Building accurate and reliable AI in regulated industries like healthcare presents significant engineering challenges. Heidi, an AI care partner, exemplifies successful modernization with its Heidi Scribe product, which automates clinician administrative tasks globally. Yu Liu, Heidi's CTO, emphasizes that even small error rates in healthcare AI are critical safety issues, necessitating robust architecture. Data residency is a fundamental requirement for Heidi, ensuring patient data remains within regional regulatory boundaries. This is achieved through logically isolated production deployments across the world.Auditability is a core component, requiring the ability to track model inputs, outputs, and user modifications. Heidi prioritizes safe change by default through rigorous testing and deployment processes, rather than rapid iteration. The company chose a document database, MongoDB, to manage diverse and evolving medical data that connects seamlessly with AI workflows. MongoDB's flexibility accommodates changing data shapes without requiring constant database restructuring.MongoDB Atlas offers integrated AI features like Vector Search, eliminating the need for separate vector databases. This enables semantic search and connects medical terms to external knowledge bases, with regional isolation ensuring compliance. Heidi's architecture, powered by MongoDB's globally distributed platform, facilitates scalable and compliant AI deployments.