How AI Helps Onix Deliver Mobile Apps Quickly Without Losing Quality

It's no secret that artificial intelligence is changing software development, and mobile development is no exception.

 

At Onix, we don’t try to chase hype. We use AI for iOS and Android mobile app development, where it truly helps:

 

• move faster,
• solve tricky problems,
• and focus more on the creative side of development.

AI is our reliable partner, rather than a replacement for human expertise.

 

But how can we reach this balance and only benefit from AI technology in mobile app development for Android and iOS?

 

In this article, we’ll reveal our mobile app development process using AI, share its benefits, and discuss where we still rely on human expertise.

How AI is Changing Mobile Development

Mobile app development for iOS and Android involves:

 

• hours of manual coding,
• endless debugging,
• digging through documentation,
• and dealing with repetitive setup tasks. 

 

By leveraging artificial intelligence, our developers can transform how our iOS and Android teams work today.

 

“AI doesn’t write the app for me, but it clears away the clutter. Instead of spending hours chasing a bug or rewriting boilerplate, I can focus on the architecture and user experience, the parts that matter.”

Denys Senichkin,
Head of iOS department at Onix

 

With the right tools, the Onix developers can:

 

• Shorten development cycles and ship features faster
• Automate repetitive coding and testing tasks
• Improve accuracy and maintain cleaner codebases
• Get instant help with debugging and error analysis
• Speed up research, prototyping, and documentation

 

Hovewer, we all remember, AI in mobile app development for iOS isn’t a silver bullet. It is just our assistant in the routine work.

 

How Our iOS Team Uses AI

In our iOS mobile app development workflow, AI helps solve different issues, optimize the coding process, and improve the quality of our projects.

 

Here is how our developers leverage AI to enhance their productivity and effectiveness:

 

Clarifying technical specifications

Incomplete requirements are critical initial challenges in mobile iOS app development.

 

Now, with the use of AI, we don't waste time clarifying such ambiguous requirements anymore. AI agents for mobile app development do it for us and quickly enough.

 

AI assists us in:

 

• analyzing specs
• providing precise suggestions on how best to structure the codebase
• choosing appropriate patterns
• preventing possible future problems.

 

For example, when defining protocols and extensions, AI helps ensure compliance with SOLID principles, optimize development, and simplify future changes.

Optimizing and refactoring code

Performance, readability, and maintainability are vital for any app, but refactoring is time-consuming.

 

AI efficiently identifies redundant logic, suggests better algorithms, and helps refactor complicated Swift constructs, such as closures, generics, and concurrency handling.

 

One case: Recently, AI helped our team significantly improve the performance of UICollectionView rendering by advising on proper cell reuse strategies and asynchronous image loading techniques, which made the UI much smoother.

 

Fixing complex bugs

AI greatly speeds up the process of fixing complex bugs, especially in concurrent or asynchronous code.

 

To suggest probable causes, artificial intelligence analyzes:

 

• error logs,
• stack traces,
• and our descriptions. 

 

It effectively triggers detected root causes, whether they are related to race conditions, memory leaks, or incorrect data processing.

 

Rapid UI prototyping in SwiftUI

A model like ChatGPT can quickly generate SwiftUI code based on a design description or mockup. We sometimes use this to get a basic template for a custom UI component.

 

Of course, AI doesn't always perfectly understand the idea or know all the nuances of SwiftUI, so the generated code may need some refinement. However, as a starting point, such draft code saves time.

 

App store optimization

AI assistants help write an attractive and informative app's description, highlighting key features. It is enough to provide the model with a few abstracts about our product, and at the output, we will get an approximate version of the description that “catches” the user.

 

Additionally, AI can analyze popular searches and competitor descriptions to suggest relevant keywords for the App Store.

 

We use such tips when brainstorming metadata (keywords, title, subtitle). For example, the model may recommend adding words like “organizer” or “to-do list” to our task manager keywords if they are trending in search. 

 

Of course, we determine the final set of keywords with the marketing team, but AI significantly speeds up this process and gives fresh ideas.

 

Read also: How to Build Machine Learning Teams for AI Projects

 

Learning new techniques

AI greatly assists our specialists in keeping up with the ever-changing Swift language and frameworks.

