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By Harshil AgrawalConnect AI to any chats in Kommo
Entrust customer service to AI using n8n and Kommo! Using this workflow, you can make the AI agent answer customer questions for your managers. See how it works in the video. Advantages of integration Works with any message channel that is connected to Kommo (telegram, whatsapp, facebook) Understands voice and text messages You can stop for a specific transaction or contact if you need a person's help. It is possible to supplement the AI agent with additional tools to suit your needs Where it can be useful In customer support In the qualification of clients When invoicing How it works 1) Any incoming message to the Kommo chats is sent by the webhook to n8n 2) n8n processes the webhook according to the specified logic 3) n8n sends a reply message to the Kommo chat Installation Steps 1) Install workflow 2) Follow the instructions to connect the kommo to the n8n 3) Set up Credentials for OpenAI 4) Fill in the Credentials as shown in the workflow notes 5) Activate Workflow 6) Write your first message as client
By yatolstoyGenerate AI images in bulk with Freepik, Google Sheets & Drive
This n8n workflow automates bulk AI image generation using Freepik's Text-to-Image API. It reads prompts from a Google Sheet, generates multiple variations of each image using Freepik's AI, and automatically uploads the results to Google Drive with organized file names. This is perfect for content creators, marketers, or designers who need to generate multiple AI images in bulk and store them systematically. Key Features: Bulk image generation from Google Sheets prompts Multiple variations per prompt (configurable duplicates) Automatic file naming and organization Direct upload to Google Drive Batch processing for efficient API usage Freepik AI-powered image generation Step-by-Step Implementation Guide Prerequisites Before setting up this workflow, you'll need: n8n instance (cloud or self-hosted) Freepik API account with Text-to-Image access Google account with access to Sheets and Drive Google Sheet with your prompts Step 1: Set Up Freepik API Credentials Go to Freepik API Developer Portal Create an account or sign in Navigate to your API dashboard Generate an API key for Text-to-Image service Copy the API key and save it securely In n8n, go to Credentials → Add Credential → HTTP Header Auth Configure as follows: Name: "Header Auth account" Header Name: x-freepik-api-key Header Value: Your Freepik API key Step 2: Set Up Google Credentials Google Sheets Access: Go to Google Cloud Console Create a new project or select existing one Enable Google Sheets API Create OAuth2 credentials In n8n, go to Credentials → Add Credential → Google Sheets OAuth2 API Enter your OAuth2 credentials and authorize with spreadsheets.readonly scope Google Drive Access: In Google Cloud Console, enable Google Drive API In n8n, go to Credentials → Add Credential → Google Drive OAuth2 API Enter your OAuth2 credentials and authorize Step 3: Create Your Google Sheet Create a new Google Sheet: sheets.google.com Set up your sheet with these columns: Column A: Prompt (your image generation prompts) Column B: Name (identifier for file naming) Example data: | Prompt | Name | |-------------------------------------------|-------------| | A serene mountain landscape at sunrise | mountain-01 | | Modern office space with natural lighting | office-02 | | Cozy coffee shop interior | cafe-03 | Copy the Sheet ID from the URL (the long string between /d/ and /edit) Step 4: Set Up Google Drive Folder Create a folder in Google Drive for your generated images Copy the Folder ID from the URL when viewing the folder Note: The workflow is configured to use a folder called "n8n workflows" Step 5: Import and Configure the Workflow Copy the provided workflow JSON In n8n, click Import from File or Import from Clipboard Paste the workflow JSON Configure each node as detailed below: Node Configuration Details: Start Workflow (Manual Trigger) No configuration needed Used to manually start the workflow Get Prompt from Google Sheet (Google Sheets) Document ID: Your Google Sheet ID (from Step 3) Sheet Name: Sheet1 (or your sheet name) Operation: Read Credentials: Select your "Google Sheets account" Double Output (Code Node) Purpose: Creates multiple variations of each prompt JavaScript Code: javascript const original = items[0].json; return [ { json: { ...original, run: 1 } }, { json: { ...original, run: 2 } }, ]; Customization: Add more runs for additional variations Loop (Split in Batches) Processes items in batches to manage API rate limits Options: Keep default settings Reset: