Anil Kumar Mohanlal

This project aims to develop an advanced UiPath-based automation solution designed to systematically extract and analyze property-related data from municipal websites across Israel. The automation tool focuses on retrieving detailed information about buildings and flats, including their ownership status, occupancy, and availability for rent or sale. This solution can efficiently handle and process complex Hebrew-language PDF documents by leveraging UiPath's powerful automation capabilities and ABBYY's OCR technology.

OBJECTIVES
• Data Extraction: Collect comprehensive data automatically from various Israeli municipal websites. This includes scraping property records, occupancy statuses, and details about flats and buildings.
• Data Processing: Utilize ABBYY OCR technology to extract and interpret Hebrew text from scanned PDF documents and images, ensuring accurate data extraction.
• Status Analysis: Categorize and analyze the extracted data to determine property statuses, such as whether a building is occupied, available for rent, or up for sale.
• Operational Efficiency: Implement a robust UiPath workflow that can run continuously for extended periods (up to 24 days) to ensure thorough and up-to-date data collection.

In the modern digital landscape, eCommerce platforms have become central to business operations and consumer experiences. As these platforms generate massive volumes of data, businesses and analysts are increasingly seeking to harness this information for a competitive advantage. UiPath, a leading robotic process automation (RPA) tool, offers robust capabilities for automating data extraction processes, making it an invaluable asset for organizations aiming to glean insights from eCommerce websites. This article explores the intricacies of data extraction from eCommerce websites using UiPath, detailing the benefits, challenges, and implementation strategies involved.

UNDERSTANDING THE NEED FOR DATA EXTRACTION
eCommerce websites are rich sources of data, encompassing product information, pricing, customer reviews, inventory levels, and more. Extracting this data can provide valuable insights into market trends, consumer behavior, competitive positioning, and product performance.

Key use cases for eCommerce data extraction include:
• Market research
• Price monitoring
• Product aggregation
• Sentiment analysis

Users email the list of container numbers to RPA email accounts, which prompts the robot to reach this email, download the Excel attachment, and go to different shipper sites to search for container numbers, location, the estimated time of departure (ETD), and the estimated time of arrival (ETA). After the robot collects all this information, it emails back details to the sender.

I worked on automating the prior authorization (auth) process for medical patients. Traditionally, this manual process could take days to weeks to receive prior auth approval and was quite time-consuming. It required searching multiple websites and validating whether the prior auth was approved or if clarifications were requested. By automating these processes across multiple websites, I ensured that updates about clarifications or status changes were communicated efficiently, significantly enhancing the process.

This proof of concept demonstrates an automated method for generating Word invoices using data maintained in Excel. The PoC addresses the challenge of manually transferring large volumes of invoice information—such as invoice number, customer name, invoice date, and amount—from Excel into a Word invoice template. In this model, the Word document is enhanced with a dynamic selection feature that allows the user to choose an invoice number from a list automatically populated from the Excel dataset. Once selected, all related fields in the Word template are filled instantly using the corresponding Excel entries.

The PoC validates the feasibility of integrating Excel as a structured data source with a smart Word template to streamline invoice preparation, reduce manual effort, and eliminate data-entry errors. It demonstrates a scalable approach suitable for larger automation initiatives, supporting faster and more consistent document generation. This controlled prototype confirms that the concept works effectively and serves as a foundation for future expansion. I have the authority to share this PoC video for demonstration purposes.