Business operations

Automated processing of logistics documents

Affinda uses AI to extract key details from logistics documents:

  • Details of the transportation company (i.e. the carrier), the shipper and/or consignee;
  • The place where the goods were loaded;
  • Destination;
  • Transportation mode (i.e. road, rail, air, sea, etc.);
  • The terms of the shipment (incoterms); and
  • A description of the goods being carried (including their weight, dimensions, classification, etc).

Business context and challenge

Freight transport requires review of numerous disparate documents.

At every stage of an organization’s logistics pipeline, human experts review and error-check a wide range of documents, including all the following:

  • Bills of lading (inland, ocean and air waybill)
  • Pro forma invoices
  • Commercial invoices
  • Customs documents
  • Packing lists
  • Certificates of origin
  • Country-specific certificates
  • Shipper’s letter of instruction
  • Dangerous goods forms
  • Bank drafts
  • FOB terms of sale
  • Freight bills
  • Delivery orders
  • Dock receipts
  • Shipping guarantees
  • Packing declarations
  • Certificates (insurance, health, phytosanitary)
  • Letters of credit
  • …and many others

All these documents must be manually read, checked for errors, and accurately completed before a shipment can be processed and transported to the next point on its itinerary.

To process these documents, many companies rely on expensive teams of manual reviewers and data-entry specialists. This process consumes valuable time — and the resulting database entries then require a second layer of expert error-checking.

In short, manual data entry leads to wide range of problems:

  1. The necessary man-hours are often prohibitively expensive.
  2. Human-extracted data is highly error-prone and inconsistent.
  3. Bottlenecks can appear throughout every stage of the pipeline.

Given the document-heavy nature of the logistics process, it has become mission-critical for companies to embrace leading-edge technologies to streamline their high volume document workflows.

Affinda's solution

Automatically read, understand, and extract information from all shipping document types.

From short forms to extensive multi-page tables, Affinda’s deep-learning AI technology is adept at pinpointing and extracting the most relevant information into consistent, ordered lists.

Our solution creates a set of structured data that can be used across a wide variety of downstream approvals processes, and even imported directly into enterprise resource planning (ERP) and accounting systems.

From day one, Affinda immediately begins capturing measurable ROI by automatically extracting the vast majority of form fields with better-than-human  accuracy. 

What’s more, Affinda’s deep-learning document parser provides a built-in way for clients to check subsets of results in mere seconds, maintaining confidence that all extracted data remains above their specified confidence threshold. In cases where a field requires checking, an employee can review the model output, and amend it as needed.

In fact, Affinda’s document scanner actually learns and improves its accuracy as you give it more feedback. This means you’ll need to check and validate even fewer results over time, while the AI continues to deliver ever-increasing ROI, straight from the first day you use it.

This revolution in document processing delivers a diverse array of benefits:

  • It begins delivering immediate ROI, right from the very first document.
  • It provides significant time savings over human-centric review processes.
  • It mitigates risks and costly mistakes due to human error.

This ROI will continue to increase through ongoing usage as the model gets better at recognizing the documents that are specific to your company.

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