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Algorithms and AI techniques are used to solve the interaction between humans and machines and to make machines understanding more easily human data.
Artificial Intelligence is used to improve or automate processes of an enterprise in order to improve revenue or reduce costs or to completely redefine products.
Reply has built its own
Robotics for Customers approach in the context of
Data-Driven Customer Engagement. Robotics for Customers is a framework built on two foundational pillars:
Recommendation Systems and Conversational Systems.
Automated Invoice is the solution, also available as a service, which facilitates the automated management of the accounts payable process, from the posting phase to the reconciliation between invoices and purchase orders/delivery notes/receipts, highlighting the differences identified.
Brick Machine Learning is the solution that makes it possible to simulate various configurations used in automated production lines, in order to recommend the optimal mix of devices required to achieve the overall performance requested by the end customer.
Customer Recovery is the solution that faces the challenge of behavioral approach on credit risk management. The solution is developed on Microsoft Azure Machine Learning, the service that allows building and testing powerful cloud-based predictive analytics.
Employee Monthly Expenses is the solution that facilitates the automated creation of expense reports starting from the underlying cost items, quickly and without the need for manual intervention.
Know your Orders is the solution that makes it possible to create a simple interface which can be consulted by users using a natural language, thus facilitating access to information while ensuring consistency and accuracy.
Match-up is an advanced tool for the analysis, reconciliation and matching of complex data (single and/or multiple). The use of this tool finds application in data-related processes.
The convergence of Big Data with Artificial Intelligence has emerged as the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities.
But even as the technology advances, companies still struggle to take advantage of it, largely because they don’t understand how to strategically implement Machine Learning in service of business goals.