Document Intelligence Pipeline
How we built a document AI backbone — OCR, classification, structured extraction, and agentic workflows — that turns thousands of delivery notes, invoices, and shipping documents per day into validated structured data for a maritime spare parts logistics provider.
The Challenge
A leading maritime spare parts logistics provider was processing thousands of delivery notes, purchase orders, invoices, and shipping documents by hand every day. Documents arrived in every format imaginable — PDFs, scans, photos, multi-page faxes — across multiple languages, and every department carried its own data-entry burden.
- Operations and finance teams spending 6+ hours daily on manual document entry
- Error rates of 8-12% causing downstream discrepancies in inventory and billing
- No standardized handling across delivery notes, invoices, POs, and packing lists
- Documents arriving in every format — PDFs, scans, photos, multi-language faxes
Our Approach
We built a document intelligence backbone — OCR, AI classification, structured extraction, and agentic workflows — that ingests documents from any source, in any format, and turns them into validated structured data ready to drive downstream operations.
Multi-Source Ingestion
Built ingestion connectors for every channel documents arrive on — email attachments, scanner uploads, vendor portals, EDI feeds, and mobile photos — so nothing has to be re-uploaded or routed manually.
OCR & Classification
Built AI-powered document processing that extracts structured data from delivery notes, invoices, POs, and packing lists — regardless of format, language, or quality — and classifies each document by type, urgency, and originating department.
Agentic Workflows
Developed autonomous AI agents that validate extracted data against purchase orders, flag discrepancies, route exceptions to the right person, and update operational systems — all without human intervention for standard cases.
Feedback & Learning
Implemented a human-in-the-loop correction system where operator overrides feed back into the models, continuously improving accuracy for edge cases and new document formats.
The Results
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