What this covers
Manual receipt entry is slow and error-prone. When someone miskeys an amount or assigns the wrong vendor, it causes reconciliation problems downstream that take longer to fix than the original entry. Optical character recognition combined with structured validation can handle most of that extraction automatically.
We build a receipt processing pipeline tailored to your document types and languages. The pipeline accepts images from mobile uploads, email attachments, or scanned batches, extracts merchant name, date, amount, currency, and tax fields, then routes the structured data into your expense system or accounting software. Confidence scoring flags low-quality extractions for human review rather than silently passing bad data through.
The result is not a perfect system that never needs oversight. It is a well-calibrated one that handles routine receipts without touching them while clearly marking the exceptions that genuinely need attention.
Session programme
Build Stages
- Document type analysis - Reviewing your most common receipt and invoice formats to set extraction priorities.
- OCR engine selection and configuration - Choosing between Google Document AI, AWS Textract, or Azure Form Recognizer based on your document mix.
- Extraction rule definition - Mapping fields to your chart of accounts and setting validation rules for each.
- Confidence threshold calibration - Testing against a sample of real documents to find the right auto-approve versus review threshold.
- System integration - Connecting the pipeline output to your expense platform or accounting software via API.
- Monitoring dashboard - A simple view showing extraction volumes, error rates, and flagged items per day.