Built a internal tool with JetBrains AI?
We'll make it production-ready.
Internal tools don't face the public internet, but they often have access to sensitive business data — customer records, financial data, operational metrics. AI tools build internal dashboards quickly, but the security bar is still high because a compromised internal tool can expose your entire business.
Internal Tool challenges in JetBrains AI apps
Building a internal tool with JetBrains AI is a great start — but these challenges need attention before launch.
Access control
Who can see what? Internal tools need role-based access — finance sees revenue data, support sees customer data, engineering sees system metrics. AI tools build the dashboard but rarely implement granular permissions.
Data sensitivity
Internal tools often connect directly to production databases. A bug that deletes records or a missing auth check that exposes customer PII can have serious legal and business consequences.
Network security
Internal tools should be behind a VPN or protected network, not on the public internet. AI tools deploy to public URLs by default. Proper network configuration prevents external access.
Audit logging
When someone modifies data through an internal tool, you need to know who did what and when. This is essential for debugging, compliance, and accountability.
Data mutations
Internal tools often write to production databases — updating orders, modifying user accounts, issuing refunds. These operations need confirmation dialogs, validation, and audit trails to prevent costly mistakes.
What we check in your JetBrains AI internal tool
Common JetBrains AI issues we fix
Beyond internal tool-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 internal tool 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 internal tool with JetBrains AI?
JetBrains AI is a great starting point for a internal tool. It handles the initial scaffolding well, but internal tools have specific requirements — access control and data sensitivity — that need professional attention before launch.
What issues does JetBrains AI leave in internal tools?
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 internal tool specifically, these issues are compounded by the need for access control.
How do I make my JetBrains AI internal tool production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most JetBrains AI-built internal tools, 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 internal tool?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger internal tool projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your JetBrains AI internal tool production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.