Freight document automation for a logistics provider
A freight forwarder's team re-keyed BOLs, PODs, and carrier invoices into their TMS by hand. Document AI now handles every standard format, and an agent chases the missing paperwork.
Client
Confidential (EDIT-ME)
Domain
Logistics
Services
Workflow Automation, AI Agents, Generative AI
Overview
A freight forwarder's team re-keyed BOLs, PODs, and carrier invoices into their TMS by hand. Document AI now handles every standard format, and an agent chases the missing paperwork.
The client is a freight forwarding company coordinating shipments across a large carrier network. (EDIT-ME: describe the real client.) Every shipment generated a file of documents (bills of lading, proof-of-delivery slips, carrier invoices, customs paperwork) arriving by email in every imaginable format: clean PDFs, phone photos of paper forms, spreadsheets, and scans of scans.
Challenge
Four full-time staff did nothing but read incoming documents and re-key their contents into the transportation management system. The work was mind-numbing, turnover in the role was constant, and every new hire meant weeks of training on document quirks.
Manual entry errors surfaced downstream as billing disputes: a transposed weight or wrong reference number meant a carrier invoice that didn't match the shipment record, hours of investigation, and sometimes a written-off difference. Month-end close regularly stalled for days on incomplete files.
Missing paperwork was its own workflow. When a POD or invoice never arrived, someone had to notice, find the carrier contact, write the chase email, and remember to follow up: a process that lived in inbox flags and failed quietly all the time.
Our approach
We built an extraction pipeline that watches the operations inbox, classifies each incoming document by type, and extracts its fields regardless of format: modern LLM-based document understanding handles the phone photos and messy scans that defeated the client's earlier OCR attempt.
Extracted data is validated against the shipment record before anything is written: quantities, weights, references, and rates must reconcile with what the TMS expects. Clean documents post automatically; discrepancies land in an exception queue with the difference highlighted, so a human resolves in seconds what used to take an investigation.
For incomplete files, a follow-up agent takes over: it knows which documents each shipment still needs, drafts the carrier chase emails, tracks responses, and escalates to a human after two unanswered attempts. Month-end no longer depends on someone remembering.
The whole pipeline is built on the client's existing tools (their inbox, their TMS, an orchestration layer we added) so operations staff kept their working environment and simply stopped doing the re-keying part of it.
Solution
Key capabilities include:
- Format-agnostic extraction. BOLs, PODs, invoices, and customs forms parsed from PDFs, photos, and scans alike: no carrier onboarding required.
- Validation before entry. Every extracted field reconciles against the shipment record before posting; mismatches go to an exception queue, not into the TMS.
- Automatic document chasing. An agent tracks each shipment's missing paperwork, emails carriers, and escalates only after real follow-up fails.
- Exception queue with highlights. Humans see exactly which field disagrees and by how much: resolution in seconds, not investigations.
- Dispute-ready records. Original document images stay linked to every TMS entry, ending the hunt through inboxes during billing disputes.
- Volume elasticity. Peak-season document surges process at the same speed as a quiet Tuesday.
Outcomes
92%
of documents processed with no human touch
4 FTEs
of manual entry redeployed to operations
2 days
faster month-end close
Data entry as a job function no longer exists at the company: the four staff who did it moved into customer-facing operations roles that had been chronically understaffed. Billing disputes dropped with entry errors, and month-end close now finishes on schedule with complete files. (EDIT-ME: replace with the real outcome story.)
“We used to hire for data entry and lose people every six months. That whole job just doesn't exist here anymore.”
Looking to solve something similar in logistics?
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Tech stack
- Python
- Claude
- n8n
- TMS API
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