false Create Image (HTTP Request) Method: POST URL: https://api.freepik.com/v1/ai/text-to-image Authentication: Generic → HTTP Header Auth Credentials: Select your "Header Auth account" Send Body: true Body Parameters: Name: prompt Value: ={{ $json.Prompt }} Split Responses (Split Out) Field to Split Out: data Purpose: Separates multiple images from API response Convert to File (Convert to File) Operation: toBinary Source Property: base64 Purpose: Converts base64 image data to file format Upload Image to Google Drive (Google Drive) Operation: Upload Name: =Image - {{ $('Get Prompt from Google Sheet').item.json.Name }} - {{ $('Double Output').item.json.run }} Drive ID: My Drive Folder ID: Your Google Drive folder ID (from Step 4) Credentials: Select your "Google Drive account" Step 6: Customize for Your Use Case Modify Duplicate Count: Edit the "Double Output" code to create more variations Update File Naming: Change the naming pattern in the Google Drive upload node Adjust Batch Size: Modify the Loop node settings for your API limits Add Image Parameters: Enhance the HTTP request with additional Freepik parameters (size, style, etc.) Step 7: Test the Workflow Ensure your Google Sheet has test data Click Execute Workflow on the manual trigger Monitor the execution flow Check that images are generated and uploaded to Google Drive Verify file names match your expected pattern Step 8: Production Deployment Set up error handling for API failures Configure appropriate batch sizes based on your Freepik API limits Add logging for successful uploads Consider webhook triggers for automated execution Set up monitoring for failed executions Freepik API Parameters Basic Parameters: prompt: Your text description (required) negative_prompt: What to avoid in the image guidance_scale: How closely to follow the prompt (1-20) numinferencesteps: Quality vs speed trade-off (20-100) seed: For reproducible results Example Enhanced Body: json { "prompt": "{{ $json.Prompt }}", "negative_prompt": "blurry, low quality", "guidance_scale": 7.5, "numinferencesteps": 50, "num_images": 1 } Workflow Flow Summary Start → Manual trigger initiates the workflow Read Sheet → Gets prompts and names from Google Sheets Duplicate → Creates multiple runs for variations Loop → Processes items in batches Generate → Freepik API creates images from prompts Split → Separates multiple images from response Convert → Transforms base64 to binary file format Upload → Saves images to Google Drive with organized names Complete → Returns to loop for next batch Contact Information Robert A Ynteractive For support, customization, or questions about this workflow: 📧 Email: rbreen@ynteractive.com 🌐 Website: https://ynteractive.com/ 💼 LinkedIn: https://www.linkedin.com/in/robert-breen-29429625/ Need help implementing this workflow or want custom automation solutions? Get in touch for professional n8n consulting and workflow development services.
By Robert BreenPeriodically send data from HTTP Request node to Telegram
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By Harshil AgrawalDocument Q&A with RAG: Query PDF content using Weaviate and OpenAI
RAG over a PDF with Weaviate This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes. Who it's for This workflow is the simplest possible implementation of RAG with Weaviate in n8n. It's intended to act as an extendable template for RAG over your own documents. Prerequisites An existing Weaviate cluster. You can view instructions for setting up a local cluster with Docker here or a Weaviate Cloud cluster here. API keys to generate embeddings and power chat models. We use OpenAI, but feel free to switch out the models as you like. Self-hosted n8n instance. See this video for how to get set up in just three minutes. How it works Part 1: Manually upload data In this example, we manually upload a 100+ page article from arXiv called "A Survey of Large Language Models". But you can replace this with your own more advanced data pipeline, if you wish. Part 2: Embed and load data into Weaviate collection Here, we generate embeddings for the full-text of the article and store them in Weaviate. Part 3: Perform RAG over PDF file with Weaviate In this part of the workflow, you can enter your query by running the Chat Node and get a RAG response grounded in context via the Question and Answer Chain node. How to run the workflow Go through the prerequisites, creating a Weaviate cluster (can be local or cloud), downloading self-hosted n8n, and adding your API keys and other credentials. Select the embedding and chat models you'd like to use. Upload a PDF file you want to ask questions about. Execute the rest of the workflow.
