Agents & Automations

Target Audience You will find this workflow or template perfect if you are in the internal talent acquisition teams, recruitment agencies, HR professionals, and hiring managers seeking to bulk automate the initial screening of CVs and resumes. Eg. Automatically get the result of candidates who have been shortlisted/rejected with their rationale and score automatically. By eliminating manual evaluation and screening, you get a smart AI-Agent helping you to have a standardized, efficient, and scalable solution for handling large volumes of applications. With bulk automation, you can focus on strategic decision-making rather than tedious screening tasks, ensuring a faster, more accurate, and fair hiring process. Key focus This workflow focuses on having a more organized file-folder management, trackable candidate CV, maintainable job description, and an autonomous AI-agent. - Organized Folder-File Structure – CVs are automatically categorized based on their status, ensuring a structured workflow and easy retrieval. - Candidate Tracker – A real-time tracking system records the state of each CV, allowing recruiters to monitor the shortlisted, rejected, or KIV (Keep in View) candidates. - AI Agent for Decision Automation – The AI autonomously orchestrates screening decisions, replacing manual LLM configurations with dynamic AI-driven evaluations for scalability and accuracy. - Maintainable Job Description Management – A structured job description file ensures continuous updates, keeping hiring criteria flexible and aligned with recruitment needs. - Email Notifications – The system automatically sends receipt confirmations upon processing completion, providing timely updates to recruiters. Features - WorkflowAutomated Resume Screening Workflow This workflow leverages Groq Llama4 for intelligent resume analysis, speeding the screening process by generating a matching score, result (shortlisted/rejected/KIV), and key insights/rationale into their suitability for the provided job description. Step-by-Step Process: 1. Monitors Google Drive: Listens and checks for new resume CVs in Google Drive. 2. Retrieve Resume: Downloads the CV resumes from Google Drive. 3. Extract Resume Data: Extracts text content from CV resume PDF files. 4. Extract Job Description Data: Extracts text content from the job description. 5. Analyze with Groq: - Generate a matching score based on job requirements. [SCORE: 1-10] - Provide decision into their job suitability. [SHORTLISTED/REJECTED/KIV] - Provide actionable insights into their job suitability. [REASON] This ensures a fast, efficient, and accurate screening process, eliminating manual evaluation. Setup GuideStep-by-Step Instructions Ensure all credentials are ready and setup (Groq, Google Drive, Gmail, Google Sheets, Google Docs). Folder & File Setup 1. Create a Google Drive folder as needed. 2. Create a job description as required. 3. Configure a tracker (Candidate Name, AI Score, AI Verdict, AI Reason). You are ready to go!

Leverage ChatGPT to provide insightful sentiment analysis from product surveys. Increase customer satisfaction and drive effective business strategies with our innovative AI solution.

This template uses GROQ LLAVA V1.5 7B API that offers fast inference for multimodal models with vision capabilities for understanding and interpreting visual data from images. The users send an image and get a description of the image from the model. Once you set your bot, you can send the image and get the descriptions.

This workflow helps you generate an llms.txt file using a Screaming Frog export. Screaming Frog is a well-known website crawler. You can easily crawl a website and then export the "internal_html" section in CSV format. How It Works: A form allows you to enter: - The name of the website - A short description - The internal_html.csv file from your Screaming Frog export Once the form is submitted, the workflow is triggered automatically, and you can download the llms.txt file directly from n8n. Downloading the File: Since the last node in this workflow is "Convert to File," you will need to download the file directly from the n8n UI. However, you can easily add a node (e.g., Google Drive, OneDrive) to automatically upload the file wherever you want. AI-Powered Filtering (Optional): This workflow includes a text classifier node, which is deactivated by default. You can activate it to apply a more intelligent filter to select URLs for the llms.txt file. Consider modifying the description in the classifier node to specify the type of URLs you want to include. How to Use This Workflow: 1. Crawl the website you want to generate an llms.txt file for using Screaming Frog. 2. Export the "internal_html" section in CSV format. 3. In n8n, click "Test Workflow," fill in the form, and upload the internal_html.csv file. 4. Once the workflow is complete, go to the "Export to File" node and download the output. That's it! You now have your llms.txt file!

