Intelligent Document Classifier
3 min read
The challenge
Invoices arrived in every format imaginable: scanned PDFs, email attachments, Word files, ZIP folders, from multiple vendors, in multiple languages, dozens to hundreds of documents a day.
A dedicated team had to open every document to check if it was an invoice, read the invoice number, cross-reference it against a client database, move the file to the right account folder, and handle edge cases like multi-invoice files, duplicates and corrupt files by hand, every single day.
What we built
An AI-powered document classifier that reads, understands and routes incoming documents automatically, with no human involvement for routine cases.
It watches incoming folders, opens and reads each document (PDF, email attachment, image, Word file or ZIP bundle), decides whether it is an invoice, finds the right client account by invoice number, and files it automatically. Anything it cannot confidently match is placed in a review queue.
How it works, in plain English
Incoming document folders are monitored continuously.
PDF, email, image, Word file or ZIP bundle, the content is extracted regardless of format.
AI determines instantly whether the document is an invoice.
The correct client account is looked up automatically from the invoice number.
The document moves to the correct folder under the correct account, automatically.
Anything it cannot confidently match goes to a review queue for a human.
Results
| Metric | Before | After |
|---|---|---|
| Time per document | 3 to 5 minutes | Under 30 seconds |
| Documents processed per day | ~100 to 150 per person | 500 to 800+ |
| Staff for routine processing | 2 to 3 people | Zero |
| Error rate on routine routing | 5 to 8% | Near zero |
| Working hours | Business hours only | 24 hours, 7 days |
- 4 languages supported
- Multi-invoice files split automatically
- Duplicate files never reprocessed
Why it matters
The system does not replace the team, it removes the repetitive, low-value work from their day. The same team now handles significantly higher volume, with fewer errors and full audit visibility, at no incremental cost as volume grows.
Staff attention is focused entirely on genuine exceptions: the ratio of files needing human attention to total files received is typically under 10%, and shrinks further as the system learns the client base.
Client identity kept confidential.