Optimizing Purchase Order Data Extraction with Machine Learning


Case Study


Wissen Team


May 24, 2024


Our client, a leading global manufacturing firm, faced challenges in efficiently extracting data from purchase orders loaded into their CRM system by customers. The manual extraction process, prone to errors and language barriers, incurred significant costs for the firm. They needed a solution to automate this process and turned to Wissen for assistance.

Analyzing the Problem

  • Manual data extraction inefficiencies
  • Language and format variability in PDF purchase orders
  • High cost associated with manual intervention

Initial Challenges

  • Difficulty in automating data extraction across formats and languages
  • Risk of errors in the manual extraction process
  • Cost inefficiencies due to manual labor

Our Solution

  • Leveraging Wissen's Machine Learning expertise, we developed a robust solution to automate the extraction of data from purchase orders:
  • Utilized Machine Learning algorithms to train the system in various formats and languages.
  • Incorporated spatial relations and typographical information to extract data accurately from PDFs.
  • Implemented a learning mechanism allowing the system to adapt and automatically extract data from new documents of the same format.
  • Developed a language-agnostic and scalable solution capable of extracting complex information from headers and line items.
  • Offered an additional Optical Character Recognition (OCR) solution to extract data from scanned images, complementing the PDF Data Extractor.

Key Results Achieved

  • Significant reduction in manual labor costs
  • Improved accuracy and efficiency in data extraction
  • Streamlined purchase order processing
  • Enhanced scalability across geographies


Harnessing the power of Machine Learning and innovative data extraction techniques, Wissen successfully addressed our client's challenge of automating purchase order data extraction. The solution reduced manual intervention and associated costs while enhancing accuracy and scalability across diverse formats and languages. Moving forward, continuous refinement of the solution will further boost its effectiveness and drive operational efficiencies for our client.