Build an AI Lead Qualifier in n8n in 15 Minutes
Learn how to build a 5-node n8n workflow that uses an LLM to score form submissions and pings Slack only for qualified leads.
Stock photo for illustration only, not from the actual event
- 5-node n8n workflow for automated lead qualification using an LLM
- Works with OpenAI, Groq, OpenRouter, or local Ollama via plain HTTP Request
- Sends Slack alerts only for leads scoring 70 or higher
- Free workflow template available on GitHub with optional advanced agent pack
Inbound leads often go cold while you are busy managing other tasks. This article explores a streamlined 5-node n8n workflow designed to score every incoming form submission using a Large Language Model (LLM) and ping Slack exclusively for leads that are worth your time.
The workflow pipeline flows through Webhook, Config, Build Prompt, LLM via HTTP Request, Parse, an IF condition checking if the score is 70 or higher, Slack notification, and finally responding to the user.
Stock photo for illustration only, not from the actual event
A major advantage of this setup is that it avoids vendor-specific AI nodes. The LLM call uses a standard HTTP Request to /chat/completions, ensuring compatibility with OpenAI, Groq, OpenRouter, or local Ollama instances by simply changing the baseUrl.
Leveraging a low-code automation platform like n8n combined with direct HTTP API calls to LLMs grants developers maximum architectural flexibility. It avoids vendor lock-in and allows seamless switching between commercial APIs and local open-source models depending on budget and privacy requirements.
The JSON configuration is freely available on GitHub. For those looking to expand their automation toolkit, the creator has also packaged nine additional workflows—including support agents, invoice extractors, and resume screeners—into the Founder AI Agent Pack.
Source: Dev.to
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