Scrape Web Data with Bright Data, Google Gemini, and MCP Automated AI Agent

This workflow implements the automatic collection and sentiment analysis of customer feedback, ensuring that data processing is efficient and accurate. After customers submit feedback through a customized form, the system automatically utilizes AI technology for sentiment classification and integrates the analysis results with the original data, ultimately storing it in Google Sheets. This process not only enhances the response speed of the customer service team but also helps product managers and market researchers quickly gain insights into customer satisfaction and needs, facilitating improved business decision-making and service quality.

This workflow combines web data scraping and large language models to achieve structured data extraction and deep analysis of web pages. Users can automatically retrieve and parse web content, extract themes, identify trends, and conduct sentiment analysis, generating easy-to-understand reports. It supports saving results as local files and provides real-time notifications via Webhook, making it suitable for various scenarios such as media monitoring, market research, and data processing, significantly improving the efficiency and accuracy of data analysis.

The main function of this workflow is to automatically retrieve website traffic data from Google Analytics, analyzing page engagement, search performance, and country distribution over the past two weeks. By utilizing AI to intelligently interpret the data, it generates professional SEO optimization recommendations and saves the results to a Baserow database for easier management and tracking. This process simplifies data comparison and analysis, enhancing the efficiency and accuracy of SEO decision-making, making it highly suitable for website operators and digital marketing teams.

This workflow is designed to convert webpage HTML content into structured Markdown format and extract all links from the webpage. By utilizing the Firecrawl.dev API, it supports batch processing of URLs, automatically managing request rates to ensure stable and efficient content crawling and conversion. It is suitable for scenarios such as data analysis, content aggregation, and market research, helping users quickly acquire and process large amounts of webpage information, reducing manual operations and improving work efficiency.

The smart factory data generator periodically generates simulated operational data for factory machines, including machine ID, temperature, runtime, and timestamps, and sends it to a designated message queue via the AMQP protocol. This workflow effectively addresses the lack of real-time data sources in smart factory and industrial IoT environments, supporting developers and testers in system functionality validation, performance tuning, and data analysis without the need for real devices, thereby enhancing overall work efficiency.

This workflow is a web content scraping and processing tool that can automatically retrieve web page content from specified URLs and convert it into Markdown format. It supports two scraping modes: complete and simplified. The simplified mode reduces links and images to prevent excessively long content from wasting computational resources. The built-in error handling mechanism intelligently responds to request exceptions, ensuring the stability and accuracy of the scraping process. It is suitable for various scenarios such as AI chatbots, data scraping, and content summarization.

This workflow aims to automate the scraping of customer reviews for specified companies on Trustpilot, utilizing a vector database for efficient management and analysis. It employs the K-means clustering algorithm to identify review themes and applies a large language model for in-depth summarization. The final analysis results are exported to Google Sheets for easy sharing and decision-making within the team. This process significantly enhances the efficiency of customer review data processing, helping businesses quickly identify key themes and sentiment trends that matter to customers, thereby optimizing customer experience and product strategies.

This workflow automates the scraping and sentiment analysis of Twitter and external form content. It regularly monitors the latest tweets related to "strapi" or "n8n.io" and filters out unnecessary information. Using natural language processing technology, it intelligently assesses the sentiment of the text and automatically stores positively rated content in the Strapi content management system, enhancing data integration efficiency. It is suitable for brand reputation monitoring, market research, and customer relationship management, providing data support and high-quality content for decision-making.