Edit 19/11/2024: As explained on the workflow, the AI Agent with the original system prompt was not effective when using gpt4-o-mini. To address this, I optimized the prompt to work better with this model. You can find the prompts I’ve tested on this Notion Page. And yes, there is one that works well with gpt4-o-mini. This AI Agent enables you to interact with your Search Console data through a chat interface. Each node is documented within the template, providing sufficient information for setup and usage. You will also need to configure Search Console OAuth credentials. Important Notes Correctly Configure Scopes for Search Console API Calls It’s essential to configure the scopes correctly in your Google Search Console API OAuth2 credentials. Incorrect configuration can cause issues with the refresh token, requiring frequent reconnections. Below is the configuration I use to avoid constant re-authentication: Of course, you'll need to add your client_id and client_secret from the Google Cloud Platform app you created to access your Search Console data. Configure Authentication for the Webhook Since the webhook will be publicly accessible, don’t forget to set up authentication. I’ve used Basic Auth, but feel free to choose the method that best meets your security requirements. 🤩💖 Example of awesome things you can do with this AI Agent

Instantly add leads to your campaign by connecting ChatGPT with a custom webhook. Automate lead generation using ChatGPT and JSON parsing.

🤖 Discord AI Workflow: Your Automated Assistant! 🚀 🌟 Workflow Overview Transforms your Discord server into an intelligent, responsive powerhouse of communication and automation! 🔧 Core Components 💬 AI-Powered Messaging 🤝 Multi-Channel Interaction 🧠 Smart Response Generation 🔗 Seamless Workflow Integration 🚦 Trigger Modes 1️⃣ Workflow Trigger 🔓 Activated by external workflows 📨 Processes incoming tasks 🌐 Supports complex automation scenarios 2️⃣ Chat Message Trigger 🗣️ Responds to direct Discord messages 🤔 Contextual understanding 🔍 Real-time interaction 🛠️ Key Features 🤖 AI-Driven Conversations 📊 Dynamic Message Handling 🔒 Secure Credential Management 🌈 Flexible Configuration 🚀 Use Cases 📢 Automated Announcements 🆘 Support Ticket Management 📝 Content Generation 🤝 Community Engagement 💡 Smart Capabilities 🧩 Modular Design 🔄 Seamless Data Flow 📝 Character Limit Management 🌐 Multi-Channel Support 🛡️ Security & Performance 🔐 OAuth Integration 🚧 Error Handling 📊 Performance Optimization 🛠️ Continuous Improvement 🎯 Workflow Magic User Input ➡️ AI Processing ➡️ Smart Response ➡️ Discord Channel 🌟 🤖 💬 📨 🔍 Customization Playground 🎨 Personalize AI Responses 🔧 Adjust Interaction Rules 📐 Fine-Tune Workflow Behavior 🚧 Troubleshooting Toolkit 🕵️ Credential Verification 🔬 Permissions Check 📋 Comprehensive Logging 🆘 Error Handling Strategies 🌈 Future Possibilities 🤖 Advanced AI Integration 🚀 Expanded Interaction Modes 🧠 Machine Learning Enhancements 🌐 Ecosystem Expansion