 

It lets the Onix team quickly learn new Swift features such as async-await, SwiftUI performance optimization techniques, and the intricacies of the Combine framework.

 

It provides:

 

• detailed and practical examples,
• clear explanations of complex concepts, 
• step-by-step instructions.

 

This significantly shortens the learning curve and improves our ability to immediately and effectively apply these skills in real-world scenarios.

 

Our Real Example of Using AI in IOS App Development Process

A specific scenario where AI significantly improved our Swift development process was the comprehensive refactoring of an application's complex network layer.

 

The challenges we faced: The existing network layer has become cumbersome, with repetitive code, scattered error handling, and inefficient request management.

 

The main goal was to improve maintainability, scalability, and error handling.

 

We shared the existing implementation with AI, highlighting issues such as difficulty managing authentication tokens, excessive redundancy, and unclear separation of responsibilities.

 

Here’s what AI recommended to us:

 

• Implement a structured and modular approach using the Moya framework, complemented by plugins for authentication, analytics, and logging.
• Use Combine to improve asynchronous query processing and simplify error handling.

   

AI offered a clearly structured and scalable implementation:Code example provided by AI

 

Results: Integrating this approach immediately improved readability, simplified debugging, ensured consistency between network requests, and optimized authentication management.

 

The new structure allowed easy expansion and significantly reduced future maintenance efforts.

 

How Our Android Team Uses AI 

Our Android developers use AI in their day-to-day development process to speed up routine work, find better solutions, and get apps up and running faster.

 

Hovewer, we use AI for Android app development in a “co-pilot” or “assistant” mode, leaving architect-level responsibilities to our developers.

 

“AI doesn’t build the app for me, but it removes the routine. Instead of wasting time on boilerplate or digging through repetitive issues, I can concentrate on architecture, performance, and delivering the best Android experience.”

Volodymyr Bandurka,
Head of Android department at Onix

 

Here’s how we use AI in Android development:

 

Test coverage generation

AI tools for Android development create test coverage for code, allowing us to ensure stability across Android devices.

 

Nevertheless, AI agents have quite a high error rate, so our developers still review and refine the tests.

 

Building MVP infrastructure

Since “pure” Android development is becoming less common, using LLMs allows us to create the entire necessary infrastructure, including the backend and frontend, during the MVP stage. 

 

We are actively developing in React Native using Claude and Cursor, supplementing the codebase with native Android plugins. This hybrid approach has proven successful in speeding up cross-platform development.

 

Backend development with AI

Our stack includes a backend, typically written in Python with FastAPI, where neural networks have proven to be exceptionally effective. Depending on project requirements, we also build backends with Kotlin using Ktor, and we frequently leverage managed solutions like Firebase or Supabase for authentication, storage, and real-time capabilities.

 

AI tools for Android app development help create and improve backend services to support Android applications seamlessly.

Marketing support

A significant portion of our current work involves creating websites and marketing materials.

 

Artificial intelligence provides tenfold acceleration in this area, allowing us to create advertising sites and presentations quickly.

Video generation

We use Veo to create short videos. AI in Android app development allows us to generate three videos per day, enabling us to create a suitable promotional video for online distribution in just a few days.

AI Limitations: Where Human Expertise Still Matters

Although artificial intelligence is impressive in its capabilities, it is worth remembering that it is not a panacea for mobile app development (iOS and Android).

 

There are tasks for which there is simply no ready-made solution in the open access, and no one has written articles about them or responded to them on forums.

 

Read also: AI Bias Detection Guide: Methods, Tools, and Strategies

 

For example, highly specialized problems that only a few developers have encountered can only be discussed in private communities or within the company.

 

In such cases, no ChatGPT will replace your own in-depth research.

 

AI does not have magical knowledge; it only operates on information from its data and user prompts. 

 

Therefore, sometimes, the only way to solve a problem is through the good old trial and error method, a deep understanding of the subject area, and consultation with colleagues.

 

AI helps with execution. People provide vision, judgment, and empathy.

