Match Results
Results are presented per document with the top 5 match candidates ranked by weighted confidence score. Each candidate includes a confidence score (0-100%) and field-level evidence showing which comparisons contributed to the match, the strategy used for each field (exact, fuzzy, date_range, or numeric_range), the individual field score, and the actual values that were compared on both sides. This transparency makes it straightforward to verify correct matches and investigate false positives.
The evidence view is designed to make match decisions transparent. For each candidate, you can see exactly which fields matched, what strategy was used, the individual field score, and the actual values that were compared. This makes it straightforward to verify correct matches and investigate false positives.
Approved matches flow downstream into delivery pipelines, where they can be included in structured exports alongside extraction data. Rejected matches are excluded from future consideration for that document, which helps the system learn from your decisions when running subsequent matching passes.
When reviewing results, focus on documents where the top candidate has a confidence score between 50% and 85% — these are the borderline cases that benefit most from human judgment. High-confidence matches (above 85%) are usually correct, while very low scores (below 30%) typically indicate no valid match exists in the reference data.
Result fields
| Parameter | Type | Description |
|---|---|---|
| Confidence | score | Weighted aggregate score across all field comparisons (0-100%). |
| Evidence | detail | Per-field breakdown showing strategy used, individual score, and matched values. |
| Top 5 | candidates | The five highest-scoring reference records for each document. |
curl -s "https://api.talonic.com/v1/matching/runs/mrun_001/results?limit=5" \
-H "Authorization: Bearer $TALONIC_API_KEY"
# Response:
# {
# "results": [
# {
# "id": "mres_001",
# "document_id": "doc_001",
# "document_name": "Invoice_ACME.pdf",
# "top_candidates": [
# {
# "reference_row_id": "row_42",
# "confidence": 87,
# "evidence": [
# { "field": "vendor_name", "strategy": "fuzzy", "score": 92, "source_value": "Acme Corp", "target_value": "ACME Corporation" },
# { "field": "total_amount", "strategy": "numeric_range", "score": 100, "source_value": "12450.00", "target_value": "12450.00" }
# ]
# }
# ]
# }
# ]
# }
# Approve a match result:
curl -X POST "https://api.talonic.com/v1/matching/runs/mrun_001/results/mres_001/review" \
-H "Authorization: Bearer $TALONIC_API_KEY" \
-H "Content-Type: application/json" \
-d '{ "action": "approve", "candidate_index": 0 }'Consider a practical example: you receive an invoice from "Acme Corp" with a total of $12,450 dated 2025-03-15. The matching engine returns the top candidate as "ACME Corporation" in your reference data with a confidence score of 87%. The evidence view shows the vendor name scored 92% via fuzzy match (handling "Corp" vs "Corporation"), the amount scored 100% via exact match, and the date scored 78% via date_range because the reference shows a PO date of 2025-03-10 — within the 7-day tolerance. You can quickly verify the match is correct and approve it, sending the linked record downstream.