N8N Financial Tracker: Telegram Invoices to Notion with AI Summaries & Reports

This workflow utilizes natural language processing technology to convert email queries posed by users through chat into SQL statements, which are then executed directly to return results. It simplifies the writing of complex SQL statements, lowering the technical barrier, and is suitable for scenarios such as enterprise email data analysis and quick identification of email records for customer support. Through multi-turn conversations and manual triggers, users can efficiently and accurately retrieve email data, enhancing work efficiency, making it an effective tool for intelligent email data retrieval.

The main function of this workflow is to automatically monitor Amazon product prices. It regularly reads the product list from Google Sheets and uses the ScrapeOps API to fetch real-time prices and detailed information. It can calculate both the absolute value and percentage of price changes, intelligently assessing the trend of price increases and decreases. When the price exceeds the threshold set by the user, it sends an email notification to the user, helping them to promptly grasp price fluctuations, avoid missing out on discounts, or respond to the risk of price increases. Overall, it enhances the efficiency and accuracy of price monitoring.

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.