An AI code review is needed when your app already works on a basic level, but you are experiencing issues or aren't sure whether it is ready for release.
Before launch
Your AI prototype works, but you want to know whether it needs improvements to be secure, stable, and ready for real users.
When bugs keep appearing
Repeated issues can be a sign of deeper problems in architecture, code structure, or testing. An AI code audit helps find the root causes instead of fixing symptoms one by one.
When development starts slowing down
If every new feature takes longer or breaks something else, the codebase may have become difficult to extend. We identify the structural issues behind that slowdown.
Before scaling the product
More users, integrations, and functionality put additional pressure on the system. A vibe code audit shows whether the current architecture can support that growth.
Our engineers work with AI throughout the software development process. Here are the issues that appear most often in AI-generated apps.
Security gaps
A working login or payment flow does not necessarily mean it is secure. AI-generated projects often have weak access controls, exposed credentials, unsafe data handling, or vulnerable dependencies. These issues can lead to unauthorized access, data leaks, and account compromise once real users start using the product.
Code without a clear structure
Features generated at different times can follow different patterns. Code style, naming, folder structure, and implementation logic may change throughout the project. This makes the codebase harder to understand, increases the chance of breaking existing functionality, and makes future development slower and more expensive.
Architectural limitations
Without clear technical guidance, AI can make architectural decisions that work for the immediate feature but create problems later. As the product grows, this can cause performance issues, make new features harder to add, and eventually force parts of the system to be reworked or rebuilt.
Hidden bugs and weak error handling
Vibe-coded apps often have weak error handling because the focus is on getting the main flow to work. When something goes wrong, users may see broken screens, lose entered data, get charged incorrectly, or be unable to complete an important action.
Tests that give false confidence
Vibe-coded apps often have limited or superficial test coverage. This means serious bugs can slip into production, break key user flows, and reappear after seemingly unrelated changes.
Not ready for production
A prototype may work without proper logging, backup and recovery, deployment processes, or safeguards for high traffic and failed services. If these production requirements are missing, the app can become difficult to operate and unreliable once real users depend on it.
Our engineers review the app from both a product and technical perspective. The AI code review consists of several interconnected components.
Architecture review
Check whether the system has a clear structure and whether its main parts work well together. This helps uncover decisions that could make future features harder to add or force expensive rework later.
Code structure and standards
Review how the code is organized and whether consistent standards are followed across the project. If the structure is chaotic, we define a clearer approach for code style, naming, and implementation patterns.
Security check
Review authentication, access control, sensitive data handling, secrets, and third-party dependencies. The goal is to find issues that could expose user data or give someone access they should not have.
Product flow review
Test the main user journeys and look at what happens when something goes wrong. This helps uncover broken flows, missing validation, and weak error handling that may not appear during normal testing.
Testing and production readiness
Check whether the existing tests cover the key parts of the product. We also review production requirements such as logging, backup and recovery, deployment setup, and failure handling.
Prioritized recommendations
Get a clear list of what should be fixed first and what can wait. We separate critical risks from lower-priority improvements, so you can make decisions without having to interpret a highly technical report.
Fixes and further development
Continue directly from the audit into implementation. We can fix critical issues, refactor unstable parts of the codebase, and continue product development on a stronger technical foundation.
Our experience across different industries enables us to understand which parts of the codebase deserve the most attention and what production-ready software should look like in each case.
AI code review for healthcare
Healthcare products need reliable access control, careful data handling, and stable user flows. We review the code with these requirements in mind and look for issues that could affect privacy, permissions, or day-to-day product reliability.
AI code review for fintech
Financial products depend on secure transactions, accurate business logic, and dependable integrations. Our fintech experience helps us identify risks around payments, authentication, sensitive data, and workflows that cannot afford silent failures.
AI code review for construction
Construction software often connects field teams, office workflows, reporting, and project data. We look at whether the codebase can support these workflows without becoming difficult to maintain as the product grows.
AI code review for logistics
Logistics products rely on orders, routes, status updates, and integrations working together consistently. We assess whether the architecture and application logic can handle this complexity without creating unstable workflows or data issues.
AI code review for real estate
Real estate platforms usually combine listings, documents, payments, tenant or client communication, and role-based access. We review how these parts are implemented and whether the code is structured well enough for further development.
AI code review for software products and SaaS
We work with SaaS platforms that include subscriptions, multiple user roles, dashboards, integrations, and complex business logic. This gives us a strong understanding of where weak architecture, inconsistent code, or poor testing can start slowing down product development.
We combine hands-on AI expertise with years of software engineering experience to assess what your product needs now and what it will need as it grows.
Hands-on AI development experience
We build AI-powered software ourselves – from generative AI features to AI agentic workflows and standalone AI apps. This gives our team practical experience with how AI behaves inside real development processes.
Strong product engineering background
AI code still needs solid software engineering behind it. With 12+ years of product development experience and 80+ delivered projects, our team knows what production-ready architecture, maintainable code, and reliable delivery should look like.
Client trust
Our clients rely on us for practical AI expertise, strong engineering, and reliable delivery. Apiko has 60+ verified Clutch reviews and a 4.9/5 rating, reflecting our approach to work.
Review aligned with your product goals
Our AI code review is not limited to technical issues. We assess the code in the context of your product requirements, business goals, and plans for further development, so the recommendations support where you actually want the product to go.
An AI code review is a structured assessment of a codebase created fully or partly with AI tools. It looks beyond whether the app works and checks whether the code is secure, maintainable, well-structured, and ready for further development.
No. We can review products built entirely with AI tools as well as projects where developers used AI during part of the development process.
We can review code created with tools such as Cursor, Lovable, Bolt, Claude, GitHub Copilot, and similar AI coding platforms. The review focuses on the resulting codebase rather than the tool used to generate it.
No. We explain the findings in clear language and focus on what each issue means for the product. You get a prioritized view of what needs attention now and what can wait.
Yes. Part of the review is understanding which areas can be kept, which need refactoring, and where rebuilding may be more practical than continuing with the current implementation.
Yes. We can review both prototypes and live products. For production apps, we pay particular attention to security, stability, maintainability, and risks that could affect real users.
The cost depends on the size of the codebase and product complexity. After a short initial assessment, we can define the scope and provide an estimate based on your project.