Dynamically switch between LLMs for AI Agents using LangChain CodePurpose This example workflow demonstrates a way to connect multiple LLMs to a single AI Agent/LangChain Node and programmatically use one – or in this case loop through them. What it does This AI workflow takes in customer complaints and generates a response that is being validated before returned. If the answer was not satisfactory, the response will be generated again with a more capable model. How it works A LangChain Code Node allows multiple LLMs to be connected to a single Basic LLM Chain. On every call, only one LLM is actually being connected to the Basic LLM Chain, which is determined by the index defined in a previous Node. The AI output is later validated by a Sentiment Analysis Node. If the result was not satisfactory, it loops back to the beginning and executes the same query with the next available LLM. The loop ends either when the result passed the requirements or when all LLMs have been used before. Setup Clone the workflow and select the belonging credentials. You'll need an OpenAI Account; alternatively, you can swap the LLM nodes with ones from a different provider like Anthropic after the import. How to use Beware that the order of the used LLMs is determined by the order they have been added to the workflow, not by the position on the canvas. After cloning this workflow into your environment, open the chat and send this example message: "I really love waiting two weeks just to get a keyboard that doesn’t even work. Great job. Any chance I could actually use the thing I paid for sometime this month?" Most likely you will see that the first validation fails, causing it to loop back to the generation node and try again with the next available LLM. Since AI responses are unpredictable, the results and number of tries will differ for each run. Disclaimer Please note, that this workflow can only run on self-hosted n8n instances, since it requires the LangChain Code Node.

What it is: Very simple connection to your Discord MCP Server and 4o. How to set it up: Just specify your MCP Server's URL, select your OpenAI credential, and you're set! How to use it: You can now send a chat message to the production URL from anywhere and the actions will occur on Discord! It really is that easy. Note: If you don't yet have a Discord MCP server set up, there is a template called "Discord MCP Server" to get you a jumpstart!

Ensure academic and professional integrity by identifying plagiarism in Google Docs using Eden AI integrated with Google Sheets. Simply paste the Google Doc ID into your sheet to receive instant plagiarism check results.

Compare Local Ollama Vision Models for Image Analysis using Google Docs Process images using locally hosted Ollama Vision Models to extract detailed descriptions, contextual insights, and structured data. Save results directly to Google Docs for efficient collaboration. Who is this for? This workflow is ideal for developers, data analysts, marketers, and AI enthusiasts who need to process and analyze images using locally hosted Ollama Vision Language Models. It’s particularly useful for tasks requiring detailed image descriptions, contextual analysis, and structured data extraction. What problem is this workflow solving? / Use Case The workflow solves the challenge of extracting meaningful insights from images in exhaustive detail, such as identifying objects, analyzing spatial relationships, extracting textual elements, and providing contextual information. This is especially helpful for applications in real estate, marketing, engineering, and research. What this workflow does This workflow: - Downloads an image file from Google Drive. - Processes the image using multiple Ollama Vision Models (e.g., Granite3.2-Vision, Gemma3, Llama3.2-Vision). - Generates detailed markdown-based descriptions of the image. - Saves the output to a Google Docs file for easy sharing and further analysis. Setup - Ensure you have access to a local instance of Ollama. - Pull the Ollama vision models. - Configure your Google Drive and Google Docs credentials in n8n. - Provide the image file ID from Google Drive in the designated node. - Update the list of Ollama vision models. - Test the workflow by clicking ‘Test Workflow’ to trigger the process. How to customize this workflow to your needs - Replace the image source with another provider if needed (e.g., AWS S3 or Dropbox). - Modify the prompts in the "General Image Prompt" node to suit specific analysis requirements. - Add additional nodes for post-processing or integrating results into other platforms like Slack or HubSpot. Key Features: - Detailed Image Analysis: Extracts comprehensive details about objects, spatial relationships, text elements, and contextual settings. - Multi-Model Support: Utilizes multiple vision models dynamically for optimal performance. - Markdown Output: Formats results in markdown for easy readability and documentation. - Google Drive Integration: Seamlessly downloads images and saves results to Google Docs.

