Built a api / backend service with JetBrains AI?
We'll make it production-ready.
APIs are the backbone of modern applications — mobile apps, SPAs, integrations, and other services all depend on your API being secure, fast, and reliable. AI tools can scaffold API endpoints quickly, but production APIs need authentication, input validation, rate limiting, documentation, and monitoring that AI tools consistently skip.
API / Backend Service challenges in JetBrains AI apps
Building a api / backend service with JetBrains AI is a great start — but these challenges need attention before launch.
Authentication and API keys
Every endpoint needs to verify the caller's identity. AI tools create endpoints without auth, or with auth that's easy to bypass. You need token-based auth, API key management, and proper session handling.
Input validation
Every parameter, request body, and header value must be validated before use. AI-generated APIs trust client data, which leads to injection attacks, data corruption, and crashes from unexpected input.
Rate limiting and abuse prevention
Without rate limits, anyone can hammer your API — brute-forcing passwords, scraping data, or running up your infrastructure costs. Rate limiting must be per-user and per-endpoint.
Error handling and status codes
APIs should return appropriate HTTP status codes (400 for bad input, 401 for unauthorized, 404 for not found, 500 for server errors) with helpful error messages. AI tools often return 200 for everything or expose internal error details.
Documentation
APIs without documentation are unusable. Auto-generated OpenAPI/Swagger docs from your code are the minimum. AI tools rarely set up API documentation.
Versioning and backwards compatibility
Once other services depend on your API, you can't change it freely. You need a versioning strategy from the start so you can evolve the API without breaking existing clients.
What we check in your JetBrains AI api / backend service
Common JetBrains AI issues we fix
Beyond api / backend service-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 api / backend service 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 api / backend service with JetBrains AI?
JetBrains AI is a great starting point for a api / backend service. It handles the initial scaffolding well, but api / backend services have specific requirements — authentication and api keys and input validation — that need professional attention before launch.
What issues does JetBrains AI leave in api / backend services?
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 api / backend service specifically, these issues are compounded by the need for authentication and api keys.
How do I make my JetBrains AI api / backend service production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most JetBrains AI-built api / backend services, 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 api / backend service?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger api / backend service projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your JetBrains AI api / backend service production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.