Intelligent Document Automation for Logistics Operations
How we would approach a high-volume, multi-format document workflow feeding a legacy ERP.
A logistics operations team keys data from hundreds of shipping documents a day — bills of lading, customs declarations, carrier invoices — into an ERP that cannot be replaced. This scenario walks through the document automation system we would design for it.
What we would agree up front
An agreed field-level accuracy bar, measured on the client's own documents before go-live
Every extraction traceable to its source document and confidence score
Manual keying reduced to reviewing only the documents the system flags
A composite scenario showing how we approach this kind of problem, not a specific client engagement.
The situation
Documents arrive by email, EDI and upload in many layouts: scanned PDFs, fax-to-email conversions, carrier-specific invoices. Each takes minutes to read and key in, errors cause shipment delays and disputes, and the team has no capacity left for process improvement.
Constraints we would design around
The legacy ERP stays. Layouts vary by carrier and change without notice. The operations team will only trust automation that is demonstrably accurate on their own documents, so a measurable accuracy bar has to be agreed before go-live.
Approach
Phase the rollout by document type, starting with the highest-volume one, so the team builds trust before scope expands. Build a document classification and extraction pipeline that combines OCR and layout detection with LLM-based structured extraction, and score every field for confidence. Low-confidence fields go to a human review queue rather than straight into the ERP.
Architecture
React dashboard and review interface for the operations team. Python FastAPI pipeline service calling a managed OCR service and an LLM with structured output. PostgreSQL for the audit log of every extraction and correction. A small adapter talks to the legacy ERP API. Everything runs inside the client's own cloud account under their existing security controls.
Design notes
Most document automation projects fail at adoption, not accuracy. The review interface — showing the source document beside the extracted fields — is as important as the model. Corrections made there become the evaluation set that shows whether each new model or prompt version is actually better.