Privacy First AI Wellness App
This project focused on the end-to-end architecture and documentation baseline for a privacy-first AI health and wellness application. The work covered the structured MVP-1 handoff foundation required to support future development, product alignment, technical implementation, and governance review. The project involved translating the product vision into a clear architecture baseline, defining the MVP boundaries, documenting system requirements, mapping user and service flows, and preparing the technical assets needed by development teams.
The scope included software architecture, data governance, consent-led design, AI boundary definition, database and schema planning, service architecture, API contract preparation, UI/UX screen mapping, and structured implementation guidance. A major focus of the project was ensuring that the platform remained privacy-conscious from the foundation stage, with clear attention to consent, data minimisation, controlled data processing, auditability, and safe use of AI within the wellness context.
The deliverables were organised as a professional handoff package so that business stakeholders, developers, designers, and future implementation teams could understand the product scope, architecture decisions, dependencies, and build direction. The work created a practical bridge between early product strategy and technical execution, reducing ambiguity before development and supporting a more controlled MVP delivery process.
Key Contributions & Strategic Impact
I contributed to the project by developing a structured architecture and documentation foundation that converted a sensitive AI wellness concept into a clear, implementation-ready MVP baseline. The work helped define how privacy, consent, AI services, data architecture, database design, APIs, and UI/UX foundations should connect within one controlled system. The strategic value of the project was in reducing technical uncertainty, improving development readiness, supporting privacy-by-design thinking, and giving the client a clear foundation for the next implementation phase.
- Developed a consolidated MVP-1 architecture and implementation handoff package.
- Defined project boundaries, architectural decisions, system requirements, and use case direction.
- Prepared data governance and privacy-focused architecture documentation.
- Covered consent-led design, event taxonomy, database/schema baseline, and service architecture.
- Supported API planning, UI/UX screen mapping, and development handoff readiness.Maintained confidentiality by avoiding exposure of sensitive business logic, production data, prompts, or private user information.
- Created documentation that could support client review, developer onboarding, and future implementation validation.
Strategic Value Summary
This project delivered a privacy-first technical foundation for an AI wellness product by combining architecture, business analysis, governance, documentation, and implementation planning into one structured handoff baseline. It helped the client move from product concept to development readiness while keeping privacy, consent, data control, and AI boundaries central to the system design.
