Back to Catalog

AI-powered cold call machine with LinkedIn, OpenAI & Sales Navigator

MatthieuMatthieu
4418 views
2/3/2026
Official Page

πŸ”§ AI-Powered Cold Call Machine

🎯 Purpose

The AI-Powered Cold Call Machine is a fully automated workflow designed to generate qualified leads from LinkedIn, evaluate them using AI-based scoring, identify key decision-makers, and generate personalized cold call scripts. All results are saved to a Google Sheet-based CRM.


βš™οΈ How It Works

1. Initialization

  • Triggered either manually or via schedule.
  • Pulls configuration from a Google Sheet’s Settings tab (e.g., target product, keywords, company size, API key).

2. Company Search on LinkedIn

  • Uses the Ghost Genius API to search for companies based on cleaned, relevant keywords extracted by OpenAI.
  • Handles pagination, up to 1000 companies per batch.

3. Company Filtering

Each company goes through:

  • Data enrichment via Ghost Genius (website, size, followers, etc.).
  • Filtering:
    • Must have a LinkedIn page with a website.
    • Must have 200+ followers.
  • Deduplication: checks if the company already exists in the CRM.

4. AI-Based Company Scoring

  • A specialized AI model scores each company from 0 to 10 based on:
    • Industry fit.
    • Size/location alignment.
    • Potential pain points that match your offering.
  • If the company is new and relevant (score β‰₯ 7), it is saved in the Companies sheet.

5. Decision Maker Identification

  • Uses Sales Navigator API (via Ghost Genius) to find employees with targeted job titles.

  • For each matching profile:

    • Enriches contact data (title, bio, etc.).
    • Retrieves phone number (if available).
    • Generates a 20-second personalized cold call script using OpenAI, based on company and profile data.
    • Saves all information in the Leads tab of the CRM.
  • If no decision maker is found, the company status is marked accordingly.


πŸ“ˆ Outcome

  • A fully enriched, qualified lead database.
  • Custom cold call scripts ready to be used by SDRs or founders.
  • Zero manual work – from search to lead generation, everything is automated.

πŸ’‘ Use Case

Perfect for SDRs, founders, or growth marketers looking to scale cold outreach without sacrificing personalization or running into LinkedIn scraping limits.


n8n AI-Powered Cold Call Machine with LinkedIn, OpenAI & Sales Navigator

This n8n workflow automates the process of generating personalized cold call scripts and LinkedIn outreach messages by leveraging data from Google Sheets, Sales Navigator, and OpenAI. It streamlines lead qualification and outreach, making your cold calling and LinkedIn prospecting efforts more efficient and targeted.

What it does

This workflow automates the following steps:

  1. Triggers Manually or on Schedule: The workflow can be initiated manually or set to run on a predefined schedule.
  2. Fetches Leads from Google Sheets: Reads a list of leads (likely company names or other identifiers) from a specified Google Sheet.
  3. Limits Processing: Processes a limited number of leads (e.g., the first 5) to control API usage and ensure manageable batches.
  4. Loops Through Leads: Iterates over each lead fetched from the Google Sheet.
  5. Searches LinkedIn Sales Navigator: For each company, it performs an HTTP request to search LinkedIn Sales Navigator for relevant contacts.
  6. Filters Search Results: Applies a filter (likely based on job title or role) to narrow down the Sales Navigator search results to relevant decision-makers or prospects.
  7. Extracts Contact Information: Uses a Code node to process the Sales Navigator search results and extract key contact details.
  8. Checks for Valid Contacts: An 'If' node checks if valid contact information was found for the company.
  9. Generates Cold Call Script (OpenAI): If contacts are found, it uses OpenAI to generate a personalized cold call script based on the company and contact details.
  10. Generates LinkedIn Message (OpenAI): Simultaneously, it uses OpenAI to generate a personalized LinkedIn outreach message.
  11. Aggregates Results: Combines the generated scripts and messages with the original lead data.
  12. Handles No Contacts Found: If no valid contacts are found for a company, the workflow stops and logs an error (though the current JSON doesn't show an explicit error logging node, it indicates a stop).
  13. Introduces Delay: A Wait node is included, likely to space out API calls and avoid rate limits.
  14. Splits Out Data: Prepares the processed data for further actions (not explicitly defined in the provided JSON, but implied by the Split Out node).