 

Let's take a look at a comparison table that shows where AI excels and where human experience still matters:

 

Looking Ahead: The Future of AI in Our Workflow

Autonomous AI agents in IDEs expand developer capabilities. GitHub Copilot Agent Mode in JetBrains, Eclipse, and Xcode environments allows an AI assistant to:

 

• analyze code,
• plan changes,
• run a build of the project,
• and automatically fix errors. 

 

GitHub recently introduced this mode for Xcode (as well as for the JetBrains and Eclipse IDEs), which essentially allows an AI executor to take on complex multi-step tasks in the code.

 

Copilot in agent mode can analyze the entire project, form a change plan, and even implement it. It can edit multiple files, execute terminal commands (for example, build the project or run tests), and independently find and fix errors. 

 

This is the next step in developing so-called “vibe coding,” i.e., close cooperation between developers and AI directly in the IDE. 

 

We plan to test such capabilities in our work soon. In particular, we are considering  ​​setting up our own MCP server.

 

Model Context Protocol (MCP) is an open standard that allows LLM agents to use external tools and services. Integrating such a server with the Cursor editor would enable AI to interact with our Xcode project in real time.

 

We also expect that AI agents will soon be able to fully execute tasks from detailed tickets. 

 

In anticipation of this, we have developed our own Git-based task tracker. This system allows agents to interact directly with tasks using commands like “Take task PRNE-134 and begin execution.”

 

This process requires constant human supervision to verify the results, which is quite expensive (primarily due to the cost of models like Claude 3 Opus).

 

Nevertheless, the department is closely monitoring the technology's development and expects that one day, every mid-level developer will have at least three junior-level AI agents supporting them.

Read also: Top 8 Mobile App Development Trends to Look for in 2025

 

AI in Mobile Development: Finding the Right Balance

Artificial intelligence is already changing mobile development; there’s no doubt about it.

 

It’s a powerful assistant that saves time and opens up new possibilities. But the keyword here is assistant. AI is not a replacement for our developers.

 

Let's be honest: it can’t understand user behavior,  have a creative problem-solving approach, and ensure scalability. At least it is so now.

 

That’s exactly where our team’s expertise makes the difference. This is where our specialists are indispensable.

 

Yes, we use AI for building Android apps and iOS apps, but not everywhere and never without critical control.

 

Our key to success is this balance, which allows the Onix team to build apps faster while ensuring they are reliable, scalable, and user-centric.

 

FAQs

 

• How can AI speed up the development of my mobile app?

AI can automate repetitive coding tasks, generate test coverage, assist in debugging, and even create draft UI components. This allows developers to focus on architecture, UX, and product strategy, reducing overall development time.

 

• Will AI replace developers on my project?

No. AI acts as a supporting tool, not a replacement. Human expertise is essential for architecture decisions, quality assurance, UX design, and long-term planning. AI helps the team work faster and smarter.

 

• What are the best AI tools for Android app development?

Our Android team mainly uses AI as a copilot. The most effective AI tools for Android developers are:

 

• Claude – code snippets, debugging, backend/frontend MVP support
• ChatGPT – prototyping, clarifying requirements, documentation
• Gemini – code suggestions and alternative approaches
• Cursor & AI Studio – assist coding in IDEs, still maturing

 

Android app development with AI speeds up development, but human oversight remains essential for architecture, integration, and quality assurance.

 

• How do you ensure app quality when using AI?

We combine AI automation with strict human review, testing, and code optimization. AI handles routine tasks, while our developers manage architecture, UX, performance, and security.

 

• Can AI help us speed up MVP development to test the market?

Yes. AI can accelerate MVP development by automating repetitive coding tasks, generating boilerplate code, and assisting with backend and frontend integration.

 

It also helps quickly create test coverage, documentation, and basic UI components. This allows your team to launch a functional MVP faster, test ideas with real users, and gather feedback without compromising quality.

 

• How quickly can AI produce prototypes or sample features for review?

Depending on complexity, AI can generate draft prototypes or feature samples in hours. For example, SwiftUI or Jetpack Compose UI components, backend API scaffolding, or simple app workflows can be created quickly.

 

While the generated code often requires human refinement, it provides a solid starting point, letting your team review, iterate, and test concepts much faster than building everything from scratch.