Amazon Product Price Tracker

This workflow utilizes automated browser technology and AI models to achieve intelligent web data scraping and analysis. It supports data collection in both logged-in and non-logged-in states, automatically searching for and filtering valid web links, extracting key information, and performing image analysis. Additionally, it has a built-in multi-layer error handling mechanism to ensure the stability of the scraping process. It is suitable for various fields such as data analysis, market research, and automated operations, significantly enhancing the efficiency and accuracy of data acquisition.

This workflow focuses on the automatic identification and integration of information from Chrome extension plugins on LinkedIn pages. By converting extension IDs into detailed names, descriptions, and links, it achieves efficient management and analysis of data by storing the results in Google Sheets. Users can process extension IDs in bulk, avoid duplicate queries, and update information in real-time, significantly enhancing the efficiency of monitoring and analyzing browser extensions. This helps IT security personnel, data analysts, and others to better understand users' extension usage.

This workflow automatically retrieves SEO data from Google Search Console every week, generates detailed reports, and sends them via email to designated recipients. It addresses the cumbersome process of manually obtaining data and the issue of untimely report delivery, ensuring that teams or individuals can stay updated on the website's search performance in a timely manner, thereby enhancing the efficiency and accuracy of data analysis. It is suitable for website operators, SEO analysts, and digital marketing teams, helping them better monitor and optimize the website's search performance.

This workflow automates the processing of survey data by identifying similar response groups through vector storage and K-means clustering algorithms. It combines large language models for summarization and sentiment analysis, and finally exports the results to Google Sheets. This process is efficient and precise, capable of deeply mining potential patterns in text responses. It is suitable for scenarios such as market research, user experience surveys, and academic research, helping users quickly extract key insights and enhance the scientific and timely nature of decision-making.

This workflow automatically scans the real estate market in specific areas on a regular basis, utilizing the BatchData API to obtain the latest property data. It identifies newly emerged or changed property information and filters out high-potential investment properties. By generating detailed property reports and promptly notifying the sales team via email and Slack, it ensures they can quickly grasp market dynamics and investment opportunities, thereby enhancing decision-making efficiency and transaction speed while reducing the hassle of manual tracking.

This workflow automates the processing of YouTube video links in Airtable. It retrieves video transcription text through a third-party API and utilizes a large language model to generate content summaries and key points. Finally, the structured information is written back to Airtable, enabling efficient organization and management of video content. This process significantly enhances the work efficiency of content creators, knowledge management teams, and market researchers when handling video materials, addressing the issues of manual organization and information fragmentation.

This workflow can automatically crawl user reviews of specified companies from the Trustpilot website, extract key information from the reviews, and perform sentiment analysis. Using the DeepSeek model, it accurately retrieves multidimensional information such as the reviewer's name, rating, date, and more. It then utilizes OpenAI to classify the sentiment of the reviews, achieving automatic collection and intelligent analysis of review data. Finally, the data is synchronized and updated to Google Sheets, providing strong support for brand management, market research, and customer service.

This workflow automatically scrapes Bing Copilot's search results through the Bright Data API and utilizes the Google Gemini AI model for structured data extraction and content summarization. It addresses the issue of disorganized traditional search result data, enhancing information utilization efficiency. Users can quickly obtain search information related to keywords, aiding in market research, competitive intelligence analysis, and content creation. Ultimately, the processed results are pushed via Webhook, facilitating subsequent integration and automation.