AIRIS Health Screening Platform

Accessible health screening in resource-constrained settings calls for low-cost, portable hardware paired with reliable AI processing, and AIRIS set out to bring edge-captured health data into a centralized, validated AI pipeline. The system integrates ESP32-CAM edge devices with a central AI processing unit, where captured data flows through a containerized inference service and MLOps pipelines make sure every model is validated before it ever reaches production.

Technically, we containerized the processing stack with Docker for portability across deployment environments and built data-engineering pipelines to clean and route the edge-captured inputs, while the MLOps layer handled validation and reproducibility of model updates. That foundation carried the project to the National Finals of HackFest at Universitas Ciputra 2025, validating both the technical approach and the product concept in front of a national judging panel.

Coordinating a five-person team across hardware, ML, and infrastructure was its own lesson: clear interface contracts between subsystems mattered as much as the code inside each one. When the camera, the model, and the pipeline each knew exactly what to expect from the others, the whole system came together far more smoothly than any individual piece would suggest.