Relying on humans to extract, enter and manage data is a valuable waste of resources. These activities can be slow, error-prone and expensive, and worse still, no employee enjoys this sort of mundane, repetitive work. 

Staple has developed an ML tool that reads, interprets and extracts structured data from documents faster, more accurately and more affordably than any human can, at scale. Our data extraction capabilities operate successfully regardless of layout or language.

Although business documents like invoices and receipts are common use cases for AP automation, we can an also capture data from semi-structured and unstructured documents including medical claims, bills of lading, trade settlement documentation and many more.

AI/ML

Data

Data Center

Documentation

Enterprise Software

Health Data

Staple

Ben Stein

S G Murali

Adelyn Koh

Hannah Tran

Anand M

Joshua A. Kettlewell

The Engineering team at Staple is responsible for the development and maintenance of our ML tool that automates data extraction from various types of documents. This team works on designing and implementing software solutions, enhancing the user interface, and ensuring the machine learning models operate with high accuracy and efficiency. They collaborate to handle complex data structures, support multi-language capabilities, and continuously improve the tool's performance to offer faster and more precise data extraction at scale.

Engineering

Engineering

About

Team members