Volo

Overview

Volo is a mobile-first gym companion designed to remove the guesswork from training. It helps users build structured routines, follow exercise tutorials with muscle group targeting, track nutritional intake, and visualize their weight progress over time — all in one streamlined app.

The gym shouldn’t require a personal trainer to navigate. Volo puts expert-level routine building in everyone’s pocket.

The Problem

Most gym-goers face a common set of frustrations:

No structure — Walking into the gym without a plan leads to random, ineffective workouts
Information overload — Hundreds of exercises exist, but which ones target what you need?
Fragmented tools — Separate apps for routines, nutrition, and progress tracking creates friction
No progression — Without tracking weight over time, it’s impossible to know if you’re improving

I wanted a single app that addresses all four problems with a clean, distraction-free mobile experience.

Goals

Build a cross-platform mobile app with Flutter for iOS and Android
Design a routine builder organized by body sections (push, pull, legs, etc.)
Include exercise tutorials with animated muscle group highlighting
Implement a nutrition tracker with macro breakdowns
Create a weight progress log with visual trend charts
Deliver a fast, offline-capable experience for gym environments

Tech Stack

Technology	Role
Flutter	Cross-platform mobile UI framework
Dart	Application logic and state management
FastAPI	Python backend for API and data processing
PostgreSQL	Database for users, routines, and exercise library
SQLite	Local device storage for offline support
Matplotlib	Server-side progress chart generation

Architecture

┌──────────────────┐     ┌──────────────────┐     ┌──────────────┐
│   Flutter App    │────▶│   FastAPI        │────▶│  PostgreSQL  │
│   (iOS/Android)  │◀────│   /api/v1/*      │◀────│              │
└────────┬─────────┘     └──────────────────┘     └──────────────┘
         │
         ▼
┌──────────────────┐
│   SQLite Local   │
│   (Offline Cache)│
└──────────────────┘

The app follows an offline-first strategy — routines and exercise data sync to local SQLite on first load. Users can complete full workouts without connectivity; data syncs when connection resumes.

Key Features

Routine Builder by Body Section

Users create routines organized by muscle group targeting:

Section	Example Exercises
Push	Bench press, overhead press, tricep dips
Pull	Deadlift, barbell rows, pull-ups
Legs	Squats, leg press, Romanian deadlifts
Core	Planks, cable crunches, hanging leg raises
Arms	Bicep curls, skull crushers, hammer curls

Each routine supports:

Custom exercise ordering
Set/rep/weight configuration per exercise
Rest timer between sets
Notes per exercise

Exercise Library with Tutorials

A comprehensive database of 200+ exercises, each featuring:

Step-by-step text instructions
Animated muscle group diagrams showing primary and secondary muscles
Common mistakes to avoid
Suggested alternatives for equipment limitations

Nutrition Tracker

Daily food logging with:

Macro breakdown (protein, carbs, fats)
Calorie tracking against daily targets
Meal categorization (breakfast, lunch, dinner, snacks)
Quick-add for frequently logged foods
Weekly/monthly nutrition summaries

Weight Progress Tracking

A dedicated section for recording body weight over time:

Daily weigh-in logging
Trend line chart smoothing daily fluctuations
Goal setting with projected timeline
Weekly averages to reduce noise from water weight
Progress photos timeline (optional)

Active Workout Mode

When a routine is started, the app enters a focused workout mode:

┌─────────────────────────────┐
│  Bench Press          3/4   │
│  ─────────────────────────  │
│  Set 1: 80kg × 10 ✓        │
│  Set 2: 85kg × 8  ✓        │
│  Set 3: 85kg × _  ← active │
│  Set 4: 80kg × 10           │
│  ─────────────────────────  │
│  Rest: 1:23 remaining       │
│  [Complete Set]             │
└─────────────────────────────┘

Challenges & Solutions

Challenge: Offline-First Data Sync

Gym environments have notoriously poor connectivity. The app needed to work fully offline, then sync without data loss or conflicts.

Solution: Implemented a last-write-wins sync strategy with timestamp-based conflict resolution. Local SQLite stores all workout data with a synced flag. A background isolate attempts sync every 30 seconds when connectivity is available. Critical operations (creating routines, logging sets) never depend on network.

Challenge: Muscle Group Visualization

Showing which muscles an exercise targets required clear, consistent anatomical illustrations that respond to the selected exercise.

Solution: Created a set of SVG body maps with individually addressable muscle groups. Each exercise maps to primary (highlighted) and secondary (dimmed) muscle IDs. Flutter’s SVG rendering paints the relevant groups, producing a clear visual without requiring dozens of individual illustrations.

Challenge: Flutter State Management at Scale

As features grew (routines + nutrition + progress + workout mode), managing state across the app became complex.

Solution: Adopted Riverpod for dependency injection and state management. Each feature domain has its own providers with clear boundaries. The active workout state uses a StateNotifier that persists to local storage, surviving app kills mid-workout.

Challenge: Rest Timer Accuracy

A rest timer that drifts or stops when the app is backgrounded is useless in a real gym scenario.

Solution: Used Flutter’s Isolate for timer computation and local notifications for background alerts. The timer calculates remaining time from a stored start timestamp rather than counting down, ensuring accuracy regardless of app lifecycle events.

Data Model (Simplified)

class Routine(BaseModel):
    id: UUID
    user_id: UUID
    name: str
    section: str  # "push" | "pull" | "legs" | "core" | "arms"
    exercises: list[RoutineExercise]

class RoutineExercise(BaseModel):
    exercise_id: UUID
    order: int
    sets: int
    target_reps: int
    target_weight: float | None
    rest_seconds: int

class WorkoutLog(BaseModel):
    id: UUID
    routine_id: UUID
    started_at: datetime
    completed_at: datetime | None
    sets: list[SetLog]

class SetLog(BaseModel):
    exercise_id: UUID
    set_number: int
    weight: float
    reps: int
    completed_at: datetime

Performance

Metric	Target	Achieved
App cold start	< 2s	1.4s
Routine load (offline)	< 100ms	45ms
Sync cycle	< 3s	~1.8s
Exercise search	< 200ms	90ms
Frame rate (animations)	60fps	60fps

Lessons Learned

Offline-first changes everything — Designing for offline from day one is vastly easier than retrofitting. It forces clean separation between local state and remote truth.
Flutter + FastAPI is a great combo — Flutter’s hot reload speed and FastAPI’s auto-generated OpenAPI docs make the development loop extremely fast.
Gym UX needs big touch targets — Sweaty hands and quick glances between sets mean buttons need to be large, contrast needs to be high, and interactions need to be forgiving.
SVG muscle maps scale beautifully — One set of vector assets works across all screen sizes and densities, with programmatic coloring replacing dozens of static images.
Rest timers are deceptively complex — Background execution, notification permissions, and timer accuracy across OS lifecycle events required more engineering than expected.

What’s Next

Social features (share routines, follow friends’ progress)
AI-powered routine suggestions based on goals and history
Apple Watch / Wear OS companion for active workout tracking
Exercise form check via device camera and pose estimation
Integration with smart scales for automatic weight logging