Built a mobile app with JetBrains AI?
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 JetBrains AI apps
Building a mobile app with JetBrains AI 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 JetBrains AI mobile app
Common JetBrains AI issues we fix
Beyond mobile app-specific issues, these are JetBrains AI patterns we commonly fix.
Over-engineered enterprise patterns generated for simple startup use cases
JetBrains AI is trained on enterprise Java and Kotlin patterns, so it tends to generate verbose factory patterns, abstract base classes, and interface hierarchies for problems that could be solved with a simple function in a startup context.
Generated Spring Boot code includes unnecessary security exposure in default configurations
Spring Boot applications generated by JetBrains AI may include actuator endpoints, management ports, or H2 console access enabled in configurations that should be disabled or secured before production deployment.
Verbose boilerplate code increases bundle size and maintenance overhead
Java code generation in particular produces verbose getter/setter patterns, checked exception hierarchies, and XML configuration that modern Kotlin or Lombok-based approaches would handle with a fraction of the code.
Generated unit tests use JUnit 4 patterns in projects that have moved to JUnit 5
JetBrains AI sometimes generates JUnit 4 annotations (@Test from org.junit, @Before, @After) in projects configured for JUnit 5, causing compilation errors and requiring annotation migration.
Start with a self-serve audit
Get a professional review of your JetBrains AI 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 JetBrains AI?
JetBrains AI 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 JetBrains AI leave in mobile apps?
Common issues include: over-engineered enterprise patterns generated for simple startup use cases, generated spring boot code includes unnecessary security exposure in default configurations, verbose boilerplate code increases bundle size and maintenance overhead. For a mobile app specifically, these issues are compounded by the need for app store and play store approval.
How do I make my JetBrains AI mobile app production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most JetBrains AI-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 JetBrains AI-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 JetBrains AI mobile app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.