The automation of web scraping has become increasingly significant as data-driven decision-making expands in both academic and commercial sectors. Netsift addresses the challenges of scalability, security, and ease-of-use by providing an end-to-end solution for creating and managing scraping tasks. The platform is built around a modular architecture that integrates secure authentication, task scheduling, and visual workflow management, making it accessible to users with varying technical expertise.

This paper describes the architectural design, key functionalities, and the technological stack underlying Netsift.

.This paper proposes a solution to this problem in the form of a personalized document and presentation generator. Powered by machine learning, the system learns from a user’s previous documents and presentations to identify their writing style, preferences, and formatting choices. By doing so, it automates the process ofcreating new content, ensuring it adheres to the user's unique voice.

The paper outlines the core components of this system, the methodologies used for training and evaluating machine learning models, and the results from testing the tool. In particular, the goal is to demonstrate how the use of machine learning can streamline content creation while preserving individual style preferences.

Numerous web scraping tools exist; however, few offer the comprehensive functionality found in Netsift. Traditional frameworks such as Puppeteer and Cheerio are often used in isolation for data extraction, while integrated platforms rarely feature advanced user interfaces or role-based access control.

Previous research has focused on automated document generation and data extraction; here, we build on these studies by embedding a visual pipeline builder and a freemium model for enhanced user engagement.

These systems primarily rely on deep learning models, such as recurrent neural networks (RNNs) and transformers, which are effective in replicating user-specific language and style. However, existing models have limitations when it comes to understanding complex formatting preferences, which are crucial for creating presentations and reports.

This paper builds upon the existing body of research by introducing a hybrid approach that combines machine learning techniques for both content generation and style preservation. Furthermore, it incorporates userfeedback to improve the accuracy of the content generation process over time.

For the purpose of analyzing writing styles and content preferences, we employed a combination of document formats (PDF, Word, PowerPoint) that users frequently upload. These datasets were used to extract the following features:

These features form the basis of a long- term user profile, which is updated with each new document or presentation uploaded.

• System Architecture and Stack Netsift is developed using a layered architecture that decouples thefrontend, backend, and scraping engine.
  • Frontend: Implemented with js and styled with Tailwind CSS for hybrid rendering and responsive design.
  • Backend: Utilizes Prisma ORM to interact with a PostgreSQL database, managing user data, scrape configurations, and logging.
  • Scraping Engine: Combines Puppeteer for dynamic JavaScript-rendered content and Cheerio for static HTML parsing.
  • Scheduling System: Integrates cron- parser and cronstrue for flexible, human- readable scheduling of scraping tasks.

4.2   Core Features and Workflow

Key functionalities of Netsift include an authenticated dashboard with role-based access (via Clerk), a visual workflow builder (using XYFlow), and form validation with React Hook Form and Zod. Additionally, the platform supports real- time monitoring of scraping tasks, which are executed on schedules and logged for performance tracking.

4.3   Monetization and Access Control

Stripe integration facilitates a freemium model where basic scraping features are accessible for free, and premium users gain access to advanced capabilities such as multi-site scraping and enhanced workflow visualization. User roles and secure routing are managed by Clerk, ensuring a robust and scalable solution

The findings of the research highlight the effectiveness of Netsift. Although Netsift has not yet been benchmarked against commercial- grade scraping platforms, its modular architecture and integration of modern web technologies position it as a promising solution. Preliminary testing indicates that the system efficiently manages multiple scraping tasks and provides real-time monitoring with minimal delay. Future work will include detailed performance evaluations, user acceptance testing, and scalability assessments under different load conditions.

Complex DOM Structures: The tool faces difficulties in accurately parsing and replicating highly intricate HTML DOM layouts, such as dynamic web content with nested elements or JavaScript-rendered

Feedback Integration: While the feedbackloop     enhances personalization, further optimization is needed to ensure real-time model updates based on user preferences.

Conclusion 

This paper presents a personalized document and presentation generator , Netsift exemplifies how a full-stack, user-centric design can simplify the complexities associated with web scraping. By offering a secure, visually driven interface and robust scheduling capabilities, the platform demonstrates a strong potential for deployment in data-driven applications. Future enhancements will aim to refine the performance and extend the feature set to supporteven more sophisticated data extraction workflows.                                                              

9. References

Singh, V., & Verma, A. (2020). A Review on Web Scraping Techniques, Tools and Applications. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 761-765).

https://doi.org/10.1109/ICCES48766.2 020.9137987

Sudhakar, P., & Dinesh, K. (2020). Automated Web Data Extraction and Mining Using Python. In 2020 International Conference on Computer Communication and Informatics (ICCCI) (pp.    1-5).   

https://doi.org/10.1109/ICCCI48352.20 20.9104152