Convert Squarespace Profiles to Shopify Customers in Google Sheets

This workflow receives Webhook events from external systems and sends the event information to PostHog in real-time for user behavior analysis. It supports dynamic parsing of event names, ensuring flexibility and accuracy of the data. This process effectively addresses the complexities and data loss issues in cross-system event data transmission, making it suitable for scenarios that require real-time monitoring of user behavior. It helps teams achieve automated data collection and integration, quickly obtain behavioral insights, and promote data-driven decision-making and product optimization.

This workflow combines visual AI intelligent agents, web scraping services, and multimodal large language models to achieve efficient structured data extraction from web content. By using webpage screenshots and HTML scraping, it automatically extracts information such as product titles and prices, formatting the data into JSON for easier subsequent processing and storage. It integrates with Google Sheets, supporting automatic reading and writing of data, making it suitable for e-commerce product information collection, market research, and complex web data extraction, providing users with accurate and comprehensive data acquisition solutions.

This workflow receives external requests in real-time through a Webhook interface and reads data from specified tables in Google Sheets to quickly return query results. It simplifies the traditional data query process, ensuring instant access to data and automated responses, thereby enhancing efficiency and convenience. It is suitable for scenarios that require quick data retrieval, such as customer service systems, internal data integration, and the development of custom API interfaces.

This workflow automatically extracts and processes sales call records through integration with Salesforce and Gong, filtering for the latest call data and converting it into a standardized JSON format. It regularly retrieves call information from the past four hours, filtering for valid calls to ensure efficient data utilization. Ultimately, the organized data will be passed to the AI processing module for intelligent analysis of sales data, helping the sales team improve performance and customer satisfaction.

This workflow automatically scrapes the latest job information from LinkedIn through the Bright Data platform and synchronizes the cleaned data to Google Sheets. Users only need to submit job search parameters, and the system can retrieve and organize job data in real-time, addressing the cumbersome nature of manual information collection and the complexity of data formats. It is suitable for job seekers, sales and marketing personnel, and HR teams, helping them quickly obtain accurate recruitment updates and improve work efficiency and decision-making quality.

This workflow automatically retrieves order data from the Shopify store every week, quickly calculates the total number of orders and total sales, and records the results in Google Sheets. At the same time, it sends sales report notifications via Slack to help the team stay updated on business dynamics in real-time. This process eliminates the cumbersome traditional manual statistics, ensuring data accuracy and timeliness, making it suitable for e-commerce operations teams, sales analysts, and finance personnel, thereby enhancing work efficiency and team collaboration.

This workflow automatically captures users' running, swimming, and cycling activities by real-time monitoring of Strava's sports data, and conducts in-depth analysis using advanced AI models. It provides users with personalized training feedback and improvement suggestions, helping athletes accurately identify their strengths and weaknesses and develop scientific training plans. Ultimately, the analysis results are sent in a structured HTML format via email or WhatsApp, ensuring that users receive efficient exercise guidance in a timely manner, enhancing their training effectiveness and motivation.

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.