Two different scores you shouldn't mix up
TaxItEasy uses two independent 0–100 scores, and the number 70 means something different in each:
- Document confidence (scanning). One overall score per scanned document, rating how sure the AI is about what it read. At 70 or above, an invoice is auto-created from the scan. This score is about reading the document.
- Match score (bank reconciliation). A separate score for how well a bank transaction fits an invoice. At 90 or above the match links silently; at 70–89 it links with a review flag, never silently. This score is about linking a payment. See how the matching pipeline works.
So "70" in scanning means "confident enough to create the invoice", while "70" in matching means "linked, but a human should glance at it".
The document confidence score
Every scan produces one overall score for the whole document. There are no per-field scores. What happens at each level:
- 70 or above: the invoice record is created automatically from the extracted data: vendor, amounts, VAT, dates, line items.
- Below 70: the document is automatically re-processed with a stronger model before anything is decided. Most borderline scans clear the bar on the second pass.
- Still below 70: the document is stored, readable, and searchable like any other; it just doesn't auto-create an invoice. On mobile you'll see an honest "OCR confidence too low" hint with a Create anyway button; on web you can create or extract the invoice manually.
A low score is not a failure. It's the system declining to book numbers it isn't sure about.
Consistency checks instead of per-field scores
Rather than scoring each field separately, the pipeline runs hard consistency checks on the numbers that matter:
- Amount reconciliation. The total paid (gross) is the anchor. If the extracted net + VAT doesn't add up to the total, the net is re-derived from the total and the invoice is flagged for human review, because contradictory numbers are never booked silently.
- Country-aware VAT. The default VAT rate comes from your company's country; the extraction prompt knows the valid rates for the detected country of the document.
If an invoice shows up flagged for review, that's usually the amount check having caught something. Open it and confirm or correct the totals.
Improving low scores
- Retake the photo. The pipeline already straightens, crops, and normalizes contrast on phone photos, but a photo you can't read yourself won't score well. Flat angle, good light, no glare.
- Rescan. The document detail page has a Rescan action, up to 3 re-runs per document.
- Corrections fix the record. A field you correct is fixed on that invoice and recorded in the audit trail. Corrections do not raise the score, and they are not used when later documents are read. A few things do carry forward, such as the spelling of a vendor you merged: see what the AI reads, and how to correct it.
The match score, briefly
Matching scores four signals: invoice number (40), amount (25), date (20), counterparty (15). The total then maps to tiers, from strong (green, 90+, silent) through likely (amber, 70–89, review flag) and possible (blue, 50–69, suggestion) down to weak (gray, 30–49, hidden). Every suggestion and auto-match carries a "Why this match?" breakdown showing exactly which signals scored, and every match can be unlinked. See why this matched, and how to undo.
Related
- What the AI reads, and how to correct it: the bigger extraction picture
- How the matching pipeline works: the four-signal match score
- Why this matched, and how to undo: the explanation view
- The OCR extracted the wrong fields: fixing extraction errors