Photograph the receipt. Let AI do the data entry.
Vendor, total, line items, VAT, date, category - extracted in seconds, with confidence scores attached so reviewers know exactly which fields to double-check. JPG, PNG, PDF, HEIC. 25MB per receipt. Native multi-language support.
London SW3 4UD
VAT: GB 232 1849 27
Whatever the camera roll or scanner gives you.
No format conversion. No compression. No "please save as PDF". Upload the file the way it left the device.
Three layers of intelligence. One receipt.
Most receipt OCR stops at "the AI got it right" or "the AI got it wrong" - there's no middle ground. OmniPATH treats every extracted field as having a confidence level so reviewers know exactly where to look.
Every field has a confidence score.
Vendor name extracted at 99%? Skip the review. VAT number at 67%? Highlighted in amber so the reviewer's eye goes straight to it. The system tells you exactly where its judgement is shaky - so you spend zero time double-checking the easy fields.
Confidence thresholds are configurable per field type. By default: above 90% auto-accepts, 70–90% flags for review, below 70% requires manual confirmation before submission.
- Per-field, not per-receipt - so a 99% total + 65% VAT-number flags only the VAT field
- Three colour-coded bands: green (high), amber (medium), red (low)
- Configurable thresholds per field (vendor, total, VAT, line items, date)
- Sub-70% scores blocked from auto-approval - must be confirmed first
Not just the total. Every line.
Most expense tools capture the receipt header - vendor and total - and stop there. OmniPATH reads each line. So when finance needs to ask "what was actually on this £487 dinner?", the answer's already there: 4 main courses, 2 starters, 6 drinks, broken down with prices and per-line VAT.
Line-item detection works on printed receipts, handwritten amendments, multi-column layouts and the awkward thermal-print formats that other OCR engines struggle with.
- Item description, quantity, unit price, line total - all separated
- Per-line VAT rates detected (mixed-rate receipts handled cleanly)
- Sub-totals, service charges, tips and discounts identified separately
- Currency-symbol detection for international receipts
Catches the same receipt twice.
The classic expense leak: same lunch claimed by two people, or same receipt submitted in two different months. OmniPATH compares every new submission against the last 90 days of expenses across the workspace - same vendor, similar amount, same date window - and flags potential duplicates before they reach an approver.
Detection runs at submission time, not at month-end audit. The submitter sees the warning, the approver sees the warning and either can resolve it inline.
- 90-day workspace-wide cross-check at submission time
- Match on vendor + amount (±£0.50) + date (±7 days)
- Cross-employee checks - catches the "we both expensed lunch" scenario
- Inline resolution: confirm legitimate or withdraw with one click
From shutter to submission.
Four stages, every one running server-side after the photograph is taken. Total elapsed time: usually under five seconds.
Image normalisation
Auto-rotate, deskew, perspective-correct. Convert HEIC to standard format. Enhance contrast on faded thermal receipts.
OCR + vision pass
Multi-model OCR plus a vision-language model that reads layout context. Critical for non-tabular receipts and mixed handwriting.
Field extraction + scoring
Identify vendor, totals, dates, VAT, line items. Generate per-field confidence scores. Cross-validate (subtotal + VAT = total).
Categorisation + checks
Suggest expense category from vendor + line items. Run duplicate check against last 90 days. Apply policy validation.
The technical details.
How does the AI handle handwritten or partially-faded receipts?
What languages and currencies are supported?
What happens if the AI gets a field wrong?
Can I capture multiple receipts in one photo?
Are receipts stored for audit, or only the extracted data?
How does mileage / distance-based receipt processing work?
See it process your receipt.
The fastest demo we run. Take a photo of any receipt you've got, drop it in. Watch fields populate with confidence scores in three seconds.