Pingar has developed a new tool that automatically applies its internally developed text analytics and natural language processing technology to invoices.

It aims to instantly organise and apply metadata to semi-structured data such as invoices.

“A significant number of enterprises regularly fail to make insightful decisions about significant changes in their business and markets,” said Peter Wren-Hilton, Pingar CEO.

“Invoices are exchanged between businesses on an every-day basis, and they all contain the same information: What is the amount of the invoice? Who is issuing the invoice? How is the payment to be made? Capturing and organizing this information can give clues to critical supply chain and cash flow questions and help business analysts make better decisions.”

Semi-structured data includes any documents, which contain table-based information including, application forms, CVs, resumes, invoices, expense sheets, budget lists and itineraries. These documents may be stored in a huge variety of formats and comprise a large volume of data in a company.

Documents may be electronic or need to be scanned. Invoice Analyzer can instantly apply metadata, extract entity information and automatically categorise data into a database for further business operations.

Pingar says that because its soolution relies on advanced natural language processing and text mining algorithms, the accuracy of data collection is improved, making it easier to obtain business intelligence.

Solutions created with Pingar can integrate with any enterprise content management or document management solution.