This n8n workflow automates SEO keyword research by querying the Ahrefs API for keyword data and related keyword insights. The enriched data is then processed by an AI agent to format a response and provide valuable SEO recommendations. Perfect for SEO specialists, content marketers, digital agencies, and anyone looking to gain valuable insights into keyword opportunities to boost their rankings. How This Workflow Works This workflow guides you through the entire SEO keyword research process, from entering the initial keyword to receiving detailed insights and related keyword suggestions. 1. User Input (Keyword Query) The user enters a keyword they want to research. This input is captured by the Chat Input Node, ready for analysis. 2. AI Agent (Input Verification) The AI Agent reviews the keyword input for any grammatical errors or extra commentary. If necessary, it cleans the input to ensure a seamless query to the API. 3. Ahrefs API (Keyword Data Retrieval) The cleaned keyword is sent to the Ahrefs Keyword Tool API. This retrieves a detailed report including metrics like search volume, keyword difficulty, and CPC. 4. Related Keywords Extraction (Using JavaScript Function) The workflow uses a JavaScript function to extract main keyword data and 10 related keywords data from the Ahrefs response. You can tweak the script to adjust the number of related keywords or the level of detail you want. 5. AI Agent (Text Formatting) The aggregated data, including both the main keyword and related keywords, is sent to an AI agent. The AI agent formats the data into a concise, readable format that can be shared with the user. 6. Final Response The formatted text is delivered to the user with keyword insights, recommendations, and related keyword suggestions. Smart Retry & Error Handling Each subworkflow includes a fail-safe mechanism to ensure: - Proper error handling for any issues with the API request. - Failed API requests are retried after a customizable period (e.g., 2 hours or 1 day). - User input validation prevents any incorrect or malformed queries from being processed. Ahrefs API Setup To use this workflow, you’ll need to set up your Ahrefs API credentials: - Ahrefs API Sign up for an Ahrefs account and get your key. Once signed up, you'll receive an API key, which you’ll use in the x-rapidapi-key header in n8n. Ensure you check the Ahrefs Keyword Tool API documentation for more details on available parameters. How to Import This Workflow Copy the JSON code. Open your n8n instance. Open a new workflow. Paste anywhere inside the workflow. Voila. Customization Options - Adjust the number of related keywords extracted (default is 10). - Customize the AI agent response formatting or add specific recommendations for users. - Modify the JavaScript function to extract different metrics from the Ahrefs API. Use Case Example Trying to optimize your blog post around a specific keyword? Query a broad keyword, like “SEO tips”. Get related keyword data and search volume insights. Use the AI agent to provide keyword recommendations and additional topics to target. Boost your content strategy with fresh keywords and relevant search data!

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: - Survey Insights - Customer Insights - Community Insights This template demonstrates the Community Insights scenario where HN comments can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, researchers or HN users can quickly break down community consensus on a particular topic and identify frequently mentioned positives and negatives. How it works HN comments are imported via the Hacknews API node. Comments are then inserted into a Qdrant collection carefully tagged with the Hackernews API metadata. Comments are fetched and put through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups. Each group is looped to fetch the payloads of the points and feed them to the AI agent to summarize and generate insights. The resulting insights and raw responses are then saved to the Google Spreadsheet for further analysis by the researcher or the HN user. Requirements - Works best with lots of comments! - Qdrant Vectorstore for storing embeddings. - OpenAI account for embeddings and LLM. Customizing the Template - Adjust clustering parameters which make sense for your data. - Adjust sentimentality setting if comments are overwhelmingly negative at times.

