Artificial Intelligence Apps in Mobile Development: Key Principles to Know in 2025

AI Mobile Development Process

Traditionally, the process of AI mobile development is divided into several main stages similar to standard mobile development. However, the implementation of AI implies additional work and specification.

• 1

  Planning and Requirement Analysis
  The initial stage defines the entire development process and typically includes a number of tasks regarding planning and management. 

  At this first step business identifies the objectives of the mobile application and defines the AI features that should be presented.

  Should it be a chatbot or a fully operational AI avatar? Which tasks should the AI tool solve? What type of data must it process and generate? Answering all these questions along with those regarding target audience, key functionality, UX/UI and more is necessary for future work.

• 2

  Data Collection and Preparation
  Most of the AI solutions, especially those based on processing of textual content, have a stage of AI model training. The model is trained on examples and sets of data it will work with in the future. Such an approach is usual for large language models (like ChatGPT) and requires the creation of a specially customized dataset. These are only several examples of data types that can be used:

  • Textual Data
    

    Articles, books, text messages and tutorials, customer queries and more

  • Tabular Data
    

    Databases, tabular reports and research, etc.

  • Audio
    

    Voice messages and other audio files

  • Images and Video Content.

  When the data is gathered, they should get through the process of data cleaning, when the irrelevant or erroneous data pieces are identified and removed. For example, your marketing report data may contain wrong figures, some of the customer queries can be useless or health reports are full of erroneous weight and lifetime values (like in our AI for agriculture project).

• 3

  Model Training 
  The training process involves loading the AI model with the created dataset. The model perceives the data and learns through identifying patterns and logical connections between them. In the context of LLMs, training also implies identifying the hierarchy and grammatic, semantic and stylistic patterns between text parts.  

  However, all the present LLMs such as GPT, Gemini or Claude are defined as foundational models, which means that it’s unnecessary to train them on large datasets, only on small specific ones.

  Note! Sometimes, it is easier to apply to a ready-to-use solution, such as integration of OpenAI API, instead of building an AI model from scratch. We thoroughly explored the topic of foundational LLMs usage in our blogpost dedicated to ChatGPT usage in business. 

• 4

  Integration & Deployment
  This stage implies integrating AI model into mobile frameworks to provide efficiency and compatibility with a capability to operate on mobile devices which are typically limited by resources. 

  Integration and deployment imply the usage of several technologies and frameworks such as TensorFlow Lite (for optimizing AI models for mobile), TorchScript, React Native (for frontend development) and ML Kit (provides on-device AI deployment without model training).

• 5

  Further Support 
  The final and one of the most sufficient parts of AI mobile development project is to provide permanent monitoring and real-time performance tracking. It will allow to ensure models remain accurate, efficient, and aligned with user expectations.  

  At this stage, the development team (whether in-house or outsourced) monitors app’s latency rates and resource usage and aligns the predicted performance metrics with the real ones. It can be a firm foundation for further improvements and additions in the app.

  Note! Mobile app development, AI integration and further improvements are tasks that should be carefully considered by business. Silk Data provides services regarding IT and AI consulting where we help companies to define how to efficiently implement certain AI or any other explicit solution.