee preferences on format, timing, budget priorities, and inclusivity for your company holiday celebration, plus a rating of intent to attend this year. An AI follow-up interview digs into why people would or wouldn't show up, so planners can fix the real barriers instead of guessing.

Captures how participants experienced a workshop, training, or program — satisfaction, perceived value of specific sessions, and likelihood to return — paired with an AI follow-up that digs into the reasoning behind their ratings so organizers know exactly what to keep, fix, or cut next time.

Evaluates lab staff's confidence and proficiency across core bench skills, SOP adherence, and equipment troubleshooting, with an AI follow-up that reconstructs a specific recent error or near-miss to surface real training gaps behind the self-ratings. Built for lab managers and QA leads running periodic competency reviews.

Captures a new fitness client's goals, exercise history, health considerations, and scheduling needs before their first session, with an AI follow-up that uncovers what derailed past attempts and what accountability will make this program stick. Built for personal trainers, gyms, and coaching apps onboarding new clients.

A structured scorecard for coaches and scouts to rate a player's hitting, fielding, throwing, speed, and coachability right after a tryout, plus a roster recommendation. An AI follow-up interview digs into the specific plays or moments behind that recommendation so the reasoning isn't lost once the tryout ends.

Captures how attendees rate a specific speaker or session — clarity, pacing, expertise, and Q&A handling — plus an AI follow-up that digs into the moment that most shaped their opinion. Built for event organizers, training teams, and internal speaker programs who need actionable feedback beyond a star rating.

Measures how shoppers actually experience your grocery store — shopping frequency, stock availability, checkout, staff, and pricing — and prioritizes which fixes matter most. An AI follow-up interview reconstructs the real story behind a shopper's most recent trip instead of settling for a bare rating.

Tracks how patients felt in the days after a vaccination — side effect severity, how well staff set expectations, and overall satisfaction — for clinics, pharmacies, and health systems monitoring vaccine programs. An AI follow-up interview reconstructs what actually happened physically and logistically for patients with rougher experiences, going beyond a checkbox symptom list.

Measures how patrons rate the library's collection, staff, spaces, and digital resources, and which improvements would matter most, with an AI follow-up that digs into the real story behind their satisfaction score. Built for public and academic libraries planning budgets, hours, or renovations.

Maps the full vehicle-purchase journey — research channel, decision factors, financing experience, and post-purchase satisfaction — for dealerships, manufacturers, and auto marketplaces. An AI follow-up interview reconstructs the actual negotiation and channel story behind the numbers, not just the checkbox answer.

Measures how satisfied partners are with communication, intimacy, trust, and shared responsibilities in their relationship, with an AI follow-up that digs into the specific moment or pattern behind their lowest-rated area rather than settling for a generic label.

A pre-consultation intake for body contouring and non-surgical fat reduction clients, covering treatment areas of interest, goals, health history flags, and expectations. An AI follow-up interview explores what's driving the request and probes any flagged health history so providers walk into the consultation prepared, not surprised.

Captures how a presentation, talk, or webinar landed with its audience — clarity, pacing, relevance, and delivery — for presenters, event teams, and internal comms who want to improve the next session. An AI follow-up digs into the exact moment attention was lost or a point clicked, going beyond generic satisfaction scores.

Captures the dietary restrictions, allergies, and intolerances your customers or diners live with, how strictly they follow them, and where current food options fall short — with an AI follow-up that digs into a real recent incident where those needs weren't met and what would have prevented it.

Measures how disagreements and tensions actually get surfaced, escalated, and resolved on a team — covering frequency, fairness, psychological safety, and preferred resolution paths. Built for HR and people leaders; an AI follow-up interview reconstructs one real, recent conflict in detail instead of relying on abstract ratings alone.

Captures how clients experience individual coaching sessions — perceived progress, coach effectiveness across key behaviors, and likelihood to continue or recommend. An AI follow-up interview digs into the specific moment behind their recommendation score, surfacing what actually drove or undermined value in the session.

A structured post-lesson observation form for instructional coaches, administrators, and peer observers to rate teaching practices, student engagement, and instructional strategy — with an AI follow-up interview that reconstructs the specific moments behind the ratings so feedback is concrete enough to coach from.

Captures how confidently people managing food allergies navigate menus, ingredient labels, and staff interactions at restaurants and food brands — and where that trust breaks down. An AI follow-up interview reconstructs their most recent close call or unclear-labeling experience in detail. Built for restaurants, food manufacturers, and allergy-focused brands.