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From field evidence to follow-up

Inspection evidence traceability

Connect an asset, an inspection point and its visual evidence so quality teams can understand a result and follow up without reconstructing the context from chat messages.

Who this is forQuality engineers and field inspectors working on large robots, complex equipment or distributed piping.

How the workflow fits together

  1. Identify the scope

    Start from the asset or serial number (SN), the inspection point and the applicable scheme version. Define what will and will not be inspected.

  2. Capture in context

    Use the iOS field workflow to guide the view and collect evidence. Check visibility, lighting and coverage before interpreting an AI finding.

  3. Review the source

    In the Web workflow, inspect the finding alongside its source evidence and review status. Keep uncertainty visible and return unclear cases for further capture or human judgment.

A captured image is not a passed inspection. A repair is not a passed reinspection. A result for inspected items does not establish whole-plant yield or authorize shipment.

Is an AI label sufficient evidence?

No. A useful review needs the source image, the inspected scope, the applicable criteria and the final human disposition. Missing or unclear evidence should remain unresolved, not silently become a pass.

Define a useful pilot.

Bring one asset type, a few representative inspection points, an agreed defect standard and the people responsible for review. Agree sample coverage, false positives, missed defects, review time and acceptance criteria before measuring results. Confirm device support, data retention, access permissions and integration scope in the proposal.

Find the source behind a finding.

These pages describe the intended EPOVIS workflow. Confirm the supported devices, feature availability and acceptance scope for your deployment during the scenario demo.