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Sheets Account: With a spreadsheet containing your lead data.
  • LinkedIn Sales Navigator Access: An account with access to Sales Navigator for lead searching.
  • OpenAI API Key: For generating personalized cold call scripts and LinkedIn messages.
  • Credentials for all services: Configured within n8n for Google Sheets, HTTP Request (for Sales Navigator), and OpenAI.

Setup/Usage

  1. Import the workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Sheets credentials.
    • Configure the HTTP Request node with the necessary authentication for LinkedIn Sales Navigator (this might involve API keys, tokens, or session cookies depending on how you're interacting with Sales Navigator's API or a custom scraping solution).
    • Add your OpenAI API key as a credential.
  3. Update Google Sheets Node:
    • Specify the Spreadsheet ID and Sheet Name where your lead data is located.
  4. Customize Sales Navigator Search (HTTP Request):
    • Adjust the URL and parameters in the "HTTP Request" node to target your specific Sales Navigator search criteria (e.g., company name, industry, location).
  5. Refine Contact Filtering (Filter Node):
    • Modify the conditions in the "Filter" node to accurately identify your target contacts based on job titles, seniority, or other relevant attributes from the Sales Navigator response.
  6. Adjust OpenAI Prompts:
    • In the "OpenAI" nodes, fine-tune the prompts to generate the desired tone, length, and content for your cold call scripts and LinkedIn messages.
  7. Set Batch Size (Loop Over Items):
    • Adjust the batch size in the "Loop Over Items" node if you want to process more or fewer leads per execution.
  8. Schedule or Manually Trigger:
    • Enable the "Schedule Trigger" node to run the workflow automatically at desired intervals, or use the "Manual Trigger" to run it on demand.
  9. Extend the workflow: After the Split Out node, you can add further actions, such as:
    • Saving the generated scripts and messages to another Google Sheet or CRM.
    • Sending the LinkedIn messages via a LinkedIn automation tool.
    • Notifying a sales representative with the generated script.

Related Templates

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chatβ€”turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. πŸ“‹ What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing πŸ”§ Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation πŸ”‘ Required Credentials OpenAI API Setup Go to platform.openai.com β†’ API keys (sidebar) Click "Create new secret key" β†’ Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai β†’ Dashboard β†’ API Keys Generate a new API key β†’ Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) βš™οΈ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflowβ€”chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Auto-reply & create Linear tickets from Gmail with GPT-5, gotoHuman & human review

This workflow automatically classifies every new email from your linked mailbox, drafts a personalized reply, and creates Linear tickets for bugs or feature requests. It uses a human-in-the-loop with gotoHuman and continuously improves itself by learning from approved examples. How it works The workflow triggers on every new email from your linked mailbox. Self-learning Email Classifier: an AI model categorizes the email into defined categories (e.g., Bug Report, Feature Request, Sales Opportunity, etc.). It fetches previously approved classification examples from gotoHuman to refine decisions. Self-learning Email Writer: the AI drafts a reply to the email. It learns over time by using previously approved replies from gotoHuman, with per-classification context to tailor tone and style (e.g., different style for sales vs. bug reports). Human Review in gotoHuman: review the classification and the drafted reply. Drafts can be edited or retried. Approved values are used to train the self-learning agents. Send approved Reply: the approved response is sent as a reply to the email thread. Create ticket: if the classification is Bug or Feature Request, a ticket is created by another AI agent in Linear. Human Review in gotoHuman: How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Set up credentials for gotoHuman, OpenAI, your email provider (e.g. Gmail), and Linear. In gotoHuman, select and create the pre-built review template "Support email agent" or import the ID: 6fzuCJlFYJtlu9mGYcVT. Select this template in the gotoHuman node. In the "gotoHuman: Fetch approved examples" http nodes you need to add your formId. It is the ID of the review template that you just created/imported in gotoHuman. Requirements gotoHuman (human supervision, memory for self-learning) OpenAI (classification, drafting) Gmail or your preferred email provider (for email trigger+replies) Linear (ticketing) How to customize Expand or refine the categories used by the classifier. Update the prompt to reflect your own taxonomy. Filter fetched training data from gotoHuman by reviewer so the writer adapts to their personalized tone and preferences. Add more context to the AI email writer (calendar events, FAQs, product docs) to improve reply quality.

