Built a developer tool with JetBrains AI?
We'll make it production-ready.
Developer tools face the most technically demanding audience there is — other developers. They'll inspect your source code, stress-test your API, and publicly criticize performance issues on Twitter. AI tools can scaffold a CLI, dashboard, or API wrapper quickly, but developer tools need exceptional error messages, comprehensive documentation, and rock-solid reliability because your users know exactly how software should work.
Developer Tool challenges in JetBrains AI apps
Building a developer tool with JetBrains AI is a great start — but these challenges need attention before launch.
Error messages and developer experience
Developers expect error messages that tell them exactly what went wrong, why, and how to fix it. AI-generated tools return generic 'Something went wrong' messages or raw stack traces. Good DX means every error is actionable and every edge case has a helpful response.
API design and consistency
Developer tools live or die by their API surface — whether REST endpoints, CLI arguments, or SDK methods. Naming must be consistent, behavior must be predictable, and breaking changes must be versioned. AI tools generate functional but inconsistent APIs that frustrate developers.
Documentation and examples
Developers won't use your tool if they can't figure it out quickly. You need API reference docs, getting-started guides, code examples in multiple languages, and a changelog. AI tools build the tool but not the documentation ecosystem around it.
Performance and latency
Developer tools are often in the critical path of other developers' workflows — slow API responses, laggy CLIs, or unresponsive dashboards directly waste their time. Every millisecond matters. AI-generated tools have unoptimized database queries and no caching.
Authentication and API key management
Developer tools need API key generation, key rotation, scoped permissions per key, usage tracking, and rate limiting. AI tools implement a single hardcoded API key or basic bearer tokens without any key lifecycle management.
Webhook and integration reliability
If your tool sends webhooks or integrates with other services, deliveries must be reliable — with retry logic, delivery logging, signature verification, and a way for users to test and debug integrations. AI tools fire-and-forget webhooks with no reliability guarantees.
What we check in your JetBrains AI developer tool
Common JetBrains AI issues we fix
Beyond developer 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 developer 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 developer tool with JetBrains AI?
JetBrains AI is a great starting point for a developer tool. It handles the initial scaffolding well, but developer tools have specific requirements — error messages and developer experience and api design and consistency — that need professional attention before launch.
What issues does JetBrains AI leave in developer 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 developer tool specifically, these issues are compounded by the need for error messages and developer experience.
How do I make my JetBrains AI developer tool production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most JetBrains AI-built developer 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 developer 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 developer tool projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your JetBrains AI developer tool production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.