Your team already has the shipment information. The problem is moving it from emails, PDFs and freight documents into the right system without typing it again.
Automatic freight data capture extracts shipment details into structured fields. Validation rules and human review help resolve missing or conflicting information before approved data moves downstream.
This guide explains how that process works, where OCR fits, and what to test before using it in your freight operation.
From Freight Documents to Usable Data
FreightGraph AutoFill is an AI-powered freight document automation application that turns incoming documents and messages into structured logistics data.
It can process information from sources including emails, PDFs, scans, images, BOLs, PODs, rate confirmations, invoices, load tenders and email attachments.
Instead of requiring an employee to repeatedly read and retype information, AutoFill can identify and extract freight-specific fields such as shipper and consignee details, BOL/PRO numbers, pickup and delivery dates, origin and destination, equipment information, commodity, weight, pieces, line-haul charges and accessorials. Custom fields can also be configured around a company’s workflow.
How AutoFill Works
The process is built around four stages: Capture → Understand → Validate → Deliver.
Documents can enter AutoFill through configured channels such as an inbox, upload, scan or system connection. AutoFill identifies the document type and maps its contents to the fields required by the workflow.
What happens when data is missing or incorrect?
A missing shipment reference or an unclear charge needs attention before it reaches the next system. AutoFill applies validation rules and configurable confidence thresholds to identify fields that need review. A reviewer can compare a flagged value with the original document, correct it, and approve the record.
Poor scans and handwriting still need testing on your own documents. Confidence scores help direct attention, but they don’t guarantee that a value is correct. Amazon Textract’s guidance similarly recommends choosing review thresholds according to the use case.
A BOL example: from document to reviewed fields
Consider a hypothetical BOL containing a shipment reference, shipper, consignee, weight and pickup date. Most values are readable, but the weight is blurred and the pickup date is missing.
| Field | Initial extraction | Review action |
|---|---|---|
| BOL number | BOL-10428 | Check against the source |
| Shipper | Example Manufacturing | Check against the source |
| Consignee | Example Distribution | Check against the source |
| Weight | Unclear | Confirm the value and unit |
| Pickup date | Not found | Obtain the missing information |
The reviewer resolves the flagged fields before approving the record. If the source doesn’t contain a required value, the team needs to obtain it rather than treat a guess as extracted data.
This is an illustrative workflow, not a measured AutoFill result. For a technical example of BOL fields returned as structured data with source references, see Extend’s extraction documentation.
More Than OCR
Traditional OCR can turn an image of a document into machine-readable text. But readable text isn’t necessarily usable freight data.
AutoFill goes further by classifying freight documents, identifying freight-specific fields, applying validation rules and preparing structured output for operational systems.
For example, instead of simply recognizing an address or number somewhere on a BOL, the workflow can map information to the appropriate fields required by the operation.
What if carriers use different document layouts?
A BOL from one carrier may place shipment details in different locations from another. AutoFill uses freight context to identify fields rather than relying on fixed templates. Before deployment, test documents from your actual carriers, including unfamiliar layouts and multi-page files, to establish which variations work reliably and which need review.
Built to Work With Existing Freight Systems
AutoFill isn’t intended to force freight teams to replace the systems they already use.
Approved data can be mapped into existing workflows and platforms, including TMS, WMS, ERP, accounting systems, CRM platforms, document-management systems, custom platforms and databases, depending on the configured integration.
This creates a much simpler flow:
Freight document → AutoFill → Review & validation → Structured data → Existing system
Why Automate Freight Data Entry?
Manual retyping isn’t just repetitive. It can slow operational decisions, delay billing and introduce transcription errors.
FreightGraph positions AutoFill around reducing that manual translation step. When documents are processed sooner, shipment information can become available earlier for operational workflows, while validation and exception handling remain part of the process.
The result is a workflow designed around:
Less manual data entry. Cleaner freight data. Faster access to shipment information. More processing capacity as document volume grows.
And once freight information becomes structured instead of remaining trapped inside PDFs, emails and attachments, it can also become more searchable, reportable and useful for analytics.
Who Is AutoFill For?
AutoFill is designed for document-heavy freight operations, including freight brokers, carriers, shippers, freight forwarders, 3PLs and logistics technology providers.
The implementation can start with a single high-volume workflow—such as BOLs, PODs, rate confirmations or invoices—and expand to additional document types and destinations as needed.
How do you evaluate extraction quality?
Start with a representative set of your own documents, including clean PDFs, scans and the formats that currently take the most effort. Have a reviewer establish the correct values, then compare the extracted fields against that reference.
Measure results by field. A correctly extracted company name doesn’t make up for an incorrect shipment number or weight. Google Document AI’s evaluation guidance explains how precision, recall and F1 can be used to assess extraction against labelled test documents.
Also record how many fields need correction and the total handling time, including review. This answers a practical question: does automatic freight data capture reduce the work required to produce a usable record?
Begin with one workflow, such as BOL processing, and use the results to decide what to expand next.
Stop Entering Freight Data. Start Using It.
Your freight data is already arriving every day.
The question is how much time your team should spend manually moving it from one document to another system.
FreightGraph AutoFill turns incoming freight documents into structured, validated data your operation can actually use.
Instead of copy-pasting shipment information, let AutoFill handle the document-to-data workflow while your team focuses on the work that requires human judgment.
Ready to see AutoFill with your own freight documents? Explore FreightGraph AutoFill