Have you built an app with Lovable, ChatGPT/Codex, Claude Code, Cursor or Vibe Coding?
We check code quality, architecture, security and performance and give you a clear assessment of whether you should continue building, launching or improving.
An independent software agency with experience in architecture, security, and operations reviews AI-generated code. We check authentication, authorization, database access, secrets management, input validation, dependencies, testing, deployment, and maintainability. The goal is to give you a clear assessment of whether to continue building, launch, or make improvements. CodeGuides reviews apps built with Lovable, Claude Code, ChatGPT/Codex, Cursor, and other tools, then provides a prioritized action plan.
Review it or get it production-ready? A review identifies risks and recommended actions. Remediation, deployment, and ongoing operations are separate services: Take an AI prototype from review to production. For a formal, independent assessment see Software reports.
Searches that include terms like Reddit, forum, review, or user experiences usually indicate a need for real information about risks, not another sales page. Teams want to know whether an app built with Lovable, Claude Code, ChatGPT/Codex, or Cursor is safe for real users. A useful answer identifies common weaknesses, explains the review criteria, and shows when an independent review makes sense. That’s what this page is designed to do.
Common searches include “Lovable app security,” “ChatGPT app review,” “Cursor app production ready,” and “AI generated code audit.” The underlying question is almost always the same: Can I trust this code before customers, investors, or internal users access it?
A page created solely to target the term Reddit would be thin and unconvincing. A real, verifiable answer is more useful: address security, authentication, databases, testing, deployment, and operations—the questions people would otherwise ask in forums.
Lovable, Claude Code, ChatGPT/Codex, Cursor, Copilot, and other AI assistants can produce working code, but they rarely account for security, architecture, and operations. That’s why an independent code review is essential.
AI generates code that fits the prompt, not the overall architecture. Typical consequences: unprotected endpoints, SQL injection, insecure secrets management.
AI optimizes locally for the current prompt. Architectural decisions, scalability issues and technical debt are recurring challenges.
Without monitoring, testing, or CI/CD, AI-generated apps often run without visibility. Issues may not become apparent until the app is in production.
We evaluate all areas that AI assistants typically neglect with technical depth and clear prioritization.
If needed, we also assess license risks in the open-source packages used, GDPR compliance in data processing, and team and process risks. This makes the review a good fit for Software Due Diligence before app purchase or investment.
Authentication, Authorization, Input Validation, SQL Injection, XSS, CSRF, Secrets Management and OWASP Top 10.
Structure, dependencies, design patterns, redundancies, technical debt and maintainability of the AI-generated code.
Schema consistency, N+1 queries, index strategy, API design, versioning and error handling.
Logging, monitoring, CI/CD, deployment strategy, load testing, caching and resiliency.
Test coverage, unit and integration testing, linter configuration and code review process.
Architectural decisions under load, database connections, queue management and cloud readiness.
Every AI tool has its own common vulnerability patterns. We know what to look for and check specifically for issues associated with Lovable, Claude Code, ChatGPT/Codex, Cursor, and other tools.
Lovable quickly generates React apps with a Supabase backend, but this approach can introduce structural security risks.
Claude Code generates readable code, but testing, operations, and architecture across the broader system are often overlooked.
ChatGPT/Codex can quickly generate app changes and complete features, but common risks include limited context, authentication issues, poor error handling, and database design problems.
Cursor works directly in the editor, but context-window limitations can lead to inconsistencies between modules.
v0.dev generates UI code (React/Next.js), while Copilot provides code snippets. Neither necessarily accounts for the overall system or backend security.
Replit Agent, Devin, Windsurf, Gemini Code Assist, and other tools: we know the common patterns associated with each and review the code independently.
Many teams now use Lovable, Claude Code, ChatGPT/Codex, or Cursor to build quick prototypes and MVPs, then want to turn them into real products. We assess what can be safely reused and what must be rebuilt.
We clearly distinguish code that can be used reliably from areas that need work before launch.
You receive a concrete roadmap with realistic effort estimates: what’s critical, what matters, and what can wait.
After the review, we can start implementing it straight away: security patches, architecture refactoring and production-ready infrastructure.
Our AI code review provides realistic timelines, structured checkpoints, and actionable recommendations.
Top risks and critical vulnerabilities in AI-generated code.
In-depth analysis of architecture, security, and operations, with a prioritized action plan.
We check all areas that AI assistants systematically neglect.
We work confidentially and can sign an NDA upon request. The results are yours.
Minimal coordination, maximum insight. We work without disrupting your operations and deliver actionable results within clearly defined time frames.
