운영·데이터

Measures employee preparedness for internal audits, identifies gaps in guidance, tooling, and training, and surfaces priority areas for process improvement. Designed for organizations seeking to standardize audit readiness across departments.

Measures how thoroughly people prepare for meetings they lead and attend — agendas, objectives, materials, and roles — and where preparation habits break down. An AI follow-up interview reconstructs a specific under-prepared meeting to surface the real friction behind missed prep steps, for operations teams trying to improve meeting quality.

Assesses the operational effort, data challenges, and improvement priorities suppliers face during customer-requested ESG audits. Designed for manufacturing and supply chain professionals involved in ESG compliance processes.

Measures how well meetings are planned, run, and followed up on — cadence, agenda discipline, and the biggest time-wasters — for teams and ops leaders auditing their meeting culture. An AI follow-up interview reconstructs a recent unproductive meeting to surface the specific root cause behind the numbers.

Measures customer satisfaction across three core last-mile dimensions—tracking accuracy, delivery timing, and handoff quality—to identify specific improvement priorities for retail delivery operations.

Measures adoption rates, usability barriers, perceived safety outcomes, and data trust related to IoT wearables and site sensors among construction workers. Designed for safety managers and operations teams evaluating wearable technology programs on active job sites.

Assesses how ready employees are for digital transformation across tool adoption willingness, skill confidence, process pain points, and change fatigue. Built for operations, IT, and transformation leaders sequencing a rollout. An AI follow-up digs into the specific reason behind hesitation or exhaustion that scale scores alone can't explain.

Pinpoints which internal processes waste the most employee time, how often people route around official steps with workarounds, and which tasks are the best candidates for automation. Built for ops, IT, or PMO teams doing a process-improvement audit; the AI follow-up reconstructs the actual workaround steps behind the numbers so you know exactly what to fix first.

Measures how predictable, swappable, and fair hourly employees find their work schedules, plus early burnout signals like clopening shifts and exhaustion. Includes an AI follow-up that reconstructs a specific recent scheduling incident — a denied swap or a last-minute change — instead of relying on general impressions.