Extract
OCR captures the likely values and a confidence score for each field.
Case study 04 / Logistics operations
Process 10,000s of documents.
Reach 99.8% accuracy.
A multi-channel document intelligence system turns poor photos, inconsistent invoices, and scattered messages into validated JSON with 99.8 percent accuracy.
Tens of thousands migratedHuman review built inProduction logistics system
01
99.8%
accuracy with confidence-based review
02
2h → 5m
from scattered document to structured data
03
3 FTE
redirected from repetitive data entry
04
~6,000h
annual capacity returned to the team
The operational mess
Unify five channels.
Remove manual entry.
Documents arrived through email, WhatsApp, Telegram, Slack, and direct uploads. Some were clean PDFs. Others were skewed phone photos taken in poor light. The operations team had to find each one, read it, resolve unclear fields, and enter the result into the core system.
Simple OCR was not enough. A capital I could become a lowercase l. A known customer number could be misread. Bad lighting could remove an entire field. At hundreds of documents per week, small recognition failures became an operational backlog.
The first migration covered tens of thousands of historical documents. The ongoing workflow then needed to handle hundreds more every week without turning three people into permanent transcription infrastructure.
The system
Route by confidence.
Resolve in five minutes.
Every document starts with inexpensive extraction and deterministic validation. Ambiguity escalates to AI. Only the rare cases that remain unclear reach a person.
Document intelligence / live routing
Every format enters one confidence pipeline
99.8%
field-level accuracy
1 in 500
documents need a human
2h → 5m
ingestion turnaround
The confidence ladder
Automate 99.8%.
Escalate one in 500.
The system combines extraction confidence with existing customer and shipment data. High-confidence fields move automatically. Medium-confidence fields get an AI check with more context. Approximately one in 500 documents is ambiguous enough to require human judgment.
Cross-check
Known customers, identifiers, and business rules catch plausible-looking mistakes.
Escalate
AI handles ambiguity. Humans see only the cases the system cannot defend.
Capacity returned
Redirect three roles.
Return 6,000 hours.
Nobody was removed. Three people who had been spending their days on data entry moved into more engaging work with room to progress. Using a standard 2,000-hour work year, that represents roughly 6,000 hours of annual team capacity redirected from transcription to operations.
Capacity estimate: 3 full-time roles × approximately 2,000 working hours per year.