Case Study
AINA (AI Nutrition Assistant)
Mobile AI assistant with a clear data flow from capture to inference to actionable results.
Overview
Problem
Users need nutrition insights that feel immediate and usable, not buried in spreadsheets.
Solution
A mobile app backed by a service boundary for inference and storage, with an explicit model lifecycle and telemetry to iterate safely.
Results
- Fast UX for capture and feedback
- Model integration isolated behind a service boundary
- Operational visibility for iteration
Implementation Notes
- Kept business logic out of UI components; inference called through a single integration surface.
- Explicit handling of failure modes: network loss, partial results, retry-safe requests.
- Privacy/security notes should be documented before public demos.
Architecture
Mobile + Inference Service
Diagram placeholder (16:9)
Key components
- Mobile app
- Inference service
- Storage
- Telemetry + logs
- Model lifecycle
Stack
FlutterPythonFastAPICV/MLStorageTelemetry