The problem
Hospital teams work across operational tasks and large bodies of knowledge. An assistant must fit the workflow, make relevant information accessible, and respect the people responsible for consequential decisions.
My role
As Lead Software Engineer at Zeon Inc., I led architecture and development for healthcare AI platforms, including Naya. My CV records work translating healthcare professionals' operational requirements into software, integrating agent frameworks, and building APIs and cloud infrastructure.
The intended workflow
Naya was designed for hospital assistance involving clinical knowledge retrieval, scheduling, workflow automation, and operational decision support. This profile describes the engineering scope. It does not establish autonomous clinical decision-making, measured patient benefits, or regulatory certification.
Architecture considerations
The application brings together an assistant interface, retrieved knowledge, tool-connected workflows, and backend services. The case-study diagram is a conceptual explanation of those responsibilities, not a disclosure of confidential infrastructure.
Important decisions
Keep clinical and operational responsibilities explicit. Make evidence accessible alongside an answer. Define human review and escalation for consequential steps. Test knowledge retrieval and workflow behavior separately from a model's fluency.
What the work demonstrates
The project contributes direct healthcare product and agent engineering experience. Detailed deployment evidence, cleared screenshots, and outcome measurements are not publicly presented here. For related services, see healthcare AI engineering.