Built a mobile app with Pythagora?
We'll make it production-ready.
Mobile apps live on someone's phone — the most personal device they own. Users expect instant load times, smooth animations, offline support, and zero crashes. AI tools can scaffold a mobile UI fast, but the gap between a working prototype and an app that survives App Store review, handles spotty network connections, and doesn't drain the battery is where most AI-built mobile apps fail.
Mobile App challenges in Pythagora apps
Building a mobile app with Pythagora is a great start — but these challenges need attention before launch.
App Store and Play Store approval
Apple and Google have strict review guidelines covering permissions, privacy policies, content ratings, and technical requirements. AI-generated apps frequently get rejected for missing privacy disclosures, requesting unnecessary permissions, or failing to handle edge cases that reviewers specifically test for.
Offline functionality and network handling
Mobile users lose connectivity constantly — in elevators, on subways, in rural areas. AI tools build apps that assume a constant internet connection. Without offline caching, request queuing, and graceful degradation, your app shows blank screens or crashes when the network drops.
Device fragmentation and screen sizes
Your app needs to work on hundreds of different devices — from small Android phones to large tablets, with notches, punch-holes, and different aspect ratios. AI-generated layouts often break on devices other than the one used during development.
Performance and battery drain
Mobile users notice when an app drains their battery or feels sluggish. Unoptimized rendering, excessive API polling, background processes that never stop, and memory leaks are common in AI-generated mobile code. These issues cause uninstalls faster than missing features.
Push notifications
Setting up push notifications requires server-side infrastructure, device token management, platform-specific configuration (APNs for iOS, FCM for Android), and proper permission handling. AI tools generate a basic notification call but skip the entire delivery pipeline.
Secure local storage
Mobile apps store auth tokens, user preferences, and cached data on-device. AI tools often use insecure storage methods (plain shared preferences or AsyncStorage) instead of encrypted keystores. Anyone with physical access to the device could extract sensitive data.
What we check in your Pythagora mobile app
Common Pythagora issues we fix
Beyond mobile app-specific issues, these are Pythagora patterns we commonly fix.
Weak input validation
Pythagora validates inputs at the UI level but often skips server-side validation, allowing malicious requests to bypass frontend checks.
Insecure session management
Sessions and tokens are sometimes stored insecurely or don't expire properly, allowing unauthorized access to persist.
Step-by-step accumulation of bugs
Each development step is reviewed individually, but the interaction between steps can introduce bugs that aren't visible in isolation.
Redundant code from iterations
Pythagora's iterative approach sometimes leaves behind old code paths, unused functions, and deprecated approaches from earlier steps.
Start with a self-serve audit
Get a professional review of your Pythagora mobile app at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
Frequently asked questions
Can I build a mobile app with Pythagora?
Pythagora is a great starting point for a mobile app. It handles the initial scaffolding well, but mobile apps have specific requirements — app store and play store approval and offline functionality and network handling — that need professional attention before launch.
What issues does Pythagora leave in mobile apps?
Common issues include: weak input validation, insecure session management, step-by-step accumulation of bugs. For a mobile app specifically, these issues are compounded by the need for app store and play store approval.
How do I make my Pythagora mobile app production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Pythagora-built mobile apps, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.
How much does it cost to fix a Pythagora-built mobile app?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger mobile app projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your Pythagora mobile app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.