AI Automation Platform for TransGlobal Logistics
How we replaced 6 hours of daily manual data entry with an AI pipeline that processes 2,400+ shipment records in 97 minutes — saving $340K annually.
The Challenge
TransGlobal Logistics managed 2,400+ shipment records daily across four carrier systems, two warehouse management platforms, and a legacy ERP built in 2011. Their operations team of 12 spent an average of 6 hours each day copying data between systems, validating addresses, cross-referencing customs codes, and reconciling invoices manually.
The manual process created three critical problems. First, a 4.2% error rate in shipment data caused delivery failures costing $18,000/month in rerouting fees. Second, the operations team couldn't scale — every 15% increase in order volume required a new hire. Third, data lag of 4-6 hours between systems meant customer service couldn't provide real-time tracking updates, driving a 23% complaint rate on delivery status inquiries.
TransGlobal had tried two RPA solutions before contacting us. Both failed because the input data was semi-structured — carrier emails, PDF invoices, and scanned customs documents that rule-based automation couldn't parse reliably.
Our Solution
We built an ML-driven data pipeline that combined document understanding (via Claude's vision capabilities) with structured workflow automation using Apache Airflow. The system handles the full lifecycle: ingest documents from email/SFTP, extract and validate data using LLMs, transform it into the ERP's schema, and push updates to all connected systems in near real-time.
The architecture follows a three-layer design. The ingestion layer monitors 6 data sources (email attachments, carrier APIs, SFTP drops, scanned PDFs, webhook events, and manual uploads) and normalizes everything into a processing queue. The intelligence layer uses Claude API with custom prompts fine-tuned on 8,000 historical shipment records to extract structured data from unstructured documents — including handwritten customs forms. The orchestration layer, built on Apache Airflow, manages 47 automated workflows with conditional routing, error handling, and human-in-the-loop escalation for edge cases.
We deployed the system in Docker containers behind an Nginx reverse proxy, with a Next.js dashboard that gives the operations team full visibility into pipeline status, exception queues, and processing metrics.
Implementation Process
Discovery & Data Audit(2 weeks)
Mapped all 6 data sources, documented 47 manual workflows, analyzed 3 months of error logs, and identified the 12 highest-impact automation candidates.
AI Model Development(4 weeks)
Built and validated extraction prompts using 8,000 historical records. Achieved 99.1% accuracy on structured carrier data and 96.8% on semi-structured customs documents.
Pipeline & Dashboard Build(5 weeks)
Developed the Airflow orchestration layer, exception handling logic, the Next.js monitoring dashboard, and integration adapters for all 6 source systems.
Testing & Parallel Run(3 weeks)
Ran the AI pipeline in parallel with manual processing for 3 weeks. Compared outputs daily, refined edge cases, and trained the operations team.
Results
The platform went live in week 14 and reached full automation capacity within 5 business days. The operations team shifted from data entry to exception management and customer communication — work that actually requires human judgment.
Technology Stack
Ready to Start?
No commitments. Tell us what you need and we'll tell you how we'd solve it.
“We went from dreading Monday morning data backlogs to having everything processed before the team finishes their first coffee. The accuracy improvement alone paid for the project in the first quarter.”
— VP of Operations, TransGlobal Logistics
Ready to Automate Your Operations?
If your team spends hours on manual data processing, we can show you exactly where AI automation fits and what ROI to expect.
Free consultation · Typically respond within 24 hours