Isaacus, an Australian AI startup, has introduced the Kanon 2 Embedder, a cutting-edge legal embedding model that outperforms OpenAI and Google in legal information retrieval across multiple jurisdictions and domains. This achievement is measured by the Massive Legal Embedding Benchmark (MLEB), a comprehensive open-source benchmark developed by Isaacus to evaluate legal retrieval capabilities. Kanon 2 Embedder, derived from a legal foundation model trained on data from 38 jurisdictions, offers superior accuracy and speed compared to its competitors, setting a new standard in the legal tech industry. Isaacus emphasizes the importance of data sovereignty and offers air-gapped model containers for heightened privacy and security concerns, while also making the MLEB data and code openly available on platforms like Hugging Face and GitHub. The company invites the legal tech community to explore the capabilities of Kanon 2 Embedder and aims to elevate global legal retrieval quality through its innovative approach and commitment to respecting legal data sensitivity.