Chat with Google Sheet

This workflow intelligently synchronizes company and contact information to the Salesforce platform by automatically downloading and parsing Excel files. It can automatically identify whether a company account already exists to avoid duplicate creation, while also supporting bulk updates and additions of contact data, significantly improving the efficiency of sales and customer management. It is suitable for teams that need to efficiently import external customer data and maintain their CRM systems, reducing errors caused by manual operations and enhancing the accuracy and timeliness of data management.

This workflow utilizes a locally deployed Mistral NeMo language model to automatically receive and analyze chat messages in real-time, intelligently extracting users' personal information. It effectively addresses the inefficiencies and error-proneness of traditional manual processing, ensuring that the extraction results conform to a structured JSON format, while enhancing data accuracy through an automatic correction mechanism. It is suitable for scenarios such as customer service and CRM systems, helping enterprises efficiently manage customer information while ensuring data privacy and security.

This workflow automatically retrieves real-time location information of the International Space Station (ISS) every minute, organizes the data, and pushes it to a specified Kafka topic, achieving high-frequency updates and distribution of orbital data. Through this process, users can monitor the ISS's position in real time, avoiding manual queries and ensuring that data is transmitted quickly and stably to downstream systems, supporting subsequent analysis and visualization. It is suitable for various scenarios, including aerospace research, real-time tracking, and big data applications.

This workflow efficiently completes contact information through batch asynchronous calls to the Dropcontact API, supporting up to 1,500 requests per hour. It automatically filters eligible contact data, ensuring that the data format is standardized, and employs batch processing with a waiting mechanism to prevent request overload. The completed information is updated in real-time to the Postgres database, and it includes anomaly monitoring and alerting features to ensure process stability. This workflow is suitable for enterprise CRM, marketing teams, and data management, significantly enhancing data quality and processing efficiency.

This workflow automates the process of identifying missing webpage titles and description information from Airtable. It then fetches the corresponding webpage content, extracts the <title> tag and <meta name="description"> content, and writes the extracted SEO metadata back to Airtable. This process requires no manual intervention, significantly improving the efficiency and accuracy of data maintenance, addressing the issue of incomplete webpage SEO metadata, and helping website administrators and content operations teams easily optimize SEO performance.

This workflow automatically extracts data from uploaded PDF files through dynamic field descriptions and updates Airtable records in real time, significantly improving data entry efficiency. Utilizing Webhook triggers, the system can respond to the creation and updating of forms, and, combined with a large language model, intelligently parses PDF content. It supports both single-line and batch processing, addressing the time-consuming and error-prone issues of traditional manual information extraction, making it suitable for the automated management of documents such as enterprise contracts and invoices.

This workflow automatically scrapes financing news from major technology news websites, accurately filters and extracts key information such as company names, financing amounts, and investors. It combines various AI models for in-depth semantic analysis, providing detailed company backgrounds and market analysis. The research results are automatically stored in an Airtable database for easy management and subsequent analysis, helping venture capitalists, researchers, and business decision-makers to access industry trends in real-time, thereby improving decision-making efficiency and information value.

This workflow automatically updates the exchange rates of the US dollar against various currencies daily by calling an external exchange rate API to obtain the latest data. The data is then formatted and the updated exchange rate information is written into a specified Google Sheets document. Additionally, historical exchange rate data will be archived for easy future reference and analysis. This process is suitable for cross-border e-commerce, foreign trade companies, and finance teams, enhancing the efficiency and accuracy of exchange rate data maintenance while reducing the complexity of manual operations.