Article
Automated Local Lead Generation with n8n: Building an AI ...
medium.com
Quoted on this wiki
Every place a page here uses this source, in the order the words come in it.
Trigger when a new lead is added to the lead list on Google Sheets “This is where an LLM really shines; it allows for classification of data based on unstructured text.” Below, I will elaborate on the most important nodes for Part 1 of the workflow.
Employ an AI Agent, powered by GPT-4o mini, to generate optimized search queries based on user input (e.g., “Find 5 digital agencies in Berlin that are focused on digital growth consulting”) and use them for the Google Places tool. “Insert the found leads into Google Sheets” Below is an overview of the workflow we will be building for Part 1:
Part 2 of the workflow will: “Trigger when a new lead is added to the lead list on Google Sheets” Qualify, prioritize and enrich leads based on extracted company details (company mission, size, contact information etc.). This is where an LLM really shines; it allows for classification of data based on unstructured text. For this, we will be combining Firecrawl and Jina.ai to employ a powerful lead enrichment agent. It will scrape different URLs and only stop when it has gathered enough information on the lead.
-- “Although agentic frameworks like LangGraph are really powerful, they come with added complexity making it harder to develop and maintain them.” Also, what makes this workflow unique is its tool use; Many tutorials, my previous LangGraph guide included, use top-level wrappers like Serper to fetch leads, which are usually quite expensive if you want to gather a fair amount of leads. In this guide however, I directly use the Google Places API, allowing for practically free scraping of thousands of leads (GCP has a really generous free tier). While this adds some setup complexity, the trade-off is well worth it given the cost and flexibility benefits, allowing for powerful local lead scraping.
In my last article, I described how to develop an AI lead generation agent team using the LangGraph framework. Although agentic frameworks like LangGraph are really powerful, they come with added complexity making it harder to develop and maintain them. In this guide, I will be showing how to develop an AI lead generation team using n8n, abstracted on a workflow I made for one of my clients. With the growing popularity for lead enrichment tools like Clay, although requiring a bit more technical expertise, this guide offers a practical way of building a virtually free alternative. “Many tutorials, my previous LangGraph guide included, use top-level wrappers like Serper to fetch leads, which are usually quite expensive if you want to gather a fair amount of leads.” Press enter or click to view image in full size