SERPBear Analytics Template

This workflow implements an intelligent chat agent by integrating the OpenAI GPT-4o language model with a Postgres database, allowing users to interact with Google Search Console data using natural language. It automatically parses user requests, generates corresponding API queries, and returns data in the form of Markdown tables. This tool simplifies the data access process and enhances user experience, making it suitable for website operators, SEO experts, and data analysts, enabling them to quickly obtain and analyze website performance data.

This workflow automates the collection and analysis of active data from top community creators and their workflows, generating detailed statistical reports. Utilizing advanced AI language models in conjunction with GitHub data, it provides clear reports in Markdown format, supporting various distribution methods such as email and cloud storage. This systematic process helps community managers efficiently identify outstanding creators, promotes knowledge sharing, enhances user experience, and drives the promotion and application of automated processes.

This workflow integrates AI technology with Google Sheets to enable intelligent customer data querying and analysis. Users can ask questions in natural language, and the AI agent will interpret the intent and invoke the appropriate tools to accurately return customer information, avoiding the inefficiencies and errors of traditional manual queries. The platform supports quick retrieval of column names, specified column values, and complete customer data, enhancing response speed and accuracy. It is suitable for various scenarios such as customer service, sales, and data analysis, simplifying data operations and lowering the usage threshold.

This workflow receives files in Parquet, Avro, ORC, or Feather format via Webhook and uses an online API to convert them into JSON format. It automates the processing of complex binary big data files, simplifies data preprocessing, lowers the technical barrier, and is suitable for data analysis, ETL processes, and development teams, enhancing data utilization efficiency. Users can easily upload files and quickly obtain the parsed JSON data, supporting various application scenarios and facilitating data-driven decision-making and system integration.

This workflow automates the processing of user research data by importing survey responses from Google Sheets, generating text vectors using OpenAI, and storing them in the Qdrant database. It identifies major groups through the K-means clustering algorithm and utilizes large language models to perform intelligent summarization and sentiment analysis on the group responses. Finally, the insights are automatically exported back to Google Sheets, creating a structured research report. This process enhances analysis efficiency and helps decision-makers quickly gain deep insights.

This workflow is manually triggered to automatically extract all order data with a "Completed" status from the Unleashed Software system, helping users efficiently filter and centrally manage order information. It is suitable for finance, sales, or operations teams, effectively reducing the time spent on manual queries, improving the accuracy and efficiency of order management, and facilitating subsequent data analysis and report generation.

The main function of this workflow is to automate the extraction of content from specified web pages. Users only need to provide the webpage URL, and the system will use the FireCrawl API to retrieve the webpage data and convert it into Markdown format for return. This process lowers the technical barrier and enhances extraction efficiency, making it suitable for various scenarios such as AI intelligent agents, office automation, data collection, and content monitoring, thereby facilitating quick integration of web scraping functionality for both developers and non-technical users.

This workflow automates the collection of customer reviews from Trustpilot and utilizes AI technology to extract key information from the reviews and perform sentiment analysis. By structuring the review data and analyzing sentiment trends, businesses can quickly gain insights into customer feedback, monitor brand reputation, and simultaneously update the results in real-time to Google Sheets. This enhances the efficiency of data collection and analysis, supporting market research, customer service improvement, and decision-making.