This workflow allows users to convert a 2D image into a 3D model by integrating multiple AI and web services. The process begins with a user uploading or providing an image URL, which is then sent to a generative AI model capable of interpreting the content and generating a 3D representation in .glb format. The model is then stored and a download link is returned to the user. Main Steps - Trigger Node: Initiates the workflow either via HTTP request, webhook, or manual execution. - Image Upload or Input: The image is acquired via direct upload or URL input. - API Integration: The image is sent to a 3D generation API (e.g., a service like Kaedim, Luma Labs, or a custom AI model). - Model Generation: The external API processes the image and creates a 3D model. - File Storage: The resulting 3D model is stored in cloud storage (e.g., S3, Google Drive, or a local server). - Response to User: A download link for the 3D model is returned to the user via the same communication channel (HTTP response, email, or chat). Advantages - Automation: Eliminates the need for manual 3D modeling, saving time for artists, developers, and designers. - AI-Powered: Leverages AI to generate realistic and usable 3D models from simple 2D inputs. - Scalability: Can be triggered automatically and scaled up to handle many requests via n8n's automation. - Integration-Friendly: Easily extendable with other services like Discord, Telegram, or marketplaces for 3D assets. - No-Code Configuration: Built with n8n’s visual interface, making it editable without programming knowledge. How It Works - Trigger: The workflow can be started manually (when clicking ‘Test workflow’) or automatically at scheduled intervals (Schedule Trigger). - Data Retrieval: The "Get new image" node fetches data from a Google Sheet, including the model image, product image, and product ID. - 3D Image Creation: The "Create 3D Image" node sends the image data to the Fal.run API (Trellis) to generate a 3D model. - Status Check: The workflow periodically checks the request status (Get status and Wait 60 sec.) until the job is marked as "COMPLETED." - Result Processing: Once completed, the 3D model URL is retrieved (Get Url 3D image), the file is downloaded (Get File 3D image), and uploaded to Google Drive (Upload 3D Image). - Sheet Update: The final 3D model URL is written back to the Google Sheet (Update result). Set Up Steps 1. Prepare Google Sheet: Create a Google Sheet with columns: IMAGE MODEL and 3D RESULT (empty). Example sheet: Google Sheet Template. 2. Obtain Fal.run API Key: Sign up at Fal.ai and get an API key. Configure the Authorization header in the "Create 3D Image" node with Key YOURAPIKEY. 3. Configure Workflow Execution: Run manually via the Test workflow button. For automation, set up the Schedule Trigger node (e.g., every 5 minutes). 4. Verify Credentials: Ensure Google Sheets, Google Drive, and Fal.run API credentials are correctly set in n8n. Once configured, the workflow processes new entries in the Google Sheet, generates 3D models, and updates the results automatically. Need help customizing? Contact me for consulting and support or add me on LinkedIn.

Gmail MCP Server Expose Gmail’s full API as a single SSE “tool server” endpoint for your AI agents. What it does Spins up an MCP Trigger that streams Server‑Sent Events to LangChain/N8N AI Agent nodes. Maps 20+ common Gmail operations (search, send, reply, draft, label & thread management, mark read/unread, delete, etc.) to ai_tool connections, so agents can invoke them with a simple JSON payload. Why you’ll love itAgent‑ready: Plug the SSE URL into any N8N Agent or any other AI tool that uses MCP and start reasoning over email immediately. Extensible: Add more GmailTool operations or swap credentials without touching your agent logic. How to use Import the workflow (n8n ≥ v1.88). Set up a gmailOAuth2 credential and select it on the GmailTool nodes. Open the Gmail MCP Server node, copy the SSE URL, and paste it into your AI agent’s “Tool Server” field.

