Built with JetBrains AI?
Let's make sure it's production-ready.
An AI assistant deeply integrated into the JetBrains IDE suite including IntelliJ IDEA, WebStorm, and PyCharm, generating code that leverages JetBrains' deep language understanding and IDE features. We help non-technical founders identify and fix the issues AI tools leave behind.
Common issues we find in JetBrains AI code
These are real problems we see in JetBrains AI projects during our audits — not hypotheticals.
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.
N+1 query patterns in generated JPA and Hibernate repository code
Generated Spring Data JPA code frequently has N+1 query problems — fetching a list of entities and then accessing a lazy-loaded relationship in a loop, which generates one database query per entity rather than a join.
Generated Kubernetes and Docker deployment files target development not production
When JetBrains AI generates deployment configurations, they often omit resource limits, readiness/liveness probes, and security contexts that are required for production Kubernetes deployments.
PHP code generated without modern PSR standards or type declarations
Generated PHP code may lack strict type declarations, PSR-4 autoloading compliance, or modern PHP 8.x features, producing code that is valid but does not match current PHP best practices.
IDE template patterns generate thread-unsafe singleton implementations
Java singleton patterns generated by JetBrains AI sometimes use the classic lazy initialization pattern without double-checked locking or enum-based implementation, producing thread-unsafe singletons in concurrent applications.
How we can help with your JetBrains AI project
From security reviews to deployment, we cover everything you need to go from prototype to production.
Security Review
Deep security analysis and hardening
Fix Bugs
Resolve issues and unexpected behavior
Deploy & Ship
Get your JetBrains AI app to production
Refactor Code
Clean up AI-generated or legacy code
Performance
Make your JetBrains AI app faster and more efficient
Add Features
New functionality, integrations, capabilities
Testing
Add tests and improve coverage
Infrastructure
Set up and manage your JetBrains AI backend
Start with a self-serve audit
Get a professional review of your JetBrains AI project at a fixed price. Results reviewed by experienced engineers.
Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 checks
- SSL/TLS analysis
- Security headers
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerabilities
- Code quality review
- Dependency audit
- AI pattern 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.
How it works
Tell us about your app
Share your project details and what you need help with.
Get a clear plan
We respond in 24 hours with scope, timeline, and cost.
Launch with confidence
We fix what needs fixing and stick around to help.
Frequently asked questions
Does JetBrains AI work offline or does it require a cloud connection?
JetBrains AI requires a cloud connection to JetBrains' AI service, which is powered by multiple underlying models. There is no fully offline mode. For teams with strict data residency requirements, review JetBrains' data processing documentation — they offer enterprise agreements with data handling guarantees for sensitive codebases.
Is JetBrains AI included in my existing JetBrains subscription?
JetBrains AI is available as an add-on to existing JetBrains subscriptions and is not included in the base IDE license. Pricing is per seat per month on top of the IDE subscription. It is bundled in some higher-tier plans — check your JetBrains account for current entitlements.
How does JetBrains AI compare to Copilot for Java and Kotlin development?
JetBrains AI has an advantage in Java and Kotlin because it integrates with JetBrains' deep language understanding — it understands your project's type hierarchy, framework, and refactoring context in ways Copilot cannot. For Spring Boot and Android development specifically, JetBrains AI tends to produce more contextually accurate suggestions. Copilot is more consistent across languages.
Can JetBrains AI help with IntelliJ refactoring tasks like Extract Method or Introduce Variable?
Yes — one of JetBrains AI's strongest use cases is augmenting JetBrains' already powerful refactoring tools. You can ask it to suggest refactoring opportunities, apply rename operations across the codebase, or generate documentation for existing methods. These IDE-integrated refactoring tasks are where JetBrains AI outperforms standalone AI tools.
What should we disable in Spring Boot apps generated by JetBrains AI before deploying?
Before deploying any JetBrains AI-generated Spring Boot application, check for and disable: the H2 console (spring.h2.console.enabled), Spring Boot Actuator's sensitive endpoints without authentication, management port exposure, and development-only bean profiles. Also verify that spring.datasource credentials are loaded from environment variables, not hardcoded in application.properties.
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