The challenge
What the client was dealing with
The operator received 300–400 shipping documents per day in inconsistent formats — scanned PDFs, photos of paper documents, and supplier-formatted spreadsheets. Each document required 8–12 fields extracted and validated. Manual entry errors were averaging 3% per document, and the team had no capacity to grow intake volume without adding headcount.
What we built
Our approach
We built a document ingestion pipeline using Google Document AI for OCR and a custom extraction layer trained on 2,000 annotated logistics documents from the client's history.
Extracted fields passed through a confidence scoring system. Documents above the threshold were auto-committed; those below were surfaced in a human review queue with pre-highlighted fields to check. A routing engine connected the validated data to the internal TMS via API, eliminating the manual entry step entirely.
The outcome
What changed
85% of documents now process end-to-end without human intervention. The 4–6 hours of daily manual entry time dropped to 40 minutes of exception review. Data entry error rate fell from 3.1% to 0.4%. The team has since grown intake volume by 60% with the same headcount.