Built a mvp / prototype with JetBrains AI?
We'll make it production-ready.
You built a prototype with an AI tool and it works — on your machine, with your test data, for the happy path. Now you need real users to validate your idea. The goal isn't perfection — it's getting to a state where real people can use it safely and reliably. That means fixing the critical gaps without over-engineering.
MVP / Prototype challenges in JetBrains AI apps
Building a mvp / prototype with JetBrains AI is a great start — but these challenges need attention before launch.
Identifying what actually needs fixing
Not everything needs to be production-grade for an MVP. The challenge is knowing which shortcuts are acceptable (visual polish, advanced features) and which aren't (security, data loss, broken core flows). Our audit prioritizes fixes by impact.
Security minimum
Even an MVP handles user data. You need authentication that works, data isolated per user, no exposed secrets, and HTTPS. You don't need enterprise-grade security, but you need the basics done right.
Deployment
Your app runs locally but deploying it involves environment variables, domain configuration, SSL, and build optimization. First-time deployment is where most AI prototypes hit unexpected issues.
Error handling
When something goes wrong (and it will), your users should see a helpful message — not a white screen or a raw error. Basic error handling is the difference between 'this app is buggy' and 'something went wrong, please try again.'
Core flow reliability
Your main user flow (signup → core action → outcome) must work every time. Edge cases in secondary flows can wait, but the core path needs to be solid for your MVP to generate valid learnings.
What we check in your JetBrains AI mvp / prototype
Common JetBrains AI issues we fix
Beyond mvp / prototype-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 mvp / prototype 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 mvp / prototype with JetBrains AI?
JetBrains AI is a great starting point for a mvp / prototype. It handles the initial scaffolding well, but mvp / prototype apps have specific requirements — identifying what actually needs fixing and security minimum — that need professional attention before launch.
What issues does JetBrains AI leave in mvp / prototype 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 mvp / prototype specifically, these issues are compounded by the need for identifying what actually needs fixing.
How do I make my JetBrains AI mvp / prototype production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most JetBrains AI-built mvp / prototype 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 mvp / prototype?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger mvp / prototype projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your JetBrains AI mvp / prototype production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.