Review, rework, and escalation

Route quality decisions with explicit ownership and context.

Review should turn a quality decision into a clear next state. Rejected work needs an actionable reason and destination; specialist escalation needs an owner and return path.

Before you make the change

• Define acceptance criteria and reviewer authority.

• Separate item-level correction from systemic policy or schema correction.

• Name the specialist or governance owner for escalated cases.

Understand the product behavior

Review has both approve and reject edges. This allows a correction path to be designed before production starts.

Unitlab AI workflow canvas connecting project, annotation, and review stages

A production workflow might be:

Project
├── Model → Annotate → Review
│                    ├── Accepted → Complete
│                    └── Rejected → Annotate
└── Difficult/low-confidence → Specialist Review
                               ├── Accepted → Complete
                               └── Rejected → Annotate

Here, Specialist Review is another stage of the released Review type with a specialist eligibility list, not a different stage type.

Human review

Review stages require Approve and Reject outcomes. Approve follows the forward edge; Reject returns the item to the configured rework stage and can generate a rework notification. Workbench actions come from the item’s current stage rather than a fixed button set. Review should test the task’s quality policy, not merely confirm that an annotation exists.

Workbench stage actions

Saving and moving through the workflow are separate actions. Saving appends annotation history inside the current stage. A stage action changes the item’s route.

The Workbench header builds available actions from the current item and can show:

• Send to Review or Send to <stage>;

• Mark as Complete;

• Reject as a danger action;

• Restart Workflow for a manager viewing a Complete item;

• item timeline.

Unitlab AI video Workbench with frame timeline and Send to Review control

Automated-stage items open read-only while the model or automation owns them. After a successful stage action, Unitlab saves dirty work, advances to the next item in the current queue/filter context, and returns to the project Datasets page with a Queue complete message when no work remains.

Operate a quality decision

1

1. Inspect the full task

Review the relevant frames, pages, panels, timeline, properties, relations, and source context.

2

2. Make the decision

Accept, reject, or escalate against the published Instructions and ontology.

3

3. Explain corrective work

Identify the instance, interval, page, field, or policy rule that needs attention.

4

4. Route to the owner

Move the task to the configured rework or specialist stage.

5

5. Re-review the correction

Confirm the specific defect and any related cohort risk are resolved.

6

6. Escalate systemic gaps

Update Instructions, ontology, workflow, or training through controlled change.

Decisions that affect production

Decision	Production guidance
Accept	The work satisfies current policy and required values.
Reject	The correction is understood and can return to an owner.
Escalate	The decision exceeds the current role or policy and requires a named specialist.
Systemic issue	Inspect the affected cohort and control, not only the triggering item.

Continue the operating flow

• Track repeated rejection reasons.

• Update calibration examples for recurring defects.

• Confirm queue state and ownership after every transition.

Product context

Unitlab helps AI teams curate, annotate, manage, version, and prepare multimodal training data at enterprise scale.

See Unitlab’s multimodal data annotation platform for the commercial overview of enterprise annotation and quality workflows.