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Approval Queue

Results that do not meet auto-approval thresholds are routed to the Approval Queue. Navigate to Review → Approval Queue to see all pending items. Each row shows the source document, schema, confidence score, and whether the result has been flagged by a validation rule.

Filter the queue by status (pending, flagged), schema, or confidence range. Click "Review" on any row to inspect the extracted values, provenance trails, and validation check results before approving or rejecting.

The review detail view shows the extracted values alongside the source document, with provenance trails tracing each value back to its origin in the text. Validation check results are displayed inline — you can see exactly which rules passed and which failed before making your decision. Batch actions are available for approving or rejecting multiple items at once.

When you approve a result, a result.approved signal is emitted to the delivery pipeline. When you reject a result, a result.rejected signal fires instead. This event-driven design lets you build automated workflows that respond to review decisions — for example, routing approved records to a webhook and rejected records to a notification channel.

For best results, review flagged items first — these are records where at least one validation check failed, making them the most likely to contain errors. Most teams assign a daily review cadence and use confidence range filters to prioritize low-confidence items that need the most attention.

The Approval Queue integrates with the broader delivery pipeline through event-driven signals. Every approve or reject action — whether manual or via batch operations — emits the corresponding signal immediately. This means downstream systems receive real-time notifications of review decisions. You can bind these signals to different destinations: approved records to a webhook that triggers an ERP import, rejected records to a Slack notification for the data operations team. The event-driven design decouples review decisions from downstream processing, making the system easy to extend.

  • Navigate to Review → Approval Queue to see all pending items
  • Filter by status (pending, flagged), schema, or confidence range
  • Review detail view shows extracted values alongside the source document
  • Provenance trails trace each value back to its origin in the document text
  • Inline validation check results show which rules passed and which failed
  • Batch approve or reject multiple items at once
  • LLM auto-review proposes decisions for pending items with one-click accept or override
  • result.approved and result.rejected signals emitted for delivery pipeline integration
List pending items in the Approval Queue via API
curl -s "https://api.talonic.com/v1/review?status=pending&limit=10" \
  -H "Authorization: Bearer $TALONIC_API_KEY"

# Response:
# {
#   "items": [
#     {
#       "id": "rev_001",
#       "document_name": "Invoice_Beta_Corp.pdf",
#       "schema_name": "Invoice Extraction",
#       "confidence": 0.78,
#       "validation_pass_rate": 0.90,
#       "field_coverage": 0.85,
#       "flags": ["low_confidence_outlier"],
#       "status": "pending"
#     }
#   ],
#   "total": 24
# }
Approve or reject a review item
# Approve a single item:
curl -X POST https://api.talonic.com/v1/review/rev_001/approve \
  -H "Authorization: Bearer $TALONIC_API_KEY"

# Reject a single item:
curl -X POST https://api.talonic.com/v1/review/rev_001/reject \
  -H "Authorization: Bearer $TALONIC_API_KEY"

# Each action emits the corresponding delivery signal
# (result.approved or result.rejected) immediately.

The Approval Queue is the human checkpoint in your data pipeline. It ensures that results below your confidence thresholds receive manual review before reaching downstream systems. For teams processing large volumes, the LLM auto-review feature can dramatically reduce review time — AI proposes approve or reject decisions based on the extraction context, and reviewers accept or override with a single click. Auto-review is especially effective for straightforward items that just missed the auto-approval threshold, letting reviewers focus their attention on the genuinely ambiguous cases that benefit most from human judgment.

LLM auto-review is available to accelerate the approval process. When enabled, AI proposes approve or reject decisions for pending items, which you can accept or override with a single click. Auto-review is especially effective for high-volume workloads where most items are straightforward — it lets reviewers focus their attention on the genuinely ambiguous cases.

Frequently asked questions

Where is the Approval Queue?+
Navigate to Review > Approval Queue to see all results pending manual review. Each row shows the source document, schema, confidence score, and validation flags.
How do I review items in the Approval Queue?+
Filter by status (pending, flagged), schema, or confidence range. Click "Review" on any row to inspect extracted values, provenance trails, and validation check results before approving or rejecting.
Can I batch approve or reject items?+
Yes. Select multiple items in the queue and use the batch action buttons to approve or reject them all at once. Each item emits the appropriate delivery signal individually.
What happens after I approve or reject an item?+
Approving emits a result.approved signal to the delivery pipeline. Rejecting emits a result.rejected signal. If you have delivery bindings configured for these signals, the corresponding payload is delivered to your destination immediately. Both signals are emitted individually even during batch operations, ensuring each item triggers its own downstream workflow.

Related

Approval Gates
Validation Checks
Confidence & Provenance