Alocare — Engineering Case Study

Alocare is an employer product in the healthcare domain. It is a Personal Health Intelligence system that helps people make sense of laboratory and medical checkup reports. Herry contributed as a Full Stack Engineer across backend, AI backend, mobile applications and frontend web, with applied AI as part of the professional role. Source code and internal implementation details remain private.

Project context

Alocare is an employer product where the name may be used publicly and technical discussion is allowed while source code remains private. The product is currently in private pilot, with pilot builds available for Android and iOS and a web portal, alongside longer-term strategic direction. This case study focuses on capabilities that are available today rather than future vision.

Public product context

The product focuses on transforming uploaded laboratory and medical checkup reports into structured, reviewable information. Core areas include report upload, structured extraction of information from reports, lab and checkup interpretation, longitudinal health tracking, personalized health context, AI-assisted insights, and a review-focused workflow where users and clinicians review results and decide next actions.

This is complemented by a consumer application and web experience for managing health history over time. The workflow conceptually moves from report upload to AI extraction and structured information, to health and clinical intelligence, to human review and action.

Role and engineering scope

Herry’s role is Full Stack Engineer from 2026 to present, working across backend, AI backend, mobile applications, frontend web and applied AI.

Platforms include Android, backend, web and AI backend. Capabilities include Full Stack Engineering, Mobile Engineering, Backend Engineering, Web Engineering and Applied AI. Responsibilities have included backend development, Android development, frontend development and AI integration. The work covered multiple surfaces as part of the same product effort rather than isolated projects.

Full-stack contribution

Herry contributed across backend, Android, frontend web and applied-AI integration. The product spans several engineering surfaces, and the contribution reflects that breadth — supporting backend services, mobile experiences and web interfaces that work together as one health product.

Source code and internal details remain private, so the focus here is on the nature and breadth of the contribution rather than implementation specifics.

Applied AI within the professional role

Applied AI was part of the broader professional work. The work is applied-AI engineering focused on evaluating and integrating AI capabilities into product workflows rather than foundational-model research.

Established areas include model selection and evaluation, Ollama deployment, prompt engineering, OCR, backend and API integration, and frontend and mobile AI integration. OCR is part of that applied-AI experience, and its relationship to Alocare’s report ingestion and structured extraction is described here only at the capability level, without inferring a specific pipeline.

Backend, mobile, and web integration

Alocare spans report handling and longitudinal health-history workflows across its product interfaces.

Herry’s established responsibilities include backend development, Android development, frontend development and AI integration.

Private-source and confidentiality boundary

Alocare is a private-source product. No private repository names, source code, credentials, patient information or proprietary internal details are disclosed.

Engineering takeaways

Working across backend, AI backend, mobile and web within the same product reinforced the importance of connecting product understanding, integration and applied-AI engineering across multiple surfaces.

This case study complements the Product Overview, which describes what the product is, while this narrative describes the engineering contribution around it.