Each step has defined deliverables and measurable progress, so you always know what we’re doing.
We clarify your goals, tech stack, and use case, and gather access to the repository and infrastructure documentation.
Structured testing of code, architecture, security, database, API, tests and operations, especially for vulnerabilities typical of AI.
Prioritized findings, risk assessments, effort estimates, and recommendations, presented in a workshop or report.
On request, we can implement the recommended changes, including security fixes, architecture refactoring, test setup, and CI/CD setup.
Clear, actionable results to help you decide whether and how to continue developing the app.
A quick overview for time-sensitive decisions or an initial reality check before launch.
An in-depth analysis with a comprehensive risk assessment, prioritized findings, and a concrete action plan.
A joint review with your team or management, ready to feed directly into implementation planning.
Our initial general assessments typically cost between €1,000 to €2,500. The exact cost depends on complexity and desired depth. We will clarify the framework in the preliminary discussion.
If you plan to use or continue developing an app built with AI, you need a clear picture of the actual risks.
Need a comprehensive technical assessment of traditional software? View the software report. Should the tested AI prototype go live afterwards? Take an AI prototype from review to production. Would you like to have the app further developed after the review? App development. Are you planning a platform or SaaS? Build a web platform.
AI-generated code often contains hidden security vulnerabilities, inadequate error handling, and poor architectural decisions that can make the app costly to operate. An independent code review gives you clarity before you invest or launch.
Vibe coding means building an app almost entirely through prompts to AI assistants such as Cursor, Copilot, or ChatGPT. The result may work, but often carries serious risks: missing tests, SQL injection, unprotected API endpoints, and an architecture that won’t scale.
We review security (OWASP, secrets, and authentication), code quality and architecture, database structure, API design, error handling, test coverage, operability, and scalability. We then provide a prioritized action plan.
Our AI app review focuses on common vulnerabilities in AI-generated code: security gaps caused by limited system-wide context, architectural inconsistencies, redundant copied code, and missing operational essentials such as testing, CI/CD, and monitoring.
A brief report covering the most important risks can be delivered in 48 hours. A full report with architectural analysis and an action plan takes 5–10 business days, depending on scope.
We typically need repository access (for example, through GitHub or GitLab), infrastructure documentation, and, if necessary, test access. We agree on the scope at kickoff and work confidentially.
The cost depends on the complexity of the app and the level of detail you want in the report. Our initial general assessments typically cost between €1,000 and €2,500. We’ll discuss the scope in a free initial consultation.
Yes. After the review, we can stabilize the app, continue its development, or rebuild it entirely. This can include a roadmap, security fixes, and implementation sprints.
Yes. Our AI app review gives investors and buyers a technical basis for their decision, with clear risks, cost implications, and recommendations to inform their evaluation.
We review apps built with all common AI tools, including Lovable, Claude Code, ChatGPT/Codex, Cursor, GitHub Copilot, v0.dev, and Replit Agent. Each tool has common weaknesses that we specifically look for during the review.
Yes. Lovable apps use React with a Supabase backend, a combination that can pose clear security risks: Supabase keys exposed in the frontend bundle, missing row-level security, no server-side validation, and authentication logic running directly in the client. We check these specific vulnerabilities and provide a prioritized action plan.
Yes. Claude Code projects can have common gaps: missing tests, no CI/CD setup, architectural decisions made without an understanding of the overall system, and operational essentials that weren’t covered in the prompt. We conduct an independent review and identify what’s missing before the app goes into production.
Yes. ChatGPT/Codex can quickly generate apps and features, but limited system-wide context can leave gaps: inconsistent authentication, weak error handling, missing tests, and no clear separation between development and production environments. We assess what needs to be rebuilt before the app is ready for production.
Yes. Many projects combine tools—for example, Lovable for the frontend, Claude Code for backend logic, and Copilot for individual functions. We review the entire codebase regardless of which tools were used, including inconsistencies between components.
We work confidentially and will sign an NDA upon request. Results and code remain solely yours.
All consultations take place on Google Meet.
Your confirmation and calendar invitation will arrive by email right away.
The appointment can be rescheduled if needed.
I look forward to our consultation
CodeGuides develops apps for consumers and businesses, with a primary focus on B2C apps. Our in-house team in Germany provides product planning, Flutter development and long-term operations, alongside custom software, AI automation and local AI infrastructure.
Use our interactive configurator to get a realistic, non-binding cost range for your project.
In a 45-minute consultation, we clarify how your project can be delivered in a technically and commercially sensitive way.
In a short analysis call, we clarify how we can take over operations, security and continued development for your existing software.
