Customer Story

How BiagiBros automated Shipment Notification For 3PL Warehouses using Docsumo

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With Docsumo, we are now saving more than 500 hours per month.

“With Docsumo, we are now able to assign barcodes in less than 2 mins. The same process used to take us 20 mins previously. We are now saving hundreds of hours a month generating Advanced Shipment Notifications. It has reduced manual errors drastically.”

Neil Lawrence

Business Process Manager, BiagiBros

About the customer

BiagiBros is a 3PL warehousing company providing businesses and organizations with supply chain solutions allover the US. With distribution centers, warehouses, and truck terminals throughout the country, BiagiBros handlesmore than 1500 deliveries a month.
Supply Chain
Company size
700+ Employees
Portfolio Units
1,500+ per Month
Document Processed
3,000+ ‍per Month

The case study: In a nutshell


Manually scanning unstructured bill of lading documents
The client captures data for 50+ inbound leads on a daily process
Scanning data from 10+ bill of lading types manually is cumbersome
Little to no validation done on captured data
All documents had to undergo double manual entry


Capture data from unstructured documents with smart AI-based APIs
Employees review only exceptions
All the variations in layout are taken care of by ML-based smart data extraction API
Docsumo's algorithms auto-classify letters and validate data with custom rules in real-time
95%+ straight through processing

The Challenge

Process unstructured bill of lading

  • Biagibros scans and extract data from bill of lading documents to generate barcodes.

Identify & classify documents

  • Biagibros needs to classify different types of bill of lading and queue for manual data extraction
  • Data to extract includes customer order information and careeir details.

Capture data from bill of lading to generate barcodes

  • Not only did the structures vary for different bill of lading documents but the position of data to capture varies
  • Some of this data was in tabular formats.

Categorize & derive attributes from extracted data

  • The manual extraction lacked a logical validation to ensure accuracy.

The Docsumo Solution

Ingesting bill of lading

  • API-based direct integration that seamlessly ingests bill of lading onto Docsumo.

Pre-processing and getting ready for data extraction

  • Inbuilt document pre-processors identified the letter formats (JPG, PDF, PNG etc.) and queued them up for data extraction.

Data extraction from unstructured text

  • Docsumo's OCR module used the vectorized position reference in a letter to extract data.
  • The OCR not only parsed through letters with varying fonts, layouts, image quality, and resolution; it even extracted data from the tables with 99%+ accuracy.

Intelligent categorization of key value pairs

  • Our proprietary NLP-based classification framework started rapidly learning from all the documents. It was trained to categorize key value pairs and line items.
  • Another algorithm started making intelligent predictions to identify the data within a document.

Rule-based data validation

  • Once the data is extracted, a rule-based validation engine applied contextual data validation and correction algorithms.

Integration with downstream software

  • The data was extracted in a JSON format that was easily integrated into DellBoomi and Highjumpinto.

Result: 99%+ Data extraction accuracy

Hours of processing time saved per month
Processing cost saved per month.
Processing cost reduced by automating workflow end to end.
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G2 & Capterra Ratings for Docsumo