🤖 AI-Powered LinkedIn Lead Generation Automation | n8n
Built an automated B2B lead generation workflow using n8n that connects AI research, prospect discovery, data enrichment, verification, and Google Sheets.
The workflow:
🎯 Defines target companies and ideal job roles
🤖 Uses AI to identify relevant positions
🔎 Finds potential LinkedIn prospects
📥 Extracts and processes lead information
📧 Enriches leads with relevant business emails
✅ Filters qualified prospects
📊 Automatically stores leads in Google Sheets
Pipeline:
Target → AI Research → Search → Extract → Enrich → Verify → Filter → Store
Tech Stack:
n8n, AI Agents, APIs, data processing, lead enrichment, Google Sheets
The goal was to turn a repetitive lead research process into a structured workflow that can save time, improve consistency, and make prospecting easier to scale.
An automated n8n workflow that receives incoming leads via webhook, validates them, qualifies them with AI (Google Gemini), scores them, and syncs the result to Airtable without creating duplicates.
Most inbound bottlenecks aren't caused by low lead volume—they happen during triage.
When inquiries roll into a Google Sheet or CRM, operations teams often waste 10–15 minutes per submission reading through messy paragraphs, deciphering budget mismatch, and manually deciding who gets an immediate call vs. a standard rate sheet.
Instead of adding a bulky CRM add-on, I documented a lightweight 3-step automation pipeline built inside Make:
Intake & Extraction: An active listener triggers immediately upon a new submission, extracting core variables: client scope, target deliverables, timeline, and stated budget.
Context-Aware Evaluation (Gemini 2.5 Flash): The payload passes through an AI evaluation node with strict guardrails:
Evaluates budget against minimum agency criteria.
Assigns a triage score: Hot (immediate priority), Warm (nurture), or Cold (disqualified/template reply).
Generates a strict 15-word operational summary—no long paragraphs.
Determines an automated next step (e.g., routing to an account lead or flagging for asynchronous follow-up).
Data Return & Clean Pipeline: Appends the structured classification directly back to the master operational tracker, keeping the sheet filterable and clean.
The outcome: The sales and operations team can filter the entire board by priority status in two clicks, cutting first-touch response time to minutes without manually reading every single submission.
I’ve documented the full scenario map, execution logic, and data schemas below.
Curious how other agency operators and automation leads here handle their lead enrichment layer—are you doing AI scoring at the form level, or later in the CRM pipeline?:
For the budget gate, I'd keep an unstated budget separate from one explicitly below your minimum. Does the Make route ask for clarification when the amount or currency is missing, so a potentially suitable lead doesn't fall into Cold by default?