Umami Analytics Template

This workflow is an efficient tool for detecting anomalies in agricultural crops, capable of automatically identifying whether crop images are abnormal or unknown. Users only need to provide the URL of the crop image, and the system converts the image into a vector using multimodal embedding technology, comparing it with predefined crop category centers to determine the image category. This tool is suitable for scenarios such as agricultural monitoring, research data cleaning, and quality control, significantly improving the efficiency and accuracy of crop monitoring.

This workflow can automatically read and convert data from local JSON files, and then append it to a specified Google Sheets spreadsheet. Through secure OAuth2 authentication, it ensures the safety of data operations, greatly simplifying the data import process, avoiding cumbersome manual tasks, and enhancing the efficiency and accuracy of data processing. It is suitable for businesses and individuals who need to regularly organize and analyze data, helping to achieve efficient data management and decision-making.

This workflow automates the crawling of social media links from specified company websites and outputs the data in a standardized JSON format. By integrating text and URL scraping tools, along with the OpenAI GPT-4 model, it ensures the accuracy and completeness of the data. It supports multi-page crawling and deduplication features, significantly enhancing the efficiency of data collection and addressing the complexities and information fragmentation issues of traditional manual collection processes. This workflow is suitable for professionals in fields such as marketing, data analysis, and recruitment.

The main function of this workflow is to automatically convert customer data from the Squarespace platform into a Shopify-compatible data format and update it in real-time to Google Sheets. It receives data through Webhooks, supports batch processing and manual triggering, ensuring data integrity and timeliness. This effectively reduces errors caused by manual operations and improves the efficiency of e-commerce businesses in managing customer information and marketing activities, making it suitable for users who need cross-platform data integration.

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.