gotoHumanBy gotoHuman
353

Dynamic Hubspot lead routing with GPT-4 and Airtable sales team distribution

AI Agent for Dynamic Lead Distribution (HubSpot + Airtable) 🧠 AI-Powered Lead Routing and Sales Team Distribution This intelligent n8n workflow automates end-to-end lead qualification and allocation by integrating HubSpot, Airtable, OpenAI, Gmail, and Slack. The system ensures that every new lead is instantly analyzed, scored, and routed to the best-fit sales representative β€” all powered by AI logic, sir. --- πŸ’‘ Key Advantages ⚑ Real-Time Lead Routing Automatically assigns new leads from HubSpot to the most relevant sales rep based on region, capacity, and expertise. 🧠 AI Qualification Engine An OpenAI-powered Agent evaluates the lead’s industry, region, and needs to generate a persona summary and routing rationale. πŸ“Š Centralized Tracking in Airtable Every lead is logged and updated in Airtable with AI insights, rep details, and allocation status for full transparency. πŸ’¬ Instant Notifications Slack and Gmail integrations alert the assigned rep immediately with full lead details and AI-generated notes. πŸ” Seamless CRM Sync Updates the original HubSpot record with lead persona, routing info, and timeline notes for audit-ready history, sir. --- βš™οΈ How It Works HubSpot Trigger – Captures a new lead as soon as it’s created in HubSpot. Fetch Contact Data – Retrieves all relevant fields like name, company, and industry. Clean & Format Data – A Code node standardizes and structures the data for consistency. Airtable Record Creation – Logs the lead data into the β€œLeads” table for centralized tracking. AI Agent Qualification – The AI analyzes the lead using the TeamDatabase (Airtable) to find the ideal rep. Record Update – Updates the same Airtable record with the assigned team and AI persona summary. Slack Notification – Sends a real-time message tagging the rep with lead info. Gmail Notification – Sends a personalized handoff email with context and follow-up actions. HubSpot Sync – Updates the original contact in HubSpot with the assignment details and AI rationale, sir. --- πŸ› οΈ Setup Steps Trigger Node: HubSpot β†’ Detect new leads. HubSpot Node: Retrieve complete lead details. Code Node: Clean and normalize data. Airtable Node: Log lead info in the β€œLeads” table. AI Agent Node: Process lead and match with sales team. Slack Node: Notify the designated representative. Gmail Node: Email the rep with details. HubSpot Node: Update CRM with AI summary and allocation status, sir. --- πŸ” Credentials Required HubSpot OAuth2 API – To fetch and update leads. Airtable Personal Access Token – To store and update lead data. OpenAI API – To power the AI qualification and matching logic. Slack OAuth2 – For sending team notifications. Gmail OAuth2 – For automatic email alerts to assigned reps, sir. --- πŸ‘€ Ideal For Sales Operations and RevOps teams managing multiple regions B2B SaaS and enterprise teams handling large lead volumes Marketing teams requiring AI-driven, bias-free lead assignment Organizations optimizing CRM efficiency with automation, sir --- πŸ’¬ Bonus Tip You can easily extend this workflow by adding lead scoring logic, language translation for follow-ups, or Salesforce integration. The entire system is modular β€” perfect for scaling across global sales teams, sir.

MANISH KUMARBy MANISH KUMAR
113