By Mary NewhauserShopify VIP alerts: AI summary & Slack notification for big orders
🧨 VIP Radar: Instantly Spot & Summarize High-Value Shopify Orders with AI + Slack Alerts Automatically detect when a new Shopify order exceeds $200, fetch the customer’s purchase history, generate an AI-powered summary, and alert your team in Slack—so no VIP goes unnoticed. --- 🛠️ Workflow Overview | Feature | Description | |------------------------|-----------------------------------------------------------------------------| | Trigger | Shopify “New Order” webhook | | Conditional Check | Filters for orders > $200 | | Data Enrichment | Pulls full order history for the customer from Shopify | | AI Summary | Uses OpenAI to summarize buying behavior | | Notification | Sends detailed alert to Slack with name, order total, and customer insights | | Fallback | Ignores low-value orders and terminates flow | --- 📘 What This Workflow Does This automation monitors your Shopify store and reacts to any high-value order (over $200). When triggered: It fetches all past orders of that customer, Summarizes the history using OpenAI, Sends a full alert with context to your Slack channel. No more guessing who’s worth a closer look. Your team gets instant insights, and your VIPs get the attention they deserve. --- 🧩 Node-by-Node Breakdown 🔔 1. Trigger: New Shopify Order Type: Shopify Trigger Event: orders/create Purpose: Starts workflow on new order Pulls: Order total, customer ID, name, etc. 🔣 2. Set: Convert Order Total to Number Ensures the total_price is treated as a number for comparison. ❓ 3. If: Is Order > $200? Condition: $json.total_price > 200 Yes → Continue No → End workflow 🔗 4. HTTP: Fetch Customer Order History Uses the Shopify Admin API to retrieve all orders from this customer. Requires your Shopify access token. 🧾 5. Set: Convert Orders Array to String Formats the order data so it's prompt-friendly for OpenAI. 🧠 6. LangChain Agent: Summarize Order History Prompt: "Summarize the customer's order history for Slack. Here is their order data: {{ $json.orders }}" Model: GPT-4o Mini (customizable) 📨 7. Slack: Send VIP Alert Sends a rich message to a Slack channel. Includes: Customer name Order value Summary of past behavior 🧱 8. No-Op (Optional) Used to safely end workflow if the order is not high-value. --- 🔧 How to Customize | What | How | |--------------------------|----------------------------------------------------------------------| | Order threshold | Change 200 in the If node | | Slack channel | Update channelId in the Slack node | | AI prompt style | Edit text in LangChain Agent node | | Shopify auth token | Replace shpat_abc123xyz... with your actual private token | --- 🚀 Setup Instructions Open n8n editor. Go to Workflows → Import → Paste JSON. Paste this workflow JSON. Replace your Shopify token and Slack credentials. Save and activate. Place a test order in Shopify to watch it work. --- 💡 Real-World Use Cases 🎯 Notify sales team when a potential VIP buys 🛎️ Prep support reps with customer history 📈 Detect repeat buyers and upsell opportunities --- 🔗 Resources & Support 👨💻 Creator: Yaron Been 📺 YouTube: NoFluff with Yaron Been 🌐 Website: https://nofluff.online 📩 Contact: Yaron@nofluff.online --- 🏷️ Tags shopify, openai, slack, vip-customers, automation, n8n, workflow, ecommerce, customer-insights, ai-summaries, gpt4o ---
By Yaron BeenBrand visibility & sentiment analysis across AI search tools (OpenAI, Perplexity, ChatGPT)
This n8n template demonstrates how to audit your brand’s visibility across multiple AI systems and automatically log the results to Google Sheets. It sends the same prompt to OpenAI, Perplexity, and (optionally) a ChatGPT web actor, then runs sentiment and brand-hierarchy analysis on the responses. Use cases are many: benchmark how often (and how positively) your brand appears in AI answers, compare responses across models, and build a repeatable “AI visibility” report for marketing and comms teams. 