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Background and pain points

In daily attendance management, more and more organizations use photo check-in to verify whether employees have actually arrived on site or completed store inspection tasks. However, the following issues often occur in practice:
  • Employees upload invalid images such as ceilings, floors, black screens, or blank walls.
  • Photos do not include a face, so they cannot prove that the person was present.
  • During store inspections, photos of shelves or displays are blurry or shot from the wrong angle and fail to reflect the real situation.
  • Special job requirements (such as wearing a safety helmet or reflective vest, or complying with grooming standards) lack effective inspection methods.
  • Admins must manually review hundreds or thousands of photos one by one, which is time-consuming, labor-intensive, and inefficient.

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Product solution

To address these management challenges, we launched the new AI error detection for photo check-in feature. Powered by large Models and image understanding technology, it automatically identifies non-compliant check-in behavior and helps Organization managers achieve efficient, accurate remote supervision.

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Key feature highlights

Error image detection

Automatically detect photos of abnormal scenes such as black screens, blank screens, ceilings, and floors.

Face detection

Determine whether a photo contains a face.

Error notification mechanism

Automatically generate error reports and push them to Admins for handling.

Visual statistics Kanban

Provide an overview of photo quality data in the attendance console.

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Feature details and access path

Feature details of photo check-in error detectionPhoto quality data overview Kanban
  • Access path: Attendance app → Statistics → More statistics → Check-in photo statistics.
  • Content: On the page, review the analysis results for today’s and historical check-in photos, including the total number of check-in photos, the number of qualified vs. unqualified photos, and the error type breakdown. Click View details to inspect each photo.
  • Generation time: This feature currently runs in asynchronous post-event detection mode. Analysis typically completes and results are generated on the morning after check-in.

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Core value: higher efficiency, lower cost, smarter supervision

HR / Admin

Cut manual review workload by more than 90% and quickly flag violations.

Business supervisor

Standardize process control for store inspections, field work, and on-site operations.

Organization-wide

Strengthen policy enforcement as well as organizational discipline and safety management standards.

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Typical use cases