Two different scores — don't mix them 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 — 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 re-scan action — up to 3 re-runs per document.
- Correct once, benefit later. When you correct a scanned invoice, the system learns vendor-specific patterns (category, currency, VAT rate, net-vs-gross layout). After 3 confirmed corrections, the hint becomes active and is applied to future scans of that vendor. If a vendor's values genuinely vary, the system learns not to assume instead of guessing.
The match score, briefly
Matching scores four signals — invoice number (40), amount (25), date (20), counterparty (15) — and maps the total to tiers: strong (green, 90+, silent), likely (amber, 70–89, review flag), possible (blue, 50–69, suggestion), weak (gray, 30–49, hidden). Every suggestion and auto-match carries a "Why this match?" breakdown showing exactly which signals scored, and every match is undoable. 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