💡 Good to know You’ll bring your own API keys for OpenAI and Perplexity. Usage costs depend on your providers’ pricing. The optional APIfy actor automates the ChatGPT web UI and may violate terms of service. Use strictly at your own risk. ⁉ How it works A Manual Trigger starts the workflow (you can replace it with any trigger). Input prompts are read from a Google Sheet (or you can use the included “manual input” node). The prompt is sent to three tools: -- OpenAI (via API) to check baseline LLM knowledge. -- Perplexity (API) to retrieve an answer with citations. -- Optionally, an APIfy actor that scrapes a ChatGPT response (web interface). Responses are normalized and mapped (including citations where available). An LLM-powered sentiment pass classifies each response into: -- Basic Polarity: Positive, Neutral, or Negative -- Emotion Category: Joy, Sadness, Anger, Fear, Disgust, or Surprise -- Brand Hierarchy: ordered list such as Nike>Adidas>Puma The consolidated record (Prompt, LLM, Response, Brand mentioned flag, Brand Hierarchy, Basic Polarity, Emotion Category, Source 1–3/4) is appended to your “Output many models” Google Sheet. A simplified branch shows how to take a single response and push it to a separate sheet. 🗺️ How to use Connect your Google Sheets OAuth and create two tabs: -- Input: a single “Prompt” column -- Output: columns for Prompt, LLM, Response, Brand mentioned, Brand Hierarchy, Basic Polarity, Emotion Category, Source 1, Source 2, Source 3, Source 4 Add your OpenAI and Perplexity credentials. (Optional) Add an APIfy credential (Query Auth with token) if you want the ChatGPT web actor path. Run the Manual Trigger to process prompts in batches and write results to Sheets. Adjust the included “Limit for testing” node or remove it to process more rows. ⚒️ Requirements OpenAI API access (e.g., GPT-4.1-mini / GPT-5 as configured in the template) Perplexity API access (model: sonar) Google Sheets account with OAuth connected in n8n (Optional) APIfy account/token for the ChatGPT web actor 🎨 Customising this workflow Swap the Manual Trigger for a webhook or schedule to run audits automatically. Extend the sentiment analyzer instructions to include brand-specific rules or compliance checks. Track more sources (e.g., additional models or vertical search tools) by duplicating the request→map→append pattern. Add scoring (e.g., “visibility score” per prompt) and charts by pointing the output sheet into Looker Studio or a BI tool.
By AOE Agent LabGoogle Play review intelligence with Bright Data & Telegram alerts
Google Play Review Intelligence with Bright Data & Telegram Alerts Overview This n8n workflow automates the process of scraping Google Play Store reviews, analyzing app performance, and sending alerts for low-rated applications. It integrates with Bright Data for web scraping, Google Sheets for data storage, and Telegram for notifications. Workflow Components ✅ Trigger Input Form Type: Form Trigger Purpose: Initiates the workflow with user input Input Fields: URL (Google Play Store app URL) Number of reviews to fetch Function: Captures user requirements to start the scraping process 🚀 Start Scraping Request Type: HTTP Request (POST) Purpose: Sends scraping request to Bright Data API Endpoint: https://api.brightdata.com/datasets/v3/trigger Parameters: Dataset ID: gd_m6zagkt024uwvvwuyu Include errors: true Limit multiple results: 5 Custom Output Fields: url, reviewid, reviewername, review_date reviewrating, review, appurl, app_title appdeveloper, appimages, app_rating appnumberofreviews, appwhat_new appcontentrating, appcountry, numof_reviews 🔄 Check Scrape Status Type: HTTP Request (GET) Purpose: Monitors the progress of the scraping job Endpoint: https://api.brightdata.com/datasets/v3/progress/{snapshot_id} Function: Checks if the dataset scraping is complete ⏱️ Wait for Response 45 sec Type: Wait Node Purpose: Implements polling mechanism Duration: 45 seconds Function: Pauses workflow before checking status again 🧩 Verify Completion Type: IF Condition Purpose: Evaluates scraping completion status Condition: status === "ready" Logic: True: Proceeds to fetch data False: Loops back to status check 📥 Fetch Scraped Data Type: HTTP Request (GET) Purpose: Retrieves the final scraped data Endpoint: https://api.brightdata.com/datasets/v3/snapshot/{snapshot_id} Format: JSON Function: