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.
AI-generated code is checked by an independent software agency with experience in architecture, security and operations. Authentication, authorization, database access, secrets, input validation, dependencies, testing, deployment and maintainability are checked. The goal is to get a clear assessment of whether you should continue building, launching or making improvements. CodeGuides checks apps from Lovable, Claude Code, ChatGPT/Codex, Cursor and other tools and provides a prioritized action plan.
Check or make productive? An audit identifies risks and measures. The subsequent fixing, deployment and operation is a separate service: Make AI prototype ready for production. For a formal, independent assessment see Software reports.
Searches with modifiers such as Reddit, forum, review or experiences usually do not signal demand for another sales page, but for real risk information. Teams want to know whether an app built with Lovable, Claude Code, ChatGPT/Codex or Cursor is safe enough for real users. A good answer names typical weaknesses, explains review criteria and shows when an independent review makes sense. That is what this page is built for.
Typical search patterns include "Lovable app security", "ChatGPT app review", "Cursor app production ready" or "AI generated code audit". The real question is almost always: can I trust this code before customers, investors or internal users access it?
An artificial page only for the Reddit modifier would be thin and not credible. A real, verifiable answer is better: security, authentication, database, tests, deployment and operations, the questions users would otherwise ask in forums.
Lovable, Claude Code, ChatGPT/Codex, Cursor, Copilot and other AI assistants can produce working code, but they rarely cover security, architecture and operations. That makes an independent code review essential.
AI generates code that fits the prompt, not the overall architecture. Typical consequences: unprotected endpoints, SQL injection, insecure secrets management.
KI optimizes locally for the current prompt. Global architectural decisions, scalability and technical debt arise systematically.
No monitoring, no testing, no CI/CD: AI-generated apps often run blind. Errors only become visible in productive operation.
We evaluate all areas that AI assistants typically neglect with technical depth and clear prioritization.
If necessary, we also check license risks for open source packages used, GDPR compliance of data processing as well as team and process risks. So ideal 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 comes with typical vulnerability patterns. We know them and specifically check what goes wrong with Lovable, Claude Code, ChatGPT/Codex, Cursor or other tools.
Lovable generates React apps with Supabase backend, fast, but with structural security risks.
Claude Code generates readable code, but operations, testing and architecture in the overall context are typically missing.
ChatGPT/Codex can generate quick app changes and complete features, with typical risks around context limits, authentication, error handling and database design.
Cursor works directly in the editor, but context window boundaries lead to inconsistencies across module boundaries.
v0.dev delivers UI code (React/Next.js), Copilot snippets, both without looking at the overall system or backend security.
Replit Agent, Devin, Windsurf, Gemini Code Assist and others: We know the typical patterns of each tool and check independently.
Many teams today develop quick prototypes and MVPs with Lovable, Claude Code, ChatGPT/Codex or Cursor, and then want to turn them into real products. We check what can be safely taken over and what must be rebuilt.
We clearly distinguish between code that can be used in a stable manner and areas that need to be cleaned up before launch.
You receive a concrete roadmap: what is critical, what is important, what can wait, with realistic effort estimates.
After the review, we can start implementing it straight away: security patches, architecture refactoring and production-ready infrastructure.
Our AI code review provides reliable time periods, structured checkpoints and actionable recommendations.
Top risks and critical vulnerabilities in AI-generated code.
Deep analysis with architecture, security, operations and prioritized action plan.
We check all areas that AI assistants systematically neglect.
We work confidentially, with NDA upon request. Results stay with you.
Minimum coordination effort, maximum insights. We do not disrupt your operations and deliver usable results in clear time frames.
Each step has defined outputs and traceable progress. No guesswork about what we're doing.
We clarify goals, tech stack, usage context and obtain repo access and infrastructure documentation.
Structured testing of code, architecture, security, database, API, tests and operations, especially for vulnerabilities typical of AI.
Prioritized findings, risk assessment, effort estimation and recommendations, presented in a workshop or as a report.
We will take care of the implementation upon request: security fixes, architecture refactoring, test setup, CI/CD setup.
Clear, usable results so that you can make an informed decision whether and how to further develop the app.
Quick overview for urgent decisions or as a first reality check before the launch.
In-depth analysis with complete risk assessment, prioritization and concrete action plan.
Joint evaluation with team or management, can be transferred directly to implementation planning.
Initial general reports usually cost between 1,000 and 2,500 €. The exact cost depends on complexity and desired depth. We will clarify the framework in the preliminary discussion.
If you want to use an AI-developed app productively or further develop it, you need clarity about the actual risks.
Do you need a complete IT report for classic software? To the software report. Should the tested AI prototype go live afterwards? Make AI prototype ready for 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, lack of error handling, and poor architectural decisions that become costly to operate. An independent code review provides clarity before you invest or go live.
Vibe coding is the practice of developing an app almost entirely through prompts to AI assistants such as Cursor, Copilot or ChatGPT. The result can work, but often comes with critical risks: missing tests, SQL injection, unprotected API endpoints and unscalable architecture.
We review security (OWASP, Secrets, Auth), code quality and architecture, database structure, API design, error handling, test coverage, operability and scalability, and deliver a prioritized action plan.
Our AI app review specializes in the typical vulnerabilities of AI-generated code: security vulnerabilities due to a lack of global context, architectural inconsistencies, copy-paste redundancies and a lack of operational fundamentals such as testing, CI/CD and monitoring.
A short report with the most important risks is possible in 48 hours. A complete report with architectural analysis and action plan takes 5-10 working days depending on the scope.
Usually repo access (e.g. GitHub, GitLab), infrastructure documentation and, if necessary, test access. We agree on the scope at kickoff and work confidentially.
The exact costs depend on the complexity and desired accuracy of the report. As a rule, initial general reports from us cost between €1,000 and €2,500. We will clarify the specific framework in a free preliminary consultation.
Yes. After the review, we can take care of stabilization, further development or complete conversion, including roadmap, security fixes and implementation sprints.
Yes. Our AI app review provides a technical decision template for investors and buyers, with clear risks, cost implications and recommendations for evaluation.
We review apps built with all common AI tools: Lovable, Claude Code, ChatGPT/Codex, Cursor, GitHub Copilot, v0.dev, Replit Agent and others. Each tool has typical weakness patterns that we consider deliberately during the review.
Yes. Lovable apps use React with Supabase backend, a pattern that presents clear security risks: Supabase keys in the frontend bundle, missing row level security, no server-side validation and auth logic directly in the client. We examine exactly these weak points and provide a prioritized action plan.
Yes. Claude Code also has typical gaps: missing tests, no CI/CD setup, architectural decisions without overall context and operational fundamentals that were not part of the prompt. We check independently and show what is missing before productive operation.
Yes. ChatGPT/Codex apps and features are created quickly, but they often have gaps caused by missing full-system context: inconsistent auth, weak error handling, missing tests and no clear separation between development and production environments. We assess what must be rebuilt for production use.
Yes. Many projects are created with a combination of e.g. B. Lovable for the frontend, Claude Code for backend logic and Copilot for individual functions. We check the entire code base regardless of the tool used, including inconsistencies in the interfaces.
We work confidentially and will sign an NDA upon request. Results and code remain solely yours.
All consultations are held via Google Meet.
Confirmation and calendar entry arrive immediately by email.
The appointment can be rescheduled if needed.
I look forward to our consultation
CodeGuides is an app and AI agency from Germany for Flutter apps, custom software, AI automation and local AI infrastructure. Consulting and implementation come from a single source: We evaluate use cases technically, build the first pilot and scale it up to company-wide operation, GDPR-compliant and with hosting in Germany.
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.