Smart Email Auto-Responder with AI Classification Automatically categorize and reply to emails using LangChain + Google Gemini + Gmail + SMTP + Brevo. This n8n workflow is designed to intelligently manage incoming emails and automatically send personalized responses based on the content. It classifies emails using LangChain's Text Classifier, sends HTML responses depending on the category, and updates Gmail and Brevo CRM accordingly. Key Features - Triggers and Classifies Emails Listens for new Gmail messages every hour. Uses AI-based classification to identify the type of inquiry, for example: - Guest Post - YouTube Review - Udemy Course Inquiry - Responds Automatically Sends professional HTML replies customized for each type. Uses SMTP to deliver emails from your domain. - Enhances Workflow with Automation Marks processed emails as read. Applies Gmail labels. Adds sender to Brevo contact list. - Optional AI Chat Integration Uses Google Gemini (PaLM 2) to enhance classification or summarization. Tools & Integrations Required - Gmail account (OAuth2) - LangChain (Text Classifier node) - Google Gemini API account - SMTP credentials (e.g., Gmail SMTP, Brevo, etc.) - Brevo/Sendinblue account and API keyStep-by-Step Node Guide 1. Gmail Trigger Polls Gmail every hour for new emails. Filters out internal addresses (e.g., @syncbricks.com). Avoids replying to already-responded emails (Re: subject filter). 2. LangChain Text Classifier Uses AI to categorize the content of the email based on pre-defined categories: - Guest Post - Youtube - Udemy Courses 3. Google Gemini (PaLM) Chat Model (Optional) Provides additional AI support to enhance classification accuracy. Can be used to summarize or enrich the context if needed. 4. Email Send Nodes Each response category has a separate SMTP node with a custom HTML email: - Guest Post Inquiry - YouTube Video Inquiry - Udemy Course Inquiry 5. Gmail: Mark as Read Marks the email so it isn’t processed again. 6. Gmail: Apply Label Adds a label (e.g., Handled by Bot) for organization. 7. Brevo: Create/Update Contact Saves the sender to your CRM for future communication or marketing. Email Templates Included - Guest Post Template Includes pricing, website list, submission guidelines, and payment instructions. - YouTube Review Template Includes package pricing, review samples, video thumbnails, and inquiry instructions. How to Use Import the template into your n8n instance. Configure your Gmail OAuth2 and SMTP credentials. Set up your LangChain Text Classifier and Google Gemini API credentials. Update label ID in the Gmail node and ensure all custom fields like from.value[0].name match your use case. Run the workflow and watch it respond intelligently to new inquiries. Best Practices - Always test with mock emails first. - Keep the Google Gemini node optional if you want to reduce cost/API calls. - Use Gmail filters to auto-label certain types of emails. - Monitor your Brevo contacts to track new leads. Attribution & Support Developed by Amjid Ali. This template took extensive time and effort to build. If you find it useful, please consider supporting my work. Buy My Book: Mastering n8n on Amazon. Full Courses & Tutorials: http://lms.syncbricks.com. Follow Me Online: - LinkedIn: https://linkedin.com/in/amjidali - Website: https://amjidali.com - YouTube: https://youtube.com/@syncbricks

Use case LLMs have provided a lot of value for several use cases. Especially some OpenAI models are proving to be quite valuable. However, it's sometimes not super accessible to chat with these models. This workflow enables you to chat directly with OpenAI's GPT-3.5 via Telegram. How it works A simple Telegram bot that connects to your BotFather bot to give AI responses, using OpenAI's GPT-3.5 model, to a user's messages with emojis. What to do Add your Telegram API key and your OpenAI API key and have fun!

Use this template to generate engaging images and chat completions using Perplexity AI and ChatGPT, triggered by new data in Google Sheets. This process will then update the sheet, enhancing your content creation workflow.

Automate lead management by extracting email data from various lead sources in a uniform way with ChatGPT and integrating them into Keap.

This workflow detects toxic language (such as profanity, insults, threats) in messages sent via Telegram. This blog tutorial explains how to configure the workflow nodes step-by-step. Telegram Trigger: triggers the workflow when a new message is sent in a Telegram chat. Google Perspective: analyzes the text of the message and returns a probability value between 0 and 1 of how likely it is that the content is toxic. IF: filters messages with a toxic probability value above 0.7. Telegram: sends a message in the chat with the text "I don't tolerate toxic language" if the probability value is above 0.7. NoOp: takes no action if the probability value is below 0.7.