Downloads completed review and app data 📊 Save to Google Sheet Type: Google Sheets Node Purpose: Stores scraped data for analysis Operation: Append rows Target: Specified Google Sheet document Data Mapping: URL, Review ID, Reviewer Name, Review Date Review Rating, Review Text, App Rating App Number of Reviews, App What's New, App Country ⚠️ Check Low Ratings Type: IF Condition Purpose: Identifies poor-performing apps Condition: review_rating < 4 Logic: True: Triggers alert notification False: No action taken 📣 Send Alert to Telegram Type: Telegram Node Purpose: Sends performance alerts Message Format: ⚠️ Low App Performance Alert 📱 App: {app_title} 🧑💻 Developer: {app_developer} ⭐ Rating: {app_rating} 📝 Reviews: {appnumberof_reviews} 🔗 View on Play Store Workflow Flow Input Form → Start Scraping → Check Status → Wait 45s → Verify Completion ↑ ↓ └──── Loop ────┘ ↓ Fetch Data → Save to Sheet & Check Ratings ↓ Send Telegram Alert Configuration Requirements API Keys & Credentials Bright Data API Key: Required for web scraping Google Sheets OAuth2: For data storage access Telegram Bot Token: For alert notifications Setup Parameters Google Sheet ID: Target spreadsheet identifier Telegram Chat ID: Destination for alerts N8N Instance ID: Workflow instance identifier Key Features Data Collection Comprehensive app metadata extraction Review content and rating analysis Developer and country information App store performance metrics Quality Monitoring Automated low-rating detection Real-time performance alerts Continuous data archiving Integration Capabilities Bright Data web scraping service Google Sheets data persistence Telegram instant notifications Polling-based status monitoring Use Cases App Performance Monitoring Track rating trends over time Identify user sentiment patterns Monitor competitor performance Quality Assurance Early warning for rating drops Customer feedback analysis Market reputation management Business Intelligence Review sentiment analysis Performance benchmarking Strategic decision support Technical Notes Polling Interval: 45-second status checks Rating Threshold: Alerts triggered for ratings < 4 Data Format: JSON with structured field mapping Error Handling: Includes error tracking in dataset requests Result Limiting: Maximum 5 multiple results per request For any questions or support, please contact: info@incrementors.com or fill out this form https://www.incrementors.com/contact-us/
By IncrementorsMonitor lead response time SLA breaches with Google Sheets & Telegram alerts
Description Never miss a lead again with this SLA Breach Alert automation powered by n8n! This workflow continuously monitors your Google Sheets for un-replied leads and automatically triggers instant Telegram alerts, ensuring your team takes immediate action. By running frequent SLA checks, enriching alerts with direct Google Sheet links, and sending real-time notifications, this automation helps prevent unattended leads, reduce response delays, and boost customer engagement. What This Template Does 📅 Runs every 5 minutes to monitor SLA breaches 📋 Fetches lead data (status, contact, timestamps) from Google Sheets 🕒 Identifies leads marked “Un-replied” beyond the 15-minute SLA 🔗 Enriches alerts with direct Google Sheet row links for quick action 📲 Sends Telegram alerts with lead details for immediate response Step-by-Step Setup Prepare Your Google Sheet Create a sheet with the following columns (minimum required): Lead Name Email Phone Status (values: Replied, Un-replied) Timestamp (time of last update/reply) Set Up Google Sheets in n8n Connect your Google account in n8n. Point the workflow to your sheet (remove any hardcoded document IDs before sharing). Configure SLA Check Use the IF node to filter leads where: Status = Un-replied Time since timestamp > 15 minutes Enrich Alerts with Links Add a Code node to generate direct row links to the sheet. Set Up Telegram Bot Create a Telegram bot via @BotFather. Add the bot to your team chat. Store the botToken securely (remove chatId before sharing templates). Send Alerts Configure the Telegram node in n8n to send lead details + direct Google Sheet link. Customization Guidance Adjust the SLA window (e.g., 30 minutes or 1 hour) by modifying the IF node condition. Add more fields from Google Sheets (e.g., Company, Owner) to enrich the alert. Replace Telegram with Slack or Email if your team prefers a different channel. Extend the workflow to auto-assign leads in your CRM once alerted. Perfect For Sales teams that need to respond to leads within strict SLAs Support teams ensuring no customer request is ignored Businesses aiming to keep lead response times sharp and consistent
