| Large Language Models (LLMs) are transforming legal and audit practices, yet their adoption in high-stakes domains remains limited by concerns around reliability and transparency. To address these challenges, we present audit.ai, an explainable legal assistant that integrates a multi-agent LLM architecture with a knowledge graph backend. By grounding responses in structured legal knowledge, audit.ai enables transparent, verifiable reasoning. Our results hint at advantages compared to long-context LLM baselines in producing more helpful and better-supported answers. Through combined visual and logical transparency, audit.ai provides a foundation for trustworthy AI in domains where accountability is critical. |
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