Service

When Vibe Coding Is No Longer Enough

AI can help you start building quickly and turn ideas into working features in a surprisingly short time. But as your project becomes more complex, technical problems can begin to appear that require experienced developers and professional software engineering.

That is where we can help. We take what you have already built with AI, identify what is holding it back, fix the underlying problems, and give your project the professional engineering foundation it needs to move forward.

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Do These Problems Sound Familiar?

AI can get you to a working first version quickly. But as the project grows, problems can start appearing that are increasingly difficult to solve with another prompt.

Fixing One Problem Breaks Something Else

You solve one issue, only for the change to break a feature that previously worked. Over time, it becomes increasingly difficult to understand how different parts of the code affect each other.

Adding New Features Is Getting Harder

Development started quickly, but every new feature now requires more changes and introduces a greater risk of breaking existing functionality.

You Can No Longer Understand the Codebase

After multiple rounds of AI-generated changes, the codebase can become inconsistent and difficult to follow. It may no longer be clear which parts are necessary, how they interact, or what can be safely modified.

It Works – But You Are Not Sure How Secure It Is

The core functionality may appear to work, but that does not necessarily mean authentication, authorisation, data handling, APIs, and other security-critical components have been implemented correctly.

It Worked – Until Real Users Started Using It

Everything seemed fine during development and your own testing. Then real users arrived, and bugs, unexpected behaviours, edge cases, and problems you had never encountered before began to appear.

You Have Reached the Point Where AI Alone Is No Longer Enough

Your idea has become a working first version, but taking it further now requires sound architecture, proper testing, security practices, and experienced engineering decisions.

84%

use or plan to use AI tools in their development process

AI has become part of everyday software development. According to the 2025 Stack Overflow Developer Survey, 84% of respondents use AI tools in their development process or plan to do so.

66%

encounter AI-generated solutions that are “almost right”

AI-generated code can look correct at first while still requiring significant fixes. 66% of developers report frustration with AI solutions that are almost right, but not quite – and identifying and correcting these issues can require additional engineering work.

45%

of AI-generated coding tasks introduced known security vulnerabilities

In Veracode's research into AI-generated code, 45% of the tested coding tasks introduced known security vulnerabilities. A feature working as expected does not necessarily mean that the code behind it is secure.

Keep What You Have Built With AI – We Will Fix What Is Behind It

You do not necessarily need to start your AI-built project again from scratch. In many cases, the idea, working functionality, and even a usable first version are already there. The real challenge lies in technical issues that may not be immediately visible but can later lead to bugs, security risks, and increasingly difficult development.

Our experienced developers review the existing codebase and determine what should be kept, what needs to be fixed, what should be refactored, and which areas require more substantial changes. We resolve identified issues, improve the code structure and architecture, address security concerns, establish the necessary testing, and create a stronger technical foundation for future development.

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Our Vibe Coding Cleanup Services

Cleaning up AI-generated code involves much more than fixing individual bugs. Our developers review the codebase as a whole, identify problematic implementations and technical issues caused by layers of AI-generated changes, and determine which parts can remain, which should be refactored, and where individual components need to be redesigned.

AI-Generated Code Audit & Technical Assessment

We review the existing codebase, its structure, dependencies, and critical execution paths. We identify duplicated logic, poor abstractions, inconsistent implementations, unnecessary dependencies, and technical decisions that make the software difficult to maintain or extend.

Targeted Code Refactoring & Cleanup

We do not automatically rewrite code that already works well. Instead, we focus on problematic areas: removing duplication, separating mixed responsibilities, simplifying unnecessarily complex logic, and creating a more consistent codebase that is easier to understand, test, and modify safely.

Architecture & Component Boundary Review

Vibe-coded applications can develop tightly coupled structures where business logic, data handling, user interfaces, and third-party integrations become difficult to separate. We review these relationships, clarify component responsibilities, and restructure the application where necessary.

Root Cause Debugging & Stabilisation

We do not simply patch the bug you can see. We investigate its underlying technical cause, including affected data flows, state management, API calls, and connected functionality. Fixes are implemented with the wider codebase in mind to reduce the risk of introducing new regressions elsewhere.

Security Review & AI-Introduced Risk Remediation

We review authentication and authorisation, API access, input handling, database operations, secrets and configuration management, and the handling of sensitive data. We identify security issues that may not be visible during normal use but could create significant risks in production.

Dependency & Third-Party Integration Review

AI-generated projects can accumulate unnecessary, outdated, conflicting, or poorly implemented packages and libraries. We review dependencies, APIs, and third-party integrations, remove unnecessary components, and correct problematic or risky implementations.

Test Coverage & Regression Protection

Rapidly built AI projects often lack the testing required to detect when a new change breaks existing functionality. We introduce appropriate testing around critical business logic and user flows, making future changes easier to validate and reducing the risk of regressions.

