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Sutherland Extract

Maximize customer engagement, customer satisfaction, and customer lifetime value

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Sutherland Extract is an intelligent data extraction platform that converts documents into meaningful data. Leveraging cutting edge ML, AI, and Natural Language Processing (NLP), the platform is truly cognitive, learning from experience and therefore achieving the industry’s highest digitization outcomes.

Sutherland Extract has pre-built AI models for documents such as invoices, loan application forms, and healthcare documents. These models have experiential learning capabilities, that lead to faster document processing. The platform It is easy to implement and offers integration capabilities with enterprise applications including Robotic Process Automation (RPA), all while remaining cost optimal with reduced Total Cost of Ownership (TCO).



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Industry-Leading Accuracy

With Sutherland Extract, our customers have been able to achieve up to 65% of data extraction accuracy on Day 1, and up to 90% data extraction accuracy between days 60-120 after implementation. Customers can benefit with these market leading efficiencies as the Extract platform leverages Sutherland’s deep process transformation experience servicing global customers across industries.

Optimized Costs

Sutherland Extract offers integration with enterprise platforms and is RPA ready, integrating with Sutherland’s Robility and other RPA platforms available in the market. It is available both on premise as well as on the cloud, meaning customers can leverage Sutherland Extract in a consumption based ‘as a service’ model. Customers can rely on Sutherland’s experienced resources for deployment, thus significantly bringing down the TCO.

Experiential Learning

Sutherland Extract utilizes cognitive data extraction techniques, tapping into Sutherland’s pre-trained models and learning from exceptions. Overall, the Extract platform is able to leverage Assisted Learning and Autolearning capabilities optimally.

It’s time. Start Transforming Your Business Processes Now.

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