Decoding Farabi: How This AI Math Solver Generates $45K Monthly

Scan & Solve: The Instant Gratification Hook

The moment you open Farabi, the core value proposition hits you immediately: "Scan & Solve." A slick animation showcases the app scanning a complex math problem, promising step-by-step solutions with just a snap. This isn't about vague educational goals; it's about immediate problem-solving power. By focusing on the action (scan) and the outcome (solve), Farabi instantly communicates its utility, targeting students under pressure. No fluff, just function.

Navigating the Permission Gauntlet: Onboarding Insights

Farabi employs a multi-step onboarding process (around 5 key interactions) designed to secure permissions and nudge users towards conversion, but it's a delicate dance.

First, it tackles the sensitive App Tracking Transparency (ATT) prompt. Wisely, it uses a pre-prompt explaining the why – tailoring explanations to the user's level, promising no ads, no nonsense. Framing tracking as a benefit, not just data collection, is a classic tactic to boost opt-ins. 👍

Next comes the notification request, cleverly linked to "homework deadlines." This ties the permission to a tangible student pain point, making it feel less like an interruption and more like a helpful feature. ⏰

Interestingly, the app weaves in a rating request ("Can you show us some love?") before hitting the user with the paywall. This attempts to capture positive sentiment early, potentially boosting App Store visibility, even if the user hasn't fully experienced the core solving feature yet.

Finally, access to the Photo Library is requested later, directly tied to the functionality of uploading existing problem images. Delaying this permission until contextually relevant reduces initial friction.

The Soft Paywall Gambit: Converting Students with a Free Trial

Before users get deep into solving problems, Farabi presents its subscription offer. It’s positioned as the key to unlocking the full potential: unlimited scans, loading solutions (implying faster or more detailed ones?), and an ad-free experience.

The app pushes a "Popular" 3-Day Free Trial, converting to $5.99/week, alongside an Annual option ($39.99/year, broken down to $0.77/wk). This uses common pricing psychology: highlight the trial, anchor with a weekly price but offer a significantly cheaper annual alternative. 💰 The trial confirmation screen reassures the user "You're all set," aiming to reduce immediate buyer's remorse. While it's a "soft" paywall (you can sometimes bypass initial subscription prompts in such apps), the flow strongly encourages starting the trial early.

From Scan to Solution: Delivering the AI Math Magic ✨

Once past the initial setup, Farabi offers two main paths:

1. Camera Scan: The primary method. A clear viewfinder with guides helps users capture handwritten or printed problems. The transition from scan to loading screen to solution is designed to feel seamless.
2. Calculator Input: For users who prefer typing, a robust calculator interface allows entering expressions, including trigonometric functions (sin, cos), logs, and standard operators.

The app demonstrates its capability with various examples, from algebraic expressions ((11+x)/x^3 + 2x(5-x)) to potentially ambiguous arithmetic (6 ÷ 2(1+2)), providing step-by-step breakdowns. This detailed guidance is the core product promise. The inclusion of a "Recent Solutions" history adds convenience, allowing users to quickly revisit past problems. 📚

Final Thoughts

Farabi's $45k MRR isn't accidental. It stems from a combination of a clear value proposition, a strategically ordered onboarding flow that tackles permissions head-on, and an early, persuasive paywall emphasizing a free trial. While the core AI math solving is crucial, the app's structure—how it guides users, asks for permissions, and presents the subscription—plays a massive role in its financial success. Understanding these intricate user journeys and conversion tactics is essential for any app developer aiming for sustainable growth in today's competitive market. Analyzing these flows reveals the repeatable patterns behind high-performing apps.