Dimensions overall score 7.0

GitHub Code Pulse

No public code linked for this paper yet.

• RAD-AI increases EU AI Act Annex IV documentation addressability from approximately 36% to 93% (mean rating) according to a regulatory coverage assessment with six experienced practitioners.result 95%
• Existing architecture documentation frameworks (arc42 and C4 model) were designed for deterministic software and cannot capture probabilistic behavior, data-dependent evolution, or dual ML/software lifecycles.limitation 90%
• RAD-AI is a backward-compatible extension framework that augments arc42 with eight AI-specific sections and C4 with three diagram extensions.method 95%
• No existing architecture documentation framework provides structured guidance for producing the technical documentation mandated by the EU AI Act Annex IV.limitation 90%
• Comparative analysis on two production AI platforms (Uber Michelangelo, Netflix Metaflow) demonstrates that documentation deficiencies are structural rather than domain-specific.result 85%
• AI-augmented ecosystems are becoming the architectural norm for smart cities, autonomous fleets, and intelligent platforms.market 80%
• RAD-AI provides a systematic EU AI Act Annex IV compliance mapping, offering a concrete documentation path for the August 2026 enforcement deadline.technical 90%
• An illustrative smart mobility ecosystem case study reveals ecosystem-level concerns, including cascading drift and differentiated compliance obligations, that are invisible under standard notation.result 85%

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Startup potential card