A portfolio of my automation projects, built with n8n to connect tools, APIs and AI and turn real-world processes into practical automated workflows.
📊 B2B Lead Enrichment & Qualification
Case
Turning inbound leads into structured, qualified company intelligence.
An inbound B2B lead often contains little more than a name, email address and company CVR number. Before a salesperson can assess whether the lead is relevant, additional company information typically has to be researched manually.
The challenge is therefore not simply to look up one company, but to create a repeatable process that can enrich and qualify multiple leads consistently.
The case demonstrates how company data from the Danish CVR register can be combined with predefined qualification rules to transform basic lead information into structured sales intelligence.
Results
The workflow creates one structured company profile per inbound lead, combining the original lead information with company data from the Danish CVR register.
This makes it possible to turn a basic lead list into a structured dataset that can be filtered and evaluated systematically rather than researched manually.
The qualification logic is deterministic and transparent. Leads are evaluated based on company status, bankruptcy, advertising protection, number of employees and industry relevance.
For example, an active company with 50+ employees in a target industry can automatically be classified as High, while an inactive or advertising-protected company can be flagged as Do Not Contact.
The resulting dataset can be used as a foundation for subsequent sales workflows, CRM integration or further lead qualification.
Cost & API Usage
No direct per-lead cost. With a self-hosted n8n setup and the free APICVR.dk API, the workflow can be operated at very low cost. APICVR.dk does not require authentication, but rate limits apply and sustained heavy traffic should be coordinated with the API provider.
Some of the automatically enriched and qualified B2B leads in Google Sheets:
Workflow
—> Workflow overview:
Workflow details:
📬Newsletter Intelligence Pipeline
Case
Turning years of newsletter data into structured marketing intelligence.
A mailbox containing years of newsletters represents a large amount of potentially valuable marketing data, but the information is:
- Unstructured
- Difficult to search systematically
- Time-consuming to analyse manually
- Spread across hundreds or thousands of individual emails
The challenge is therefore not simply to analyse one newsletter, but to create a repeatable pipeline capable of processing a large historical dataset.
The case is about demonstrating how unstructured information can be converted into structured data through automation and AI.
Results
The workflow creates one structured marketing analysis per newsletter, combining text, visual content and newsletter metadata in a consistent format.
This makes it possible to turn a large archive of unstructured newsletter data into a structured marketing intelligence dataset that can be searched, compared and analysed systematically.
The completed workflow can analyse a newsletter and convert its content into structured marketing intelligence at an estimated cost of approximately $0.01 per newsletter analysed.
The analysis captures marketing objectives, target audience, value proposition, key messages, calls to action, persuasion techniques, copywriting style, promotional strategy, brand positioning and visual strategy – including strengths, weaknesses and key marketing insights.
Example of the structured analysis:
Workflow
Workflow overview:
Workflow details:
🤖 Automated SEO Content Pipeline
Case
Built an automated content publishing pipeline that transforms a single unpublished entry in Google Sheets into a complete WordPress article.
The workflow generates the article, SEO metadata and two AI-generated images, publishes everything to WordPress and automatically updates the Yoast SEO fields.
Results
The completed workflow can produce an article with over 2,000 words, two original AI-generated images and SEO metadata at an estimated cost of approximately $0.26 per article.
See the live result in [Lab → Tools].
Workflow
Workflow overview:
Workflow details:
🐳 Setting Up a Self-Hosted n8n Automation Platform
Case
Built a self-hosted n8n automation platform using Docker, creating a persistent environment for building, testing and running automation workflows.
The setup covers the deployment of n8n, persistent data storage and the basic infrastructure required to run the platform independently.
Results
A working self-hosted n8n environment with persistent workflow data, providing a foundation for building and testing automation projects.
Workflow
host OS → Docker
runtime → Volume
storage → Compose
config → n8n
ready
Cost & Usage
Unlike Zapier and Make, n8n does not charge per workflow execution when self hosted. The platform itself can therefore be run without execution based fees, leaving the main variable costs to the APIs and AI services used by the workflows.
In this setup, n8n runs locally on my own machine, so there is no additional hosting cost. n8n can also be hosted on an external server or VPS, where low cost hosting options are available.
This makes self hosted n8n particularly interesting for workflows with high execution volumes, where usage based automation platforms can become more expensive.