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In daily attendance management, more organizations rely on photo clock-in to verify that employees actually arrive on site or complete store-visit tasks. In practice, however, common issues occur:
  • Employees upload invalid images such as ceilings, floors, black screens, or blank walls.
  • Photos do not include a face, making it impossible to prove the employee was present.
  • Store-visit photos of shelves or displays are blurry or taken from the wrong angle, failing to reflect the actual situation.
  • Special role requirements (such as wearing a safety helmet, reflective vest, or compliant makeup) lack effective inspection methods.
  • Admins must manually review hundreds or thousands of photos one by one, which is time-consuming and inefficient.

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Product solution

To tackle these management challenges, DingTalk offers AI-powered detection for photo clock-in errors. By combining large models with image understanding, it automatically identifies non-compliant clock-in behavior, enabling Organization managers to supervise remotely with efficiency and precision.

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Key feature highlights

Invalid image detection

Automatically detects abnormal photos such as black screens, blank screens, ceilings, and floors.

Facial recognition check

Determines whether the photo contains a face.

Error notification mechanism

Automatically generates Error reports and pushes them to Admins for handling.

Statistics visualization Kanban

Provides an overview of photo quality Data in the attendance Admin Console.

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Feature details and access path

Clock-in photo statistics exampleError detection results example
Access path: Attendance App > Statistics > More statistics > Clock-in photo statistics. Page content: On this page, you can review analysis results for clock-in photos from today or previous days, including the total number of clock-in photos, the counts of compliant versus non-compliant photos, and Error type categories. Click any item to view details and inspect the photos one by one. Generation time: The system currently runs in asynchronous, post-event detection mode. Analysis usually completes and results appear the morning after the clock-in.

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Core value

  • HR / Admins: Cut manual review workload by more than 90% and quickly surface violations.
  • Business managers: Achieve standardized process control over store visits, field work, and on-site operations.
  • Organization-wide: Strengthen policy enforcement, organizational discipline, and safety management Standards.

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Typical use cases