From Manual Paperwork to ERP-Ready Data

A logistics and supply chain company managing high daily volumes of shipping documents, including invoices, proof of delivery (POD), transport documents, and customs-related paperwork. The operation supported multiple clients and routes, with documents arriving from drivers, warehouses, and partners in different formats and languages. Document processing was largely manual, relying on back-office teams to review, validate, and enter data into ERP and billing systems

The client faced growing operational pressure due to high manual effort required to process documents, frequent data entry errors and missing fields, delays in billing and customer invoicing, difficulty validating PODs and shipment completion, and limited traceability and audit readiness. These challenges resulted in slower cash collection cycles, increased operational costs, and disputes with customers due to missing or incorrect documentation.

The Document AI Engine was deployed to automate document ingestion, extraction, and validation across the logistics workflow. The engine processed scanned and photographed documents, PDFs, and digital forms, including handwritten and printed content. Using document classification and intelligent extraction, it converted unstructured documents into structured, ERP-ready data. Validation rules ensured completeness, consistency, and accuracy before documents entered billing and reporting workflows.

The implementation delivered clear efficiency gains: document processing time reduced by ~80%; data entry errors reduced by ~70%; billing cycle time shortened by ~30%; operational cost per shipment reduced by ~20%; enabling faster invoicing, improved accuracy, and better operational scalability.

NLP & Generative AI · Process Intelligence, integrated with ERP, TMS, document management systems, and operational dashboards through secure APIs in cloud or hybrid environments

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