Fernando Scheinkman

I developed a code that downloads data from SAP. The client needed information from the past four months, which must be extracted from five different transactions. Some transactions were small enough to download the four months together, while others required downloading into two-month, one-month, or half-month segments.

I developed a system that downloaded this information in the most efficient way possible, choosing the combination of periods that would be more efficient. To achieve this, the bot first downloaded the five transactions in each possible way. Then, it stores how much time it took in each combination. With this information, it proceeds to execute the code in the most efficient way. Every time the code executes, the robot stores how much time it took to download and then averages it against the information it had previously, and thus determines if it needs to change how it downloads the information.

I was a part of a major RPA project for an international retail company.

The bot performed the cash count for 352 stores, allowing the client to reallocate the 15 employees who were involved with this project. The bot downloads Excel files from a web portal, which are created by the store's tellers. Then, it inputs the cash register information contained in this Excel file to another web portal. Once completed, it proceeds to execute the cash count in the web portal. If a discrepancy is discovered, it sends an email to the Sales Control team. Because of the volume of the data to process, the bot runs on five devices, four virtual machines, and one notebook, using a queue. One of the major challenges in this process was logging each execution of the process. Because the client wanted access to this information, they wanted an Excel file. However, because there were five devices trying to use this file, the process was both slow and prone to failure. So, I decided to develop a small database that the client could access through an Excel file, which solved the issue.

One thing I am proud of is that the bot not only inputs the data into the web portal, but it also controls the data that's correctly inputted, avoiding duplicates.

A UiPath automation that, based on a text file, searches through five different portals, commercial payors, and government payors and returns with the claim status. This process will generate an output text file to be uploaded to a healthcare ERM.

A Power Automate bot that picks up claims from Epic and checks their status on payor portals such as UHC and Availity. It then returns to Epic and adds the status as an account activity. It then reports the results through a dashboard.

• Designed and implemented an end-to-end lead processing automation using Zapier, integrating web forms, email, and CRM systems.
• Built multi-step Zaps to capture leads from various sources, enrich data, and route them dynamically based on business rules.
• Automated document handling by saving email attachments to cloud storage and triggering downstream workflows.
• Implemented error handling, retries, and logging to ensure reliability.

This solution significantly reduced manual data entry, improved response times, and ensured consistent data synchronization across platforms.