n8n Research AI Agent Intelligent Assistant Workflow

This workflow automates the processing and analysis of financing pitch materials for startups. It detects and downloads PDF files from the Airtable database, uses an AI vision model to transcribe the content into a structured Markdown format, and extracts key information to generate reports. Finally, the data is written back to Airtable and a vector database is constructed, enabling team members to perform natural language queries, significantly enhancing the efficiency of processing financing materials and the convenience of information retrieval.

This workflow automatically generates and edits knowledge base articles by combining multiple AI models. Users only need to input a topic, and the system can conduct in-depth research to produce a structured and content-rich draft, followed by multiple rounds of intelligent editing and review. Ultimately, high-quality articles are automatically published to the content management system, ensuring professionalism and practicality. This process significantly enhances content production efficiency, addressing the time and quality issues associated with traditional manual writing, making it suitable for enterprises and content teams.

This workflow combines intelligent web scraping and API data interaction, allowing it to automatically retrieve relevant information and provide smart recommendations based on users' natural language input. By efficiently utilizing the Firecrawl API to scrape web content and flexibly calling external APIs, it simplifies traditional data processing workflows. The integrated AI Agent and chat model enhance the intelligence of automated responses, significantly reducing development difficulty and time costs, making it suitable for various scenarios such as automated development, customer service systems, and information recommendation.

This workflow automatically filters relevant "Ask HN" posts and comments from HackerNews based on the learning topics submitted by users. It utilizes advanced language models for analysis, extracting high-quality learning resource recommendations, and generates a list in structured Markdown format, which is ultimately sent to the user via email. This process effectively addresses the issue of information overload, helping users quickly find practical learning materials and enhancing their learning efficiency and experience.

This workflow automates the process from receiving a Request for Proposal (RFP) document to generating a complete response document. It intelligently extracts questions from the RFP, automatically generates answers using internal company resources, and organizes them into a structured Google Docs document. Additionally, the system supports email and Slack notifications to ensure the team is promptly informed about the response status. This process significantly improves response efficiency, reduces labor costs, and helps the sales team quickly and accurately address customer needs.

This workflow automatically captures the homepage content of the custom website field when a new organization is created in Pipedrive. It utilizes AI for intelligent analysis to generate detailed notes that include the company description, market positioning, and competitor information. This information is synchronized back to Pipedrive and pushed to Slack after format conversion, ensuring that team members can share customer information in real-time, enhancing sales and customer management efficiency while reducing manual data entry work.

This workflow can automatically monitor a specified Google Drive folder, real-time retrieve the content of newly uploaded Google Docs, and generate intelligent summaries using an AI model. The summaries and the information of the document uploaders will be automatically saved to Google Sheets, facilitating later management and quick reference. This process significantly improves document management efficiency, reduces the time spent on manual organization, and minimizes the risk of omissions, making it suitable for businesses, teams, and educational institutions that need to quickly obtain and organize document information.

This workflow builds an intelligent travel assistant that integrates large language models and vector search technology to achieve personalized travel recommendations and intelligent Q&A functions. Through dynamic data reception and chat memory, users can receive real-time updates on travel information, enhancing the interactive experience. At the same time, the system addresses issues such as the isolation of traditional travel information, inaccurate recommendations, and incoherent interactions, making it suitable for online travel platforms, travel agencies, and personal travel planning, significantly improving service intelligence and travel efficiency.