ERP AI Chatbot for Odoo Sales Module

This workflow receives users' dietary records via Telegram, including text and voice messages. It utilizes AI technology to intelligently analyze the nutritional components of the ingredients and automatically stores the structured data in Google Sheets. It addresses the cumbersome issues of traditional dietary recording, supporting health management, exercise nutrition tracking, and medical rehabilitation, providing users who are concerned about dietary health with a convenient and efficient tool for recording and analysis.

This workflow integrates DeepSeek's latest V3 chat model and R1 inference model, supporting real-time conversations triggered by messages and possessing multi-turn contextual understanding capabilities. Users can flexibly call cloud APIs or local models to quickly build intelligent Q&A and inference services, suitable for scenarios such as customer service, knowledge management, and educational tutoring. By enhancing interaction coherence and accuracy through memory window management, it reduces the complexity of AI integration, making it easier for developers and enterprises to build and test intelligent assistants.

This workflow can automatically process YouTube video links provided by users, verify their validity, and extract video subtitles. Through powerful API services and AI models, the extracted text undergoes grammar correction and formatting, ultimately returning clear and readable transcribed content. This process eliminates the need for manual video viewing, allowing learners, content creators, and corporate employees to quickly access the core information of the videos, thereby effectively enhancing learning and work efficiency.

This workflow automatically reads keywords in bulk from Google Sheets, using an AI intelligent agent to analyze whether each keyword is related to known IT software, services, or tools. The final classification results are then updated back to the spreadsheet. It effectively addresses the inefficiencies and errors of manual analysis while preventing API call frequency limitations, ensuring a stable and efficient process. This workflow is suitable for scenarios such as SEO research, market research, and keyword database management.

This workflow utilizes AI technology to automatically scrape and analyze corporate blog content, extract article structure and brand voice characteristics, and then generate new article drafts that align with the brand style, which are directly saved to WordPress. This significantly enhances the efficiency and consistency of content creation, addressing issues such as brand voice standardization, maintaining content style, and lengthy production cycles. It is applicable in various scenarios including content marketing, brand management, and for creators.

This workflow achieves automated questioning and answering functions by calling an intelligent Q&A interface. Users can preset prompts and questions, as well as specify the search domain, to obtain structured response content. The returned results are cleaned and formatted for easier subsequent display or processing, simplifying the interaction process with the intelligent Q&A service and enhancing integration efficiency. It is suitable for scenarios such as corporate knowledge bases, automated customer service responses, and product inquiries, helping users quickly obtain and organize information, thereby improving work efficiency.

This workflow is an intelligent conversational AI assistant that can automatically trigger dialogues by receiving chat messages. It combines contextual memory, real-time web search, and a powerful language model to enhance the intelligence and accuracy of conversations. After user input, the system generates natural and fluent responses based on historical dialogues and the latest information, making it suitable for scenarios such as smart customer service, Q&A systems, and personal assistants. It provides a richer interactive experience and an efficient automated communication solution.

This workflow implements a private and locally deployed dynamic router that intelligently selects the most suitable local large language model for responses based on user input. It supports various specialized models, ensuring that the entire process runs locally to safeguard data privacy and security. With built-in decision trees and classification rules, it automatically schedules models and manages contextual memory, enhancing the interaction experience and task processing efficiency, making it suitable for user groups that require efficient and diverse task handling.