App Showcase: Pep AI
App info
- App name: Pep AI: Peptide GLP-1 Tracker
- Onboarding steps: 27
- Paywall type: Free Trial - Hard Paywall
- Monthly installs: 35,000
- Monthly revenue: $65,000
- App category: health and fitness
What it does
Pep AI is a specialized tracking and management app designed for individuals using peptide protocols. It goes beyond simple logging, offering a suite of tools to manage complex dosing schedules, calculate reconstitution math, track nutrition via AI-powered scanning, log workouts, and monitor various wellness metrics like sleep and side effects. At its core, the app aims to be an all-in-one dashboard for a user's entire health protocol, centralizing data that would otherwise be scattered across spreadsheets, notes, and memory.
Where it shines
Pep AI excels in its deep understanding of its niche audience. The reconstitution calculator (07:01) is a standout feature, turning a complex and error-prone task into a simple, visual process. It even warns users if their syringe is too small for the calculated dose (07:24), adding a thoughtful layer of safety. Another impressive moment is the integration of the AI chatbot, PepBot. At 16:19, the user asks the bot to show their current stack, and it immediately pulls live, user-specific data into the chat, demonstrating its power as a personal data assistant rather than a generic help tool.
UX highlights
- Comprehensive Onboarding: The app uses a detailed, multi-stage quiz (00:11-00:29) to deeply personalize the user experience from the very beginning.
- Gamified Progression: A system of XP and badges (06:44) provides constant positive reinforcement for tracking activities, encouraging long-term user engagement.
- Centralized Actions: The floating action button (FAB) at 02:45 provides quick access to all major logging functions (protocol, dose, weight, meal scan), serving as a consistent entry point for all data input.
- Visual Data Representation: The app consistently uses charts and visual aids, like the injection site map (06:06) and the compound level graph on the dashboard (05:42), to make complex data easy to understand at a glance.
- AI-Powered Nutrition Logging: The meal scanning (09:14) and barcode scanning (10:48) features significantly reduce the friction of calorie and macro tracking.
- Contextual AI Prompts: The PepBot suggests relevant questions based on the user's data (16:19), guiding them to discover the feature's capabilities organically.
Monetization & growth
The app employs a hard paywall with a free trial, which users encounter at the end of the extensive onboarding process (02:24). By this point, the user has invested significant time in the personalization quiz, creating a sense of sunk cost that may increase the likelihood of starting a trial. The paywall itself highlights a significant discount for the annual plan (02:28) and breaks down the cost to a monthly figure, making it seem more affordable. A dedicated screen earlier in the onboarding (00:30) cleverly reframes the app's cost as a small price to pay to protect the much larger investment in peptides.
Who it’s for
Pep AI is clearly built for the biohacking and self-optimization community, specifically those who use peptides or other complex supplement protocols. The level of detail in tracking, from injection sites to reconstitution math, caters to a data-driven user who wants granular control and oversight of their health regimen. It's for someone who is already committed to a protocol and is looking for a powerful tool to manage it more effectively than a simple notebook or spreadsheet.
Notes & opportunities
While the app is feature-rich, the sheer length and depth of the onboarding could be a point of friction for some users, potentially leading to drop-off before they even see the paywall. The main dashboard (04:40), while informative, is quite dense. There might be an opportunity to allow users to customize this view to prioritize the metrics most important to them. Finally, the AI chatbot is powerful but could be more proactive, perhaps offering unsolicited insights or summaries based on detected trends in the user's data.