Candidate Engagement | Resume Screening | AI Voice Interviews | Applicant Insights This intelligent n8n workflow automates the process of extracting and scoring resumes received through a company career page, populating a Notion database with AI insights where the recruiter or hiring manager can automatically invite the applicant to an instant interview with an Elevenlabs AI voice agent. After the agent conducts the behavior-based interview, the workflow scores the overall interview against customizable evaluation criteria and updates the Notion database with AI insights about the applicant. AI Powered Resume Screening & Voice AI that interviews like a Recruiter!AI Insights in Notion dashboardWho is this for? HR teams, recruiters, and talent acquisition professionals. This workflow is ideal for HR teams, recruiters, and talent acquisition professionals looking for a foundational, extensible framework to automate early stage recruiting. Whether you're exploring AI for the first time or scaling automation across your hiring process, this template provides a base for screening, interviewing, and tracking candidates—powered entirely by n8n, Elevenlabs, Notion, and LLM integrations. Be sure to consult State and Country regulations with respect to AI Compliance, AI Bias Audits, AI Risk Assessment, and disclosure requirements. What problem is this workflow solving? Manually screening resumes and conducting initial interviews slows down hiring. This template automates: - Resume assessment against job description. - Scheduling first and second round interviews. - First-round AI-led behavioral interviews with AI scoring assessment. - Centralized tracking of AI assessments in Notion. What this does This customizable tool, configured to manage 3 requisitions in parallel, automates the application process, resume screen, and first round behavioral interviews. Pre-screen Applicants with AI Immediately screens and scores applicant’s resume against the job description. The AI Agent generates a score and an AI assessment, adding both to the applicant's profile in Notion. Notion automatically notifies hiring manager when a resume receives a score of 8 or higher. Voice AI that Interviews like a Recruiter AI Voice agent adapts probing questions based on applicant’s response and intelligently dives deeper into skill and experience to assess answers against a scoring rubric for each question. AI Applicant Insights in Notion Get detailed post-interview AI analysis, including interview recordings and question-by-question scoring breakdowns to help identify who you should advance to the next stage in the process. AI insight provided in Notion ATS dashboard with drag and drop to advance top candidates to the next interview stage. How it worksLink to Notion Template Notion Career Page: Notion Career Page published to web, can be integrated with your preferred job board posting system. Notion Job Posting: Gateway for applicants to apply to active requisitions with ‘Click to Apply’ button. Application Form: N8N webform embedded into Notion job posting captures applicant information and routes for AI processing. AI Agent evaluates resume against job description AI Agent evaluates resume against the job description, stored in Notion, and scores the applicant on a scale of 1 to 10, providing rationale for score. Creates ATS record in Notion with assessment and score Workflow creates the applicant record in the Notion ATS where Recruiters and Hiring Managers see applicants in a filtered view, sorted by AI generated resume score. Users can automatically advance applicants to the next step in process (AI Conversation interview) with drag and drop functionality. Invites applicant to an Instant AI Interview Dragging the applicant to AI Interview step in the Notion ATS dashboard triggers Notion automation that sends the applicant an email with a link to the Elevenlabs Conversation AI Agent. The AI Conversation Agent is provided with instructions on how to conduct the behavior-based interview, including probing questions, for the specific role. AI Conversation Agent Behavior Based Interview The email link resolves to an ElevenLabs AI Conversation agent that has been instructed to interview applicants using pre-defined interview questions, scoring rubric, job description, and company profile. The Elevenlabs agent assesses the applicant on a scale of 1 to 5 for each interview question and provides an overall assessment of the interview based on established evaluation criteria. Updates Notion record with Interview Assessment and Score All results—including the conversation transcript, interview scores, and rationale for assessment are automatically added back to the applicant’s profile in Notion where the Hiring Manager can validate the AI assessment by skimming through the embedded audio file. AI Interview Overall Score: 1 to 5 based on response to all questions and probes. AI Agent confirms that it was able to evaluate the interview using the assigned rubric. AI Interview Criteria Score: Success/Failure based on response to individual interview questions. Invites applicant to second interview with Hiring Manager Dragging the applicant to the ‘Hiring Manager Interview’ step in the Notion ATS dashboard triggers a Notion automation that sends an email with a link to the Hiring Manager’s calendar scheduling solution. Configuration