The development of our AI-based Safety Advisor continues to advance, driven by real-world use cases across the safety lifecycle - including safety management, impact analysis and safety analysis. We implemented a Retrieval-Augmented Generation framework based on existing safety artifacts, combined with task-specific LLM selection to maximize reasoning accuracy and consistency.
To handle structural dependencies across work packages, we introduced LongGraph as a backbone for safety logic flow, supported by a vector database enabling semantic retrieval within context. This setup has already led to measurable improvements, such as the identification of critical change impacts and increased consistency in artifact alignment. In parallel, we are strengthening the platform’s robustness through architectural and validation measures to support traceable and audit-ready results in compliance-critical environments.
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