Intelligent Automation for Operational Efficiency
How a mid-market logistics company eliminated 40 hours of manual processing per week with an AI-powered document automation system.
Meridian Logistics was processing 800+ shipping documents daily through a manual, error-prone workflow. VictorLabs designed and deployed an intelligent document processing system that reduced processing time by 95% and improved accuracy to 94%.
Manual hours eliminated
43 hrs/week
Processing accuracy
94%
Error disputes reduced
83%
Documents processed daily
800+
Context
Meridian Logistics is a mid-market logistics provider managing cross-border freight for 200+ corporate clients. Their operations team of 12 was spending the majority of each day manually extracting data from shipping documents — bills of lading, customs declarations, and carrier invoices — and entering it into their ERP system. Errors in data entry were causing shipment delays and customer disputes.
Problem
Document processing was the bottleneck. Each document required 3-5 minutes of manual data extraction and validation. With 800+ documents arriving daily across email, EDI, and PDF, the team was perpetually behind. Overtime costs were rising, error-related disputes averaged 12 per week, and the team had no bandwidth for strategic improvements.
Constraints
The existing ERP (a legacy on-premise system) could not be replaced. Documents arrived in unstructured formats — scanned PDFs, emails with attachments, fax-to-email conversions. Any automation system needed to handle 15+ document layouts, integrate with a legacy API, and achieve at least 90% accuracy before the operations team would trust it.
Approach
Rather than attempting a single monolithic solution, we designed a phased approach. Phase 1 focused on the highest-volume document type (carrier invoices). We built a document classification and extraction pipeline using a combination of computer vision for layout detection and LLM-based extraction for unstructured text. A human-in-the-loop review interface was built for flagged documents. Phase 2 expanded to all document types and added direct ERP integration.
Solution
The final system ingests documents from email, API, and upload. A classification model routes each document to the correct extraction pipeline. LLM-based extraction pulls structured data from unstructured documents with confidence scoring. Low-confidence extractions are queued for human review in a purpose-built interface. Confirmed extractions are posted to the ERP via a secure integration layer. The system includes a dashboard for throughput, accuracy, and exception monitoring.
Architecture
Frontend: React dashboard for operations team and review interface. Backend: Python FastAPI microservice for document processing pipeline, connecting to AWS Textract for OCR, GPT-4o for extraction, and PostgreSQL for audit logging. Integration layer communicates with the legacy ERP via a custom API adapter. All components run in the client's AWS environment under their existing security controls.
Results
Document processing time dropped from 45 hours per week to under 2 hours. Accuracy improved from approximately 82% (manual) to 94% (automated with human review on low-confidence items). Error-related disputes decreased from 12 per week to fewer than 2. The operations team was reassigned to customer relationship management and process improvement — higher-value work that had been continually deferred.
Lessons
The phased approach was critical to adoption. Starting with a single document type allowed the team to build trust in the system before expanding. The human-in-the-loop review interface — often skipped in automation projects — proved essential: it gave the team visibility into the system's decisions and a mechanism to correct errors before they reached the ERP.
“VictorLabs didn't just build us an AI product — they helped us understand what to build in the first place. Their discovery process revealed automation opportunities we hadn't considered.”
— Sarah Chen, VP of Operations, Meridian Logistics