IRVO
A personal performance tracking app that helps you understand how your daily habits affect how you feel and function. Log tasks, energy, focus, and stress. Let the data show you the patterns.
Project landing pageType
Health App
Platform
iOS & Android
Stack
React Native • Expo • Express • Redis
The Problem
- Existing habit trackers count streaks, not why you feel off
- No way to connect daily inputs (sleep, meals, tasks) to performance
- Users sense patterns but have no data to confirm them
- Turning structured self-tracking into actionable insights is hard
- Result: vague feelings, no concrete answers, no way to improve
What We Built
A cross-platform mobile app for structured self-tracking, available on iOS and Android.
- Task + mood logging: energy, focus, and stress throughout the day
- Optional physical symptom tracking
- Pattern detection that surfaces correlations when they repeat
- Async processing via BullMQ + Redis for reliable background analysis
- TypeScript across mobile and backend for a maintainable codebase
Task + Mood Logging
Log planned tasks alongside real-time energy, focus, and stress levels throughout the day.
Pattern Detection
The app identifies meaningful patterns in your data and surfaces them when they repeat.
Physical Symptoms Tracking
Optional symptom logging for users who want to connect physical state with performance.
Async Processing with BullMQ
Background jobs handle data analysis and insight generation without slowing the app down.
Tech Stack
We picked a stack that handles cross-platform mobile well and can process behavioral data reliably in the background.
Cross-platform development targeting both iOS and Android from a single codebase. Expo handles builds and updates cleanly.
Type safety across both the mobile app and the backend. Catches issues early and makes the codebase easier to maintain as it grows.
Backend API for data sync, authentication, and processing user entries. Clean and fast.
Async job queue for reliable background processing. Pattern analysis and insight generation run as background jobs without impacting app performance.
The Result
- Runs on iOS and Android from a single codebase
- Quick daily logging: behavioral picture builds in the background
- Patterns surface automatically, no manual analysis required
- Async pipeline handles growing data without impacting app performance
- TypeScript backend is easy to extend with new insight types
From vague feelings to concrete, data-backed insights about what drives your performance.
Common questions
Answers about self-tracking, performance apps, and the tech behind IRVO.
What is self-tracking and why does it matter for performance?
Self-tracking is the practice of logging data about your own behavior, mood, energy, and habits over time. Most people feel they perform differently on different days but have no data to explain why. Structured self-tracking gives you that data. When patterns repeat, you can see exactly what drives your best and worst days, and adjust accordingly.
How is IRVO different from a habit tracker like Streaks or Habitica?
Habit trackers record whether you did or didn't do something. IRVO goes further by capturing how you felt while doing it. You log your energy, focus, and stress levels alongside your tasks, and the app surfaces correlations over time. The goal isn't a streak, it's understanding what actually affects your performance.
Why use React Native and Expo instead of building native iOS and Android apps separately?
A shared React Native codebase means features and fixes ship to both platforms at once, with one team. Expo simplifies the build and release pipeline significantly. For most apps, the performance difference versus fully native code is not noticeable to users, and the development speed advantage is real.
What is BullMQ and why use it for a mobile app backend?
BullMQ is a job queue built on Redis. For IRVO, background jobs handle the async work of analyzing user data and surfacing patterns without blocking the API or the app. When a user logs an entry, the app responds instantly. The pattern analysis runs in the background on its own schedule.
How does the app protect user data?
User data is stored server-side with authentication required to access it. The app uses token-based auth flows so only you can see your entries. Personal performance data is sensitive, and the architecture reflects that.
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