Build an AI Lead Qualifier in n8n in 15 Minutes (Free Workflow)

Description and use

Create an AI lead qualifier in n8n in 15 minutes, using an LLM to score incoming leads and ping Slack only for high‑score leads.

Problem

Inbound leads go cold while you're busy.

Outcome

The workflow scores inbound leads from 0 to 100 using BANT, returns a JSON with score, tier, summary, reasons, and next_step, and sends a Slack ping for scores ≥70.

Workflow

• Webhook
• Config
• Build Prompt
• LLM (HTTP)
• Parse
• IF score ≥ 70
• Slack
• Respond

Evidence

Inbound leads go cold while you're busy. Here's a 5-node n8n workflow that scores every form submission with an LLM and pings Slack only for the leads worth your time.

Source

Webhook → Config → Build Prompt → LLM (HTTP) → Parse → IF score ≥ 70 → Slack → Respond

Source

I don't use a vendor-specific AI node. The LLM call is a plain HTTP Request to /chat/completions, so it works with OpenAI, Groq, OpenRouter, or local Ollama. Just change baseUrl.

Source

You are a B2B sales analyst. Score inbound leads 0-100 for fit and intent using BANT. Return JSON: {score, tier, summary, reasons[], next_step}

Source

Response: {"score":82,"tier":"hot","next_step":"Book a call this week..."}, and a Slack ping.

Source