By Rahul JoshiAI-powered cover letter generator with resume matching & Google Docs
This workflow generates a tailored cover letter using a provided resume and job description. Users submit a job description via form (or workflow input), the workflow uses an LLM to write a professional, casual cover letter, then creates and populates a Google Doc and redirects the user to download or review it. --- What You Must Update Before Running Resume Content Update the Configuration node to include your own resume text. This resume is injected directly into the prompt and used as the sole source of experience and qualifications. LLM Credentials The workflow uses OpenRouter with an OpenAI-compatible model. You must: Add your own OpenRouter API credentials Optionally change the model selection if desired Google Docs Credentials This workflow creates and edits Google Docs. You must: Connect your own Google Docs OAuth credentials Update the destination folder ID if you want files saved elsewhere --- What You Can Configure Prompt Tone & Constraints Edit the Write Cover Letter agent system prompt to adjust: Tone (more formal or more casual) Length Writing style constraints Execution Method The workflow supports: Manual execution via form submission Execution as a sub-workflow via workflow inputs
By Joel GambleGenerate property investment reports with GPT-4, SerpAPI, Google Docs & Airtable
How It Works ⚙️ This workflow is a comprehensive, multi-AI-agent system that acts as a virtual property analysis team. It automates the entire process from initial data research to final report delivery. Data Research (AI Agent 1): The workflow is triggered by an input of a property address. The first AI agent, powered by Google Search, immediately scours the web to gather all relevant public data, including historical sales data, local rental prices, and nearby amenities. Market Analysis (AI Agent 2): All the raw data from the first agent is consolidated. A second AI agent, powered by OpenAI, then performs a deep, intelligent analysis. It identifies key insights, calculates potential rental yield, and assigns a definitive "Investment Score." Report Generation (AI Agent 3): A third AI agent, also using OpenAI, takes the structured analysis and writes a professional, persuasive report. The report is then automatically populated into a pre-designed Google Docs template, ensuring a polished and professional look. Database Logging & Delivery: The final report is automatically converted to a PDF and sent to the client via Gmail. Simultaneously, the key findings (address, score, key takeaways) are logged into an Airtable database for quick reference and tracking. How to Set Up 🛠️ Import the Workflow: Copy the provided workflow JSON and import it into your n8n instance. Configure Credentials: Google Search: Add your API Key. OpenAI: Add your API Key. Google Docs: Add your OAuth2 credential. Airtable: Add your API Key and token. Gmail: Add your OAuth2 credential. Customize Workflow Nodes: Node 6 (Google Docs): Create a new Google Doc to serve as your report template. Use placeholders like {{ reportcontent }} and {{ propertyaddress }} to define where the AI-generated text will go. Then, copy the document ID from the URL and paste it into this node's parameters. Node 7 (Airtable): Replace [YOUR AIRTABLE BASE ID] and Property Reports with your specific Airtable base and table names. Ensure your table has columns that match the data you are sending (Property Address, Investment Score, etc.). Save & Activate: Once all settings and credentials are configured, save the workflow and click the "Inactive" toggle in the top-right corner to make it live.
