Automated DNB Company Data Extraction Workflow

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This n8n workflow automates the process of searching, scraping, and extracting structured company data from DNB using Bright Data MCP Search and OpenAI GPT-4 mini. The workflow begins with a manual trigger, where you input a search query and configure notification URL. It then uses Bright Data’s MCP tools to perform a Google search for the specified company or URL. The scraper node extracts webpage content from DNB’s site, which is then processed by OpenAI’s GPT-4 mini models to identify URLs and extract detailed company profiles in a highly structured JSON format.

Key steps include:

– Initiating the workflow manually for testing or execution.

– Setting input parameters such as the target URL and notification webhook.

– Using Bright Data MCP Client nodes to search Google and scrape DNB pages.

– Processing the webpage content with OpenAI GPT-4 mini models to extract URLs and detailed company data.

– Structuring the extracted data into JSON according to predefined schemas.

– Saving the structured data to disk and sending a webhook notification with the results.

This workflow is particularly useful for market research, competitor analysis, or data enrichment tasks where large-scale, automated collection of company information from DNB is required, saving time and ensuring consistency in data extraction.

Node Count

11 – 20 Nodes

Nodes Used

@n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.outputParserStructured, function, httpRequest, manualTrigger, n8n-nodes-mcp.mcpClient, readWriteFile, set, stickyNote

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