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This paper introduces a personalized document and

presentation generator powered by machine learning, aimed at

automating content creation while preserving individual writing

style, tone, and formatting preferences. The tool analyzes a

user’s previous documents and presentations to build a long-term

profile, which allows the generation of new content based on the

established style. The research explores various machine

learning algorithms used to enhance content accuracy, reduce

false positives, and offer AI-driven suggestions for improvements.

This research evaluates the tool's effectiveness through rigorous

testing and its potential application in a variety of industries.

This paper presents Netsift, a full-stack web scraping platform designed for authenticated and visual management of scraping tasks. By combining a modular architecture, robust scheduling, and real-time data visualization, Netsift enables both technical and non-technical users to automate and monitor web scraping. The platform integrates modern technologies such as Next.js, Prisma, PostgreSQL, Puppeteer, and Cheerio to ensure scalability, maintainability, and a seamless user experience. This study outlines the system design, implementation strategy, and key components of the platform.