By MarthDiscover decision makers by responsibilities (not titles) with Octave & Airtable
Discover relevant contacts from target accounts using Octave intelligent prospecting Who is this for? Sales development teams, account-based marketing professionals, and RevOps teams who are tired of generic job title filtering that misses the real decision makers. Built for teams that need to find the right people based on actual responsibilities and business context, not just titles on LinkedIn. What problem does this solve? Most prospecting tools are flying blind when it comes to finding the right contacts. You search for "VP of Engineering" but miss the "Head of Platform" who actually owns your use case. You filter by "Marketing Director" but the "Growth Lead" is the real buyer. Traditional prospecting relies on job title matching, but titles vary wildly across companies. This workflow uses Octave's context engine to find contacts based on who actually does the work your solution impacts, regardless of their specific job title. What this workflow does Target Account Processing: Reads target account lists from Airtable (or other data sources) Processes company domains for intelligent contact discovery Handles batch processing for multiple target accounts Context-Aware Contact Discovery: Uses Octave's prospector agent to find relevant stakeholders within target organizations Leverages your defined personas to identify the right people based on responsibilities, not just titles Analyzes organizational structure, role responsibilities, and KPIs to match contacts to your solution Discovers decision makers and influencers who might be missed by traditional job title searches Structured Contact Output: Returns discovered contacts with complete profile information Includes LinkedIn profiles, contact details, and role context Organizes contacts by relevance and decision-making authority Exports contact lists back to Airtable for sales team action Setup Required Credentials: Octave API key and workspace access Airtable API credentials (or your preferred contact management platform) Access to your target account list Step-by-Step Configuration: Set up Target Account Source: Add your Airtable credentials to n8n Replace your-airtable-base-id and your-accounts-table-id with your actual account list Ensure your account list includes company domains for prospecting Configure trigger method (manual, scheduled, or webhook-based) Configure Octave Prospector Agent: Add your Octave API credentials in n8n Replace your-octave-prospector-agent-id with your actual prospector agent ID Configure your prospector with relevant personas and role definitions Test prospecting with sample companies to verify contact quality Set up Contact Output Destination: Replace your-contacts-table-id with your target contact list table Configure field mapping between Octave output and your contact database Set up data validation and deduplication rules Test contact creation and data formatting Customize Contact Selection: Configure which personas to prioritize in your prospector agent Set relevance thresholds for contact discovery Define organizational levels to target (individual contributors vs. management) Adjust contact volume per account based on your outreach capacity Required Account List Format: Your Airtable (or data source) should include: Company Name Company Domain (required for prospecting) Account status/priority (optional) Target personas (optional) How to customize Prospector Configuration: Customize contact discovery in your Octave prospector agent: Persona Targeting: Define which of your Library personas to prioritize when prospecting Role Responsibilities: Configure the specific responsibilities and KPIs that indicate a good fit Organizational Level: Target specific levels (IC, manager, director, VP, C-level) based on your solution Company Size Adaptation: Adjust prospecting approach based on organization size and structure Contact Selection Criteria: Refine who gets discovered: Decision-Making Authority: Prioritize contacts with budget authority or implementation influence Problem Ownership: Focus on roles that directly experience the pain points your solution solves Technical Influence: Target contacts who influence technical decisions if relevant to your offering Process Ownership: Identify people who own the processes your solution improves Data Integration: Adapt for different contact management systems: Replace Airtable with your CRM, database, or spreadsheet system Modify field mapping to match your contact database schema Add data enrichment steps for additional contact information Integrate with email platforms for immediate outreach Batch Processing: Configure for scale: Adjust processing volume based on API limits and prospecting quotas Add scheduling for regular account list updates Implement error handling for accounts that can't be prospected Set up monitoring for prospecting success rates Use Cases Account-based marketing contact list generation Sales territory planning and contact mapping Competitive displacement campaign targeting Product expansion within existing customer accounts Event-based prospecting for specific personas Market research and competitive intelligence gathering
By Nalin