Production Readiness & Continued Development

We review the technical areas that separate a working prototype from software ready for production, including environment configuration, error handling, logging, monitoring, build and deployment processes, database migrations, and operational considerations. Once the cleanup is complete, our team can also take over continued product development.

How We Can Work Together

Vibe Coding Cleanup can be a standalone project or the first step in a longer engineering partnership. Depending on what your product needs, we can take over its development after the cleanup or provide ongoing engineering support as your product evolves.

Full Product Development Takeover

Best suited to projects where you have already built a working first version with AI but want an experienced software development team to take it forward. We clean up and stabilise the existing solution, then take responsibility for new feature development, testing, and ongoing technical development.

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Long-Term Engineering Support

If you want to remain actively involved in building the product, our developers can provide ongoing technical support. We help with engineering decisions, complex development tasks, code reviews, and maintaining a stable codebase as the product continues to evolve.

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Results You Will Get

High-Level Engineering Support

Experienced software engineers take responsibility for the technical challenges and decisions that AI alone cannot reliably handle.

Market-Ready, Reliable Software

We turn your working AI-built prototype into more stable, tested, and secure software that is better prepared for real users and production environments.

A Strong Foundation for Continued Development

We improve and restructure the existing solution so that new features and future product development become more predictable – without automatically starting again from scratch.

Explore our AI software development projects

Caspar

Caspar Cloud is a case management system built for organisations that are looking after the elderly, sick or other people who cannot take care of themselves.

Custom Software Development • IT Staff Augmentation • United Kingdom • Healthcare

Fyld

Fyld enables fieldworkers to survey job sites with their smartphone or tablet, upload pictures, audio, and video content to the system.

IT Staff Augmentation • United Kingdom • Infrastructure

Grandio

Grandio is a web and mobile application for a Hungarian asset management firm that provides secure access to portfolio values and related data, with RabIT delivering ongoing support, shared QA, and agile development packages.

Custom Software Development • Mobile Application Development • Web Application Development • Hungary • Advisory and Financial Services

Rentkey

Rentkey is a modern, scalable bilingual web application built for international markets, designed to efficiently manage complex business workflows, document handling, and automated processes within a single platform through Scrum-based development.

Web Application Development • Hungary • Real Estate

From Discovery to Deployment: Our AI Process

Discovery & AI Opportunity Assessment

We identify your business needs, available data and the areas where AI can create real value.

Technical Feasibility & Solution Design

We evaluate the data, technologies, integrations and architecture required to build the right AI solution.

Proof of Concept & Rapid Prototyping

We validate the core AI functionality with a focused prototype before moving into full-scale development.

AI Development & Integration

We build the production-ready solution and integrate it with your existing software, data and business workflows.

Deployment, Monitoring & Continuous Improvement

We deploy the solution, monitor its performance and continuously improve it as requirements and data evolve.

Frequently Asked Question

Still have questions?

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AI software development is the process of designing and building software that uses artificial intelligence to perform tasks such as understanding language, analysing data, automating workflows, generating content or supporting decision-making. It can involve technologies such as large language models (LLMs), machine learning, AI agents, RAG systems and computer vision, depending on the business problem being solved.

AI can be integrated into existing software through APIs, AI models and custom services that connect with your current applications, databases and business systems. This makes it possible to add capabilities such as intelligent search, automation, recommendations, document processing or conversational interfaces without necessarily rebuilding the entire application.

We develop AI agents and multi-agent systems, generative AI and LLM applications, RAG and enterprise knowledge systems, AI chatbots and copilots, intelligent document processing solutions, AI-powered workflow automation, machine learning and predictive systems, and computer vision and multimodal AI solutions.

Yes. AI solutions can be designed around your existing documents, databases, knowledge bases and other business data. For example, RAG systems can connect large language models with internal company information, enabling users to search and interact with relevant organisational knowledge while keeping the solution grounded in approved data sources.

An AI chatbot primarily interacts with users through conversation, answering questions or providing information. An AI agent can go further by accessing tools, APIs and business systems, making decisions within defined boundaries and executing multi-step tasks. Depending on the use case, multiple specialised agents can also work together as part of a larger workflow.

Retrieval-Augmented Generation (RAG) connects a generative AI model with external knowledge sources such as company documents, databases or internal knowledge bases. When a user asks a question, the system retrieves relevant information and provides it to the AI model as context, helping it generate answers based on the organisation's own data.

We can begin with technical feasibility assessment and, where appropriate, a focused proof of concept or prototype. This allows us to test the core AI functionality with representative data and real-world scenarios, evaluate model performance and identify technical limitations before moving into full-scale development.

Yes. AI capabilities can be integrated into existing web and mobile applications without necessarily replacing the underlying software. Depending on the application, this can include AI assistants, intelligent search, recommendations, automated workflows, document processing, data analysis or other AI-powered functionality connected to the existing backend and business systems.