Resources

Case Studies, Insights, and Publications.

The worlds of AI, ML, and RPA are complex. Simply knowing what questions to ask is a key part of determining what your company may need. Here, we provide insights and publications to help you, if not answer questions, give you enough information so you know which questions to ask.

Automated Invoice Processing

Case Study

Robotic Process Automation (RPA) helps large organizations lower costs, increase speed, and reduce errors. Advoqt implements RPA solutions that can automate processes in various industries, including healthcare, retail, and fnancial services. A common problem our clients bring to our attention is the complexity
of their invoicing and accounting processes (AP and AR).

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Machine Learning For Health Sciences

Case Study

Clinical research requires analyzing large amounts of data to validate results and ensure product safety. Using outdated methods results in increased cost due to extensive human interaction and long timelines. Machine Learning (ML) can reduce cost by increasing the processing speed of the data required for the trial. By automating the data analysis process, a team can accomplish more in less time with the same number of people.

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Machine Learning For Identity Governance

Case Study

Advoqt’s Intelligent Access tool leverages Machine Learning to analyze entitlements and automatically identify users that are provisioned for rights they should not have. The tool can be deployed within a day as part of an auditing program and the analysis takes seconds. It can also be confgured in-line as part of the user provisioning process to prevent errors.

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Machine Learning For Retail

Case Study

Incorrectly recorded distributor agreements, missed opportunities for volume and promotional discounts, and price mismatches within invoices all translate to lost profts. Advoqt uses Machine Learning to solve these problems for retailers.

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Machine Learning & RPA For Retail

Case Study

Currently many retailers do not have an effective inventory forecasting system that is able to predict future minimum inventory levels based on information such as current sales velocity, anticipated sales and historical sales data. Today, Inventory forecasting systems use present stock levels and generate re-order requests once the levels are low. Advoqt has created an accurate forecasting system that predicts future sales down to the SKU and specifc day of the year.

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Machine Learning For Government

Case Study

Most local and state government customer services lines are overwhelmed with the number of resident complaints, inquiries, and concerns they receive daily. This leads to constituent dissatisfaction and inefcient use of public resources. Government entities can use systems that are machine learning enabled to triage incoming calls and answer common questions. High-priority and less common calls can be routed to a human for resolution. This results in better use of public resources, lower wait times, and happier residents.

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Interested in how you can use AI, ML, and RPA to drive efficiencies and profits for your organization?

Martin
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