CLASI is a high-quality, human-like simultaneous interpretation system developed by ByteDance's research team. It balances translation quality and latency through an innovative data-driven reading and writing strategy, enhances the translation of domain-specific terms with a multimodal retrieval module, and generates fault-tolerant translations using large language models (LLMs), taking into account input audio, historical context, and retrieval information. In real-world scenarios, CLASI achieved an Effective Information Proportion (VIP) of 81.3% for Chinese to English and 78.0% for English to Chinese, significantly outperforming other systems.