Intelligent Triathlon Coach (AI Triathlon Coach)

This workflow automatically processes uploaded PDF files by listening to events from the Baserow table. It utilizes an AI language model to extract key information from the PDFs and populates the corresponding fields in the table, supporting dynamically defined extraction rules for intelligent data entry. This process significantly improves data processing efficiency, reduces manual operations and errors, and is suitable for document management scenarios such as contracts and invoices, aiding in the digital transformation of enterprises.

This workflow automates the retrieval of detailed data for main project items and their sub-items from Monday.com, recursively obtaining associated contact information and structuring the data. It supports converting the results into JSON format for easy subsequent upload or export. With a flexible process design, users can efficiently handle multi-level task data, avoiding manual queries and enhancing project management transparency and collaboration efficiency. It is suitable for teams and analysts who need to export or integrate data in bulk.

This workflow is triggered at regular intervals and automatically retrieves real-time location data of the International Space Station every minute, including latitude, longitude, and timestamps. It features an intelligent deduplication function to ensure that the output trajectory points are the most recent and unique, preventing duplicate records and thereby enhancing the accuracy and timeliness of the data. It is suitable for aerospace research institutions, educational projects, and aerospace enthusiasts, enabling efficient monitoring and analysis of the dynamics of the International Space Station.

This workflow is designed to automatically monitor competitors' pricing information. It begins by retrieving pricing page links from Google Sheets and uses intelligent extraction tools to analyze prices and features. By comparing with historical data, it identifies price changes in real time and feeds the updated information back into Google Sheets. Additionally, it notifies the team via Slack to ensure timely awareness of market dynamics. This process effectively reduces manual checking time, improves data flow efficiency, and helps businesses quickly adjust strategies to enhance market competitiveness.

The main function of this workflow is to automate the comparison of two datasets, allowing for the identification of common items, differences, and unique items. It supports multiple output options, facilitating subsequent data processing and in-depth analysis. With a streamlined design, users can quickly generate datasets and perform comparisons, enhancing the efficiency and accuracy of data verification. It is suitable for scenarios such as data analysis, quality checks, and cross-department collaboration. This is an efficient tool that helps users easily master data comparison techniques.

This workflow enables the batch reading, deduplication, filtering, and date sorting of multiple CSV files, and automatically imports the processed data into Google Sheets. It supports the identification and integration of the latest subscriber data, significantly improving data processing efficiency and addressing the time-consuming and error-prone issues of traditional manual processing. It is suitable for fields such as marketing, data analysis, and content operations, helping teams stay updated on user subscription status in real-time, and supporting informed decision-making and strategy formulation.

This workflow regularly retrieves website keyword ranking data from the SERPBear platform, automatically parses it, and generates a summary of keyword performance. The data is then sent to an AI model for in-depth analysis, and the results are finally saved to a Baserow database. The purpose is to help website operators and SEO practitioners efficiently monitor changes in keyword rankings, identify well-performing and under-optimized keywords, thereby enhancing the scientific accuracy of SEO decision-making and reducing the workload of manual analysis.

This workflow automates the processing of transaction receipt images received by a LINE chatbot. By uploading the images to Google Drive and using OCR technology to recognize the information within them, the system can accurately extract transaction details and automatically record the data in Google Sheets. This process significantly enhances the efficiency and accuracy of manual data entry, addressing the challenge of structuring image information for storage. It is suitable for scenarios where efficient management of transaction receipts is needed, such as in finance departments, individuals, and small businesses.