App Showcase: Gravl
App info
- App name: Gravl: AI Personal Trainer
- Onboarding steps: 29
- Paywall type: No Free Trial - Soft Paywall
- Monthly installs: 15,000
- Monthly revenue: $350,000
- App category: health and fitness
What it does
Gravl is a fitness app that acts as an AI personal trainer. It builds personalized workout plans based on a user's goals, experience, and available equipment. The app's core function is to guide users through workouts, track their performance set-by-set, and use that data to intelligently adjust future sessions for progressive overload. It also includes features for tracking body measurements, visualizing progress, and earning achievements through a gamified leveling system.
Where it shines
Gravl stands out in its ability to make data feel both personal and motivational. The onboarding process, while lengthy, culminates in a satisfying "Your workout plan is ready" screen (03:28) that makes the initial time investment feel worthwhile. The post-workout experience is another high point. Instead of just showing a list of completed exercises, the app generates an "AI summary" (10:17) that translates raw numbers into a narrative of achievement, highlighting personal records and effort level. Finally, the introduction of "Gravl Levels" (13:37) adds a compelling long-term engagement loop, turning workout consistency into a game of earning XP and unlocking new badges.
UX highlights
- Purposeful Onboarding: The app's initial quiz (starting at 00:38) is extensive, but every question clearly ties back to creating a better workout plan, which justifies the data collection.
- Anticipation Building: A loading screen at 03:21 explicitly shows the AI "Analyzing your goals," "Creating gym profile," and "Creating your program," which builds excitement for the final plan.
- Granular Workout Control: Within a workout, users have extensive control. They can add dropsets (07:42), create supersets (08:19), or replace exercises (08:26) on the fly, offering flexibility.
- Clear Visual Feedback: Logging a set provides immediate visual feedback by turning the row green (08:04). This small interaction provides a satisfying sense of completion for each step of the workout.
- Data Visualization: The app uses clear charts and graphs to visualize trends in workout volume, energy, and sets (14:05), making it easy to see progress at a glance.
- Gamification: The app features a robust gamification system with badges for milestones like "First Dropset" (15:41) and an XP-based leveling system to encourage long-term adherence.
Monetization & growth
Monetization is introduced after the user's personalized plan has been generated but before they can view it. At 03:39, a paywall appears, presenting a yearly and monthly subscription option. The app frames this as unlocking "unlimited access" and highlights key premium features. After the user dismisses this initial paywall, a one-time discount offer for 25% off is presented at 04:37, a classic strategy to capture users who were hesitant at the first price point. The app also heavily features social components, such as a friend feed and the ability to share workouts (12:55), to encourage viral growth.
Who it’s for
Gravl is clearly designed for intermediate to advanced gym-goers who are serious about strength training and progressive overload. The detailed exercise logging, advanced options like dropsets and supersets, and focus on metrics like 1RM (one-rep max) cater to individuals who are already comfortable in a gym setting. The AI component is for those who want to offload the mental work of planning their next session while still maintaining a high degree of control and tracking their data meticulously.
Notes & opportunities
The app experience is dense with features, which can be overwhelming initially. The onboarding tour after the first workout is helpful (05:13), but some advanced features like editing rep progression types (06:48) are buried and may be missed by users. The app could benefit from more contextual tooltips or a guided exploration of these advanced settings. Additionally, the initial workout generation led to a notification about "not enough exercises for adductors" (05:12), which is a slight crack in the AI's otherwise seamless presentation; a smoother fallback could be designed for such edge cases.