Orchid

Tech Stack
Overview
Problem Solved
Solution
Workflow
- Onboarding: Complete a confidential health questionnaire with adaptive follow-up questions
- Analysis: The app processes responses through a secure recommendation engine
- Insights: Receive personalized health predictions, fertility windows, and wellness tips
- Tracking: Log daily symptoms, moods, and activities for increasingly accurate insights
- Consultation: Book appointments with verified health professionals directly from the app
- Reminders: Get daily wellness tips and cycle notifications via push alerts
Technical Highlights
- Mobile: React Native with TypeScript for iOS and Android
- Backend: Firebase (Auth, Firestore, Cloud Functions, Cloud Messaging)
- Security: End-to-end encryption for all health data with user-controlled deletion
- Analytics: Anonymous engagement tracking for app improvement without compromising privacy
- Compliance: Clear medical disclaimers and professional consultation pathways
Key Takeaways
Features
Personalized health questionnaires
Pregnancy prediction and fertility tracking
Health recommendations based on responses
Reproductive health insights and guidance
Daily wellness reminders and tips
Secure and private health data storage
Consultation booking with health professionals
Architecture
Orchid is a React Native application with a Firebase backend. Firestore stores encrypted user responses and health profiles. Firebase Authentication provides secure access, and Cloud Functions process questionnaire responses to generate personalized recommendations. Push notifications deliver daily wellness reminders.
API & System Flow
User authenticates and completes an onboarding questionnaire → Responses are encrypted and stored in Firestore → Cloud Functions analyze patterns and generate personalized recommendations → Results are displayed with confidence scores and medical disclaimers → Users can book consultations or receive daily tips via push notifications.
Challenges & Solutions
Created a progressive disclosure pattern where questions adapt based on previous answers, maintaining relevance while respecting user comfort levels.
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