운영·데이터

Measures employee satisfaction with facilities maintenance requests—from submission clarity to resolution speed—helping workplace operations teams identify service bottlenecks and communication gaps.

Measures employee-perceived usability, reliability, and efficiency of internal tools and workflows. Use with cross-functional teams to identify friction points and prioritize improvements.

For employees requesting to onboard a new vendor or supplier. Captures the business need, estimated spend, urgency, risk factors, and alternatives already considered so procurement and operations teams can triage requests quickly. The AI follow-up interview probes why existing approved vendors won't meet the need and surfaces risk details a form alone would miss.

Measures how easily analytics teams can find, evaluate, and trust data assets across the organization, revealing gaps in metadata, documentation, and lineage practices. Designed for data and analytics professionals; estimated 6–8 minutes.

Assesses how employees find, evaluate, and trust internal knowledge resources—measuring findability, content quality, ownership clarity, and tool effectiveness to prioritize knowledge management improvements.

Measures how employees experience IT support — ticket resolution speed, first-contact resolution, technician communication, and whether self-service resources actually deflect tickets. An AI follow-up reconstructs what really happened on the respondent's most recent ticket instead of a generalized impression, surfacing the specific breakdowns behind low satisfaction scores.

Measures how a vendor performs on quality, delivery, pricing, and support, plus renewal intent and what factors matter most in the relationship. Built for procurement and operations teams running periodic vendor reviews, with an AI follow-up interview that digs into the specific incident behind a low or high score instead of leaving it as a number.

Replace single-point guesses with honest uncertainty: experts build a probability distribution over outcomes instead of naming one number, and the AI interviewer elicits the assumptions behind their shape. Ideal for sales forecasts, launch estimates, and planning reviews.

Captures what went well, what broke, and why on a just-completed project, then surfaces the process changes the team would realistically adopt. An AI follow-up reconstructs the actual sequence of events behind the biggest setback instead of settling for a vague 'communication issues' answer. Built for project leads and PMOs running structured retros.

Measures consumer trust, noise tolerance, adoption likelihood, and barriers for retail drone delivery. Designed for market researchers evaluating residential readiness for aerial last-mile logistics.

Measures warehouse workers' safety perceptions, incident exposure, training adequacy, and improvement priorities related to collaborative robots. Designed for operations and safety teams managing human-cobot work environments.

Collects structured partner feedback on reliability, delivery, communication, and value over a 90-day period to generate actionable supplier performance scorecards for operations optimization.

Measures how much customers trust your supply chain claims, which transparency details (sourcing, labor, environmental impact, certifications) actually matter to them, and what they'd pay for verified information — with an AI follow-up that digs into the real story behind a specific trust or mistrust moment.

Evaluates how well your organization sources from certified diverse-owned suppliers — spend levels, barriers, and category priorities — for procurement and sourcing leaders. An AI follow-up interview reconstructs a real recent sourcing decision to surface what actually helped or blocked a diverse supplier from winning the business.

Measures employee perceptions of facility cleanliness, environmental comfort, and maintenance responsiveness over a 30-day recall period to help facility and operations teams identify and prioritize improvement areas.

Measures how people actually schedule, reschedule, and coordinate meetings today — time spent, satisfaction with current tools, and which features would save the most time. Built for product and operations teams evaluating or building meeting planning software. An AI follow-up interview reconstructs a specific recent scheduling breakdown instead of relying on a generic satisfaction score.

Measures employee satisfaction with HR service quality across responsiveness, clarity, and fairness dimensions. Designed for quarterly internal pulse checks to identify improvement priorities and track service trends.

Measures employee phishing detection confidence, reporting behavior, and security practices to identify organizational risk gaps and prioritize awareness training investments.

Assesses internal stakeholders' experiences with vendor onboarding speed, process friction, and compliance steps over the past 6 months to identify bottlenecks and prioritize process improvements.

직원들이 실제로 언제 회의에 참석할 수 있고 참석할 의향이 있는지, 회의 시간이 실제로 어떻게 배분되는지, 그리고 일정 관리의 마찰이 어디에서 집중 업무를 갉아먹는지를 파악합니다. AI 후속 인터뷰를 통해 최근 잘못 진행된 특정 회의 하나와 그것을 개선할 수 있었던 방법을 심층적으로 파헤칩니다. 회의 문화를 점검하는 운영, IT, 인사 팀을 위해 제작되었습니다.