Asset Documentation with LLMs & Archimate

Enterprise architects often struggle with maintaining comprehensive and accurate documentation of IT assets. The challenge becomes even greater as organizations scale and their technology landscape becomes more complex. In this article, I explore how Large Language Models (LLMs) can be combined with Archimate modeling standards to revolutionize enterprise asset documentation.

The Documentation Challenge

Documentation of enterprise assets is typically:

• Time-consuming and manual
• Prone to human error
• Quickly outdated
• Inconsistent across teams and departments
• Difficult to maintain at scale

These challenges lead to poor visibility, suboptimal decision-making, and increased operational risks.

Leveraging LLMs for Asset Documentation

Large Language Models offer several capabilities that make them ideal for improving asset documentation:

• Automated extraction of asset information from unstructured data sources
• Classification and categorization of assets according to predefined taxonomies
• Relationship identification between different assets and systems
• Standardized output formatting that adheres to architectural frameworks
• Natural language generation for creating human-readable documentation

Integration with Archimate Modeling

Archimate, as an open and independent enterprise architecture modeling language, provides a common language for describing, analyzing, and visualizing architecture within and across business domains.

By combining LLMs with Archimate principles, organizations can:

1. Automate documentation generation in a standardized format
2. Maintain consistency across the enterprise architecture
3. Visualize relationships between business, application, and technology layers
4. Update documentation in real-time as changes occur
5. Enhance searchability and accessibility of architectural information

Implementation Approach

The implementation involves several key components:

1. Data Collection

• API integrations with existing IT management systems
• Document processing of legacy documentation
• Web scraping of internal wikis and knowledge bases
• Log analysis from operational systems

2. LLM Processing Pipeline

• Text extraction and preprocessing
• Entity recognition for identifying assets, stakeholders, and relationships
• Classification according to Archimate domains
• Relationship mapping between identified entities

3. Archimate Model Generation

• Automated creation of Archimate model elements
• Visualization rendering in standard formats
• Versioning and change tracking
• Export capabilities to various tools and formats

Results and Benefits

Organizations implementing this approach have experienced:

• 60% reduction in documentation time
• Improved accuracy of asset information
• Enhanced visibility into the technology landscape
• Better alignment between business and IT
• More informed decision-making for technology investments

Conclusion

The combination of Large Language Models and Archimate modeling offers a powerful solution to the persistent challenge of enterprise asset documentation. By automating and standardizing this process, organizations can maintain a more accurate, up-to-date, and useful repository of architectural knowledge.

For organizations looking to improve their enterprise architecture practices, this approach provides a scalable and efficient pathway to better documentation, increased visibility, and enhanced decision-making.

For more information, visit the full article on LinkedIn.