This paper explores the design of an interpretable AI system capable of performing real-time triage of car insurance claims using lightweight parallelism. By breaking the claim assessment workflow into modular, independent tasks—including damage image evaluation, document verification, fraud flagging, and cost estimation—the system can operate in parallel, enabling rapid response while maintaining clarity in decision logic. Each component of the triage system is built with interpretability in mind, relying on transparent models like decision trees or rule-based frameworks, and supported by efficient parallel computing frameworks that enable scaling on multicore processors or cloud infrastructure. The paper investigates how to balance computational performance with intelligibility, particularly in environments where AI decisions directly impact customers’ financial outcomes. Even under real-time constraints, AI models can remain interpretable and trustworthy—provided they are designed using scalable, parallel architectures tailored to the structure of the task.