AI-Powered Invoice Processing
Replaced manual invoice validation with an OCR and AI extraction pipeline that checks every invoice against its PO.
- Extraction accuracy
- 90%
- Extraction accuracy
- Manual data entry at steady state
- ≈0
- Manual data entry at steady state
- PO / invoice mismatches
- ↓
- PO / invoice mismatches
- Faster vendor settlements
- ↑
- Faster vendor settlements
The problem
Finance teams keyed in and cross-checked invoices against purchase orders by hand. It was slow, error-prone, and a bottleneck to settling with vendors and partners on time.
What I built
- Built an OCR pipeline that extracts structured line-item data directly from vendor invoices in varied formats.
- Layered an AI validation engine on top to flag mismatches against POs before anyone reviews them.
- Kept a human in the loop for exceptions, with confidence scoring so only real edge cases reach a reviewer.
Outcome
Reached 90% extraction accuracy, cut manual data entry to near zero, reduced PO/invoice mismatches and noticeably shortened vendor settlement cycles.
How it rolled out
- 01
Framing
Quantified manual effort and error rate across invoice processing.
- 02
OCR pipeline
Stood up document extraction for varied vendor invoice formats.
- 03
Validation
Added AI-based PO matching with confidence-scored exception routing.
- 04
Rollout
Deployed across finance operations with human review for edge cases.
Architecture
- Invoice ingestion (scans / PDFs)
- OCR extraction engine
- AI validation & PO matching
- Exception queue → human review
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