and Set UpAccounts & API Keys You will need accounts and credentials for: - n8n (hosted or self-hosted) - Elevenlabs (for AI Conversation Agent) - Gemini (for LLM model access) - Google Drive (to back up applicant data) - Calendly (to automate interview scheduling) - Gmail (to automate interview scheduling) Data / Documents to implement - Job Descriptions for each role - Interview questions for each role - Evaluation criteria for each interview question Notion Set UpCustomize your Notion Career PageLink to Free Notion Template that enables workflow: Update Notion job description database: - Update job description(s) for each role - Add interview questions to the job description database page in Notion - Add evaluation criteria to the job description database page in Notion - Edit each ‘Click to Apply’ button in the job description template so it resolves to the corresponding N8N 'Application Form' webform production URL (detail provided below) Notion Applicant Tracker In the Applicant Tracker database, update position titles, tab headings, in the custom database view (Notion) so it reflects the title of the position you are posting. Edit the filter for each tab so it matches the position title. Notion Email Automation Update Notion automation templates used to invite applicants to the AI Interview and Hiring Manager interview. Note: Trigger email automation by dragging applicant profile to the next Applicant Comm Status in the Applicant Tracker. AI Interview invite template: revise position title to reflect the title of the role you are posting; include the link to your Conversation AI Agent for that role in the email body. Note: each unique role will use an Elevenlabs AI conversation agent designed for that role. Hiring Manager Interview invite template: revise position title to reflect the title of the role you are posting; include the link to your Calendly page or similar solution provider to automate interview scheduling. N8N ConfigurationWorkflow 1 Application Forms (3 Nodes - one for each job) Update the N8N form title and description to match the job description you configured in Notion. Confirm Job Code in Applicant Form node matches Job Code in Notion for that position. Edit the Form Response to customize the message you want displayed after applicant clicks submit. Upload CV - Google Drive Authenticate your Google Drive account and select the folder that will be used to store resumes. Get Job Description - Notion Authenticate your Notion account and select your Career Page from the list of databases that contain your job descriptions. Applicant Data Backup - Google Sheet Create a Google Sheet where you will track applicant data for AI Compliance reporting requirements. Open the node in n8n and use the field names in the node as Google Sheet column headings. Workflow 2Elevenlabs Web Hook (Node 1) Edit the Web Hook POST node and copy your production URL that is displayed in the Node. This URL is entered into the Elevenlabs AI Conversation Agent post-call webhook described below. AI Agent Authenticate your LLM model (Gemini in this example) and add your Notion database as a tool to pull the evaluation_criteria hosted in Notion for the specific role. Extract Audio Create an Elevenlabs API key for your conversation agent and enter that key as a json header for the Extract Audio node. Upload Audio to Drive - Google Drive Authenticate your Google Drive account and select the folder that will be used to store the audio file. Elevenlabs Configuration Create an Elevenlabs account. Create Conversation AI Agent. Add First Message and Systems Prompt: Design your ‘First Message’ and ‘Systems Prompt’ that guides the AI agent conducting the interview. Tool Tip: provide instruction that limits the number of probes per interview question. Knowledge Base: Upload your role specific interview questions and job description, using the same text that is stored in your Notion Career page for the role. You can also add a document about your company and instruct the Elevenlabs agent to answer questions about culture, strategy, and company growth. Analysis: Evaluation Criteria: Add your evaluation criteria, less than 2000 characters, for each interview question / competency. Analysis: Data Collection: Add the following elements, using the exact character string represented below. - phone_number_AI_screen: "capture applicant's phone number provided at the start of the conversation and share this as a string, integers only." - full_name: "capture applicant's full name provided at the start of the conversation." Advanced: Max Duration Set the max duration for interview in seconds. The AI Agent will timeout at the max duration. Conversation AI Widget: Customize your AI Conversation Agent landing page, including the position title and company name. AI Conversation Agent URL: Copy the AI Conversation Agent URL and add it to your Notion email template triggered by the AI Interview email automation. Use a custom AI Agent URL for each distinct job description. Enable your Elevenlabs Post-Call Webhook for your Conversation Agent: Log into your Elevenlabs account and go to Conversational AI Settings and click on Post-Call Web Hook. This is where you enter the production URL from the N8N Web Hook node (Workflow 2). This sends the AI Voice Agent output to your n8n workflow which feeds back to your Notion dashboard.