We leverage a combination of RPA, generative AI, intelligent workflow and other cognitive automation tools to streamline routine day-to-day activities and release human energy to more creative tasks.
Intelligent Process Automation (IPA) is a technology stack that allows you to manage, automate, and integrate digital systems. Artificial Intelligence (AI), Robotic Process Automation (RPA), and Digital Process Automation (DPA) are the three main technologies that make up IPA. A typical application for Intelligent Process Automation is when organizations need to get data to customers but manual processes, such as handling insurance claims or streamlining customer requests, take a long time.
Artificial intelligence-powered intelligent search breaks down data silos, allowing employees and customers to find the information they need more easily and quickly. End-users can use intelligent search to obtain data from any destination (within or outside the organization) and any data set, irrespective of format: on paper, big data in databases, webpages, digital content, document management systems, and anywhere else. We leverage tools like ChatGPT to design efficient and cost-effective solutions.
By analyzing existing simulation data, AI is proving to be a viable approach for reducing the less essential simulation scenarios. AI sampling will generate individual simulations to be used for a variety of purposes, such as training AI-models. These scenario simulations can aid a company's ability to move quickly and reduce risk by allowing them to create, test, and learn about possible improvements before they are implemented for customers and employees.
Resource optimization enables organizations to meet resource requests as efficiently as possible. Resource optimization compares all open resource requests to available resources and devises a strategy to help you achieve the company's goals. Adaptive workload management was developed to intelligently assign data resources across workflows, either automatically or by supplying an alert based on the preferences of the business. Machine learning is an essential component of this process, tracking anticipated and real runtimes to make predictions for future workflows and adapt resources accordingly.
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