직원·HR

Tracks overall staff satisfaction, satisfaction across key workplace dimensions, and early flight-risk signals, then uses an AI follow-up interview to uncover the specific reasons behind low scores or turnover intent. Built for HR and people teams running quarterly or annual pulse checks.

A pulse-style survey that measures trust, workload sustainability, manager support, and overall satisfaction across your workforce, then uses an AI follow-up interview to surface the specific story behind each person's lowest-rated area. Built for HR and people teams running quarterly or annual climate checks.

A structured self-assessment for performance review cycles, combining competency self-ratings, goal progress, and development priorities with an AI follow-up that draws out the specific evidence behind an employee's accomplishments and challenges. Built for HR teams and managers running annual or quarterly review processes.

Measures how valued employees feel, which recognition channels actually land, and how often meaningful appreciation happens day to day. Includes a best-worst trade-off on recognition types and an AI follow-up interview that digs into the specific moment recognition worked (or fell flat) instead of generic satisfaction scores. Built for HR and people teams auditing or redesigning a recognition program.

A structured pulse survey for fintech organizations measuring intent to leave, retention drivers, compensation fairness, manager effectiveness, and psychological safety to identify and mitigate turnover risk across teams and levels.

Tracks how employees experience the process of raising issues with HR — from reporting channel and response speed to fairness, confidentiality, and final resolution. Built for HR and People Ops teams auditing their issue-handling process, with an AI follow-up interview that reconstructs exactly what happened in a specific case instead of relying on averaged satisfaction scores.

Measures how emotionally invested, obligated, or 'stuck' employees feel toward the organization, plus their real retention risk and the specific factors that would deepen commitment. The AI follow-up interview probes the story behind each person's score instead of settling for a number.

Measures employee comfort, trust, privacy concerns, and usability experiences with biometric time clocks to inform workplace adoption strategies and data governance policies.

직원 표창 또는 시상 프로그램을 위해 각 추천의 구체적인 사연을 포함하여 상세한 행동 기반 후보 추천을 수집합니다 — 인사팀과 표창 위원회를 위한 것입니다. AI 후속 인터뷰는 추천인이 제시한 구체적 사례를 깊이 파고들어, 체크박스 양식만으로는 놓칠 수 있는 영향력과 인용 표현을 이끌어냅니다.

신규 사용자의 1~7일차 인상을 수집하여 활성화와 유지에 영향을 미치는 온보딩 가치 순간, 마찰 지점, 지원 격차를 파악합니다.

Captures how employees actually use the internet, the company intranet, and internal web tools during the workday — device mix, frequency, pain points, and satisfaction — alongside role and tenure demographics. An AI follow-up interview digs into the specific friction points behind low satisfaction scores so IT and HR can prioritize fixes to the digital workplace.

Measures employee perceptions of objectivity, clarity, and potential bias in formal performance reviews. Designed for HR teams seeking to identify systemic issues and improve review processes.

Measures employee adoption rates, perceived impact, barriers, and enablers of AI coaching tools for writing, communication, and meetings. Designed for HR and L&D teams evaluating AI coaching ROI and rollout strategy.

매장 최일선 직원의 몰입도(근무 일정의 공정성, 관리자 지원, 인정, 인력 배치, 승진 기회)와 직원 추천 점수를 측정하여 조직 문화의 건강도를 벤치마킹합니다. AI 후속 인터뷰가 낮은 점수와 이직 위험의 실제 이면을 파고들어, 폐쇄형 질문이 놓치는 구체적인 내용을 드러냅니다. 소매 HR 및 매장 운영 리더를 위해 설계되었습니다.

Measures how safe employees feel day-to-day, how strong your safety culture and reporting habits actually are, and what stops people from speaking up — with an AI follow-up that reconstructs a real near-miss or incident and what would change how it's handled next time.

A structured self-assessment for performance review cycles, covering goal progress, core competencies, manager support, and development priorities — paired with an AI follow-up interview that digs into the story behind the ratings, not just the numbers, for richer 1:1 prep and calibration.

Captures what drew a new hire to accept the offer, how the first weeks stack up against what was promised, and what would keep them long-term. Built for HR and people teams running structured 30/60/90-day check-ins, with an AI follow-up that digs into the specific moment expectations and reality diverged.

직원들이 직장에서 실제로 얼마나 행복하다고 느끼는지, 그 감정을 이끄는 요인이 무엇인지, 그리고 어디에서 그 행복이 흔들릴 위험이 있는지를 점검하는 펄스 체크입니다. 정기적인 정서 점검을 진행하는 HR 및 피플팀을 위해 만들어졌습니다. AI 후속 인터뷰가 각 응답자의 평가 뒤에 있는 구체적인 순간을 파고들어, 낮은 점수에는 단순한 숫자가 아닌 실제 사연이 담기게 됩니다.

Measures how employees experience or witness racism and racial bias at work, how safe they feel raising concerns, and whether leadership follows through on accountability. An AI follow-up interview probes the real story behind low safety ratings or unreported incidents, surfacing details a scale score alone can't capture.

신규 사용자가 가입 및 온보딩 과정에서 어디서, 왜 멈추는지를 진단합니다. 최근 가입자를 대상으로 활성화를 저해하는 마찰 지점, 부족한 신뢰 신호, 가치 실현 시간(time-to-value) 장벽을 파악하는 데 활용하십시오.