Logistics Company (Confidential) · Logistics · 16 weeks
AI Document Processing: 90%+ Reduction in Manual Data Entry
How Softotic built an AI document processing pipeline that classifies, extracts, and routes 10,000+ documents per day with over 95% accuracy.
10,000+
Documents processed/day
Up from 3,000 with manual team
96.2%
Extraction accuracy
Across all document types
<4%
Manual review required
Only low-confidence extractions flagged
12 → 2
Data entry team size
Operators now handle exceptions only
The problem
The client processed thousands of logistics documents daily—freight invoices, customs declarations, delivery notes—manually keyed into their ERP. A team of 12 data-entry operators was handling this round the clock. Error rates were 4–6%, delays were frequent, and the operation was a bottleneck for on-time delivery confirmations.
The delivery approach
Softotic built an end-to-end AI document processing pipeline. Documents arrive via email attachment, FTP, or API upload. A Python-based ingestion layer converts them to normalised images, runs OCR (AWS Textract + Tesseract fallback), and feeds the output to a fine-tuned classification model. Structured data is extracted field-by-field using template matching and ML, validated against business rules, then pushed to the ERP via API. All documents and extracted data are stored in PostgreSQL with a review UI for low-confidence items.