Freight invoice digitization
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How to automate freight invoice digitization
When it comes to freight invoice automation, it’s rare for a team to be working with a single PDF format. Most brands, 3PLs, and freight forwarders constantly find themselves working across a variety of carriers, making it difficult to structure data consistently. As a result, many teams settle by pulling the big number off the bottom of their invoices instead of maintaining comprehensive invoice records with line item detail.
With Parabola, you can easily parse messy freight invoices into structured datasets by leveraging LLMs and computer vision to make PDF parsing easy. Parabola’s PDF parsing tool was designed with varying PDF formats in mind, enabling you to consistently parse data from PDFs, even if the document format changes from carrier to carrier. Beyond extracting data from freight invoices, most freight invoice parsing workflows also leverage AI and additional data transformation steps to map carrier data to an internal format — enabling historical spend databases, line item categorization, and more comprehensive carrier scorecards.
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Why Parabola
The value Parabola has added to internal reporting and preparing workflows at Flexport is huge. We've seen such a dramatic reduction in our cost to serve since implementing, and Parabola has been a key partner in our quest to reduce cost to serve and performance transparency.
Digitizing freight invoice PDFs involves extracting key information from freight invoices, such as shipment IDs, origin and destination data, and line item details. Most commonly these invoices will come in from a carrier via email, requiring operators to manually open PDF attachments and copy and paste data from PDFs. Since invoices rarely come in a consistent format from carrier to carrier, the creation of these workflows also involves creating logic to ensure invoices are being handled consistently — even if the format changes from email to email.
- Pull a freight invoice into Parabola either using the Pull from PDF file or Pull from inbound email step.
- Within the PDF parsing step, configure the step settings to extract key fields like shipment ID, invoice due date, and line items. Use the Keys section for document-level data like your shipment ID and origin port; use the Columns section for table data like your line item description and amounts.
- Standardize and clean the extracted data using steps like Edit columns and Standardize with AI.
- Action on your data by sending it off to a destination like a Parabola Table (used for historical tracking), your ERP or TMS, or any other system where spend data lives.
- To improve the accuracy and consistency of your freight invoice PDF parsing, add additional fine tuning context to each column including sample values and context on where to find the values in the document. The more context and examples you provide, the better your results will be.
- After parsing the data, consider using the Categorize with AI step to assign line items into spend buckets for more granular reporting.
- Automate email-based workflows by leveraging the Pull from inbound email step.
- To start building your freight invoice parsing Flow, use the Template above and check out the Parabola University lesson on PDF parsing.
- Learn more about how to set up an auto-forwarding rule in Parabola here.
