AI app development: A simple guide to build AI application

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The inception of ChatGPT in 2022 marked the wide-scale adoption of Artificial Intelligence in application development. In the field of creating mobile apps, AI-powered tools and frameworks have become indispensable resources for developers. Software companies that provide AI app development services use cutting-edge artificial intelligence tools to speed up app delivery, minimize coding needs, and simplify the development process. Let’s look at what benefits AI-powered tools and frameworks provide their users, along with steps to build an AI application.

Benefits of using AI in app development

Implementing Artificial Intelligence (AI) into applications delivers a host of benefits that enhance user experience and provide innovative solutions for enterprises. These advantages include:


First and foremost, the greatest benefit of AI in app development is the hyper-personalization the application offers to clients in the form of personalized content recommendations and experiences based on previous interactions with the clients.

Better User Experience

An AI-coupled application provides a much better user experience through technologies such as Natural Language Processing(NLP) and sentiment analysis. It does so by employing these technologies to make the interface more intuitive, responsive, and user-friendly.

Automation of Repetitive Task Applications

Implementing AI is instrumental in automating routine and repetitive tasks. These tasks can include data entries, sorting, categorization, and more, thanks to smart AI algorithms designed to optimize and automate these tasks.

Enhanced Search and Discovery

AI-implemented applications rank high on search engines, increasing the visibility and discovery of products online. The algorithms also help devise marketing strategies best crafted for customers and their needs.

Predictive Analytics

Predictive analysis is another crucial avenue to predict user behavior and preferences. It refers to making calculated decisions based on past patterns in current and historical data.

Security and Fraud Prevention

Another added benefit of AI in applications is its ability to detect abnormalities in inputs, outputs, security breaches, identify unauthorized transactions, and more without human intervention or their limitations.

Data Analysis

In the world of Big Data, where data is currency, it is tiresome to sift through huge amounts of data quickly and thoroughly. One could say it is humanly impossible! However, with AI tools, one can simply reduce the time to analyze this data by many fractions. Besides faster analysis, AI tools are also instrumental in providing key insights to multiple stakeholders, further facilitating data-driven decision-making and better applications.

Natural Language Processing (NLP)

Natural language processing, aka NLP, refers to the branch of AI and machine learning technology that teaches any machine to interpret, manipulate, comprehend human input, and respond. One could also say that NLP is why it is becoming difficult to distinguish between a machine and a human.

Cost Savings and Efficiency

Undeniably, poor efficiency results in higher costs for any given business. Here, AI tools can increase efficiency manifold for any and every task. This attribute can save copious amounts of money in the long run.

Adaptive Learning

Also known as adaptive teaching, adaptive learning refers to an educational method that uses computer algorithms and AI to deliver customized resources uniquely crafted to the needs of an individual learner. Adaptive learning is an educational milestone and extremely beneficial for the users it is designed for.

Competitive Advantage

When all workflows are optimized with the help of AI tools, an enterprise gains a competitive advantage over its peers who are not using these tools. This is because AI tools have much to offer that sets companies apart in the corporate rat race. AI tools are revolutionary new-age assets that can digitally transform a company and guarantee a place ahead in the markets.


The most common problem with applications is their inability to stretch beyond a point once the company starts growing. It lacks the flexibility to hold more data, resources, and functions. AI applications are more scalable compared to traditional applications and most, if not all, applications are hosted on a cloud which further removes the need for heavy infrastructure for data storage.

How to develop an app in AI? – simple steps of AI app development

AI applications no longer involve an extensive process or huge amounts of resources. It is much simpler to develop AI applications compared to a few years ago. In 2010, AI app development cost could go up to millions, and the project could take years. Today, one can develop an AI application for less than 100,000 US dollars in a few months. Here are a few simple steps and (tech stacks required for these steps) one can follow for artificial intelligence app development.

Defining the scope of the application

Start with clearly devising the purpose of the application. One can start by identifying the intended audience, the problem the application would solve, and the goals the app wishes to achieve. The tech stack required for this particular application is as follows:

  • Project Management Software,
  • Wireframing Tools,
  • Mind Mapping Tools

Focusing on AI ability

The next step is determining which AI ability you want to implement in the application. It can be NLP, computer vision, image recognition, machine learning, etc. This decision should be taken after carefully examining the vision of the application and the purpose it will serve once it is launched. There can also be a combination of these technologies for more innovation in the digital product.
Different AI components include:

  • Natural Processing Language
  • Machine Learning
  • Predictive analysis
  • Augmented Reality and
  • Image and Video Recognition, amongst others.

Collect data and resources for your application

Another important step is curating high-quality resources to best implement AI technology. It is important to ensure that datasets are diverse and accommodate various users and platforms. The tech stack for this particular phase includes:

  • Web Scraping Tools
  • Data Cleaning
  • Pre-processing Software
  • Natural Language Processing (NLP) Libraries

Select a framework

Choosing a machine learning framework is yet another important task in the application development process. Here are a few frameworks that one can pick from:

  • Tensor Flow
  • Microsoft CNTK
  • Amazon machine learning
  • Scikit-Learn
  • PyTorch
  • Caffe
  • Theano
  • Torch
  • Accord.Net
  • Apache MahoutChoosing a high-level library or platform for quicker and sound development is also advisable.

Build, train, and evaluate models

The next step incorporates developing machine learning models on the chosen framework for your application. This process also includes training the machine learning for the NLP model with pre-vetted data for the best, optimized software performance. The performance is tested iteratively with various metrics and testing techniques. One can do so by employing the following tools:

  • Machine Learning Frameworks (TensorFlow, scikit-learn)
  • Deep Learning Libraries (PyTorch, Keras)
  • Statistical Analysis Tools

Iterate, Iterate and Iterate

The success of the application lies in iteration. The more frequent testing and debugging can remove the smallest of bugs and potential failures, thus mitigating the risk of post-launch application failures and problems.

Develop the application

The next phase entails developing and integrating the trained models into your application by choosing a programming language compatible with the framework. Both frontend and backend development are important in developing the application.

Designing user-friendly front-end

Front-end development needs to be intuitive and user-friendly to ensure that the integration of AI tools within the application is seamless. Its users prefer the application if it’s convenient and simple to navigate. The tech stack to develop a user-friendly interface includes:

  • HTML
  • CSS
  • JavaScript and frontend frameworks like
  • React
  • Angular
  • and Vue. js etc

Test the application thoroughly

Thorough testing is required for any application to perform well. There are many ways one can test an AI application. A few of them are:

  • Artificial intelligence testing
  • Test automation
  • Functional testing
  • Mobile testing
  • Performance evaluation
  • Data testing
  • Payment testing
  • Test coverage

Deploy, monitor, update, and maintain

All the phases amount to this final phase, including deploying the app to the desired platform (cloud, mobile devices, etc). However, the work hasn’t been completed yet. The application must be tracked post-launch for performance, user interactions, feedback, and more. This data is crucial in improving the application and ensuring it evolves with ever-changing tech scenarios. The data also provides insight into what the consumer wants and provides direction for the application to be developed accordingly.

Good Read: 9 Best Programming Languages for AI in 2024


AI applications are becoming increasingly accessible and affordable for individuals and businesses alike. These AI applications and AI app ideas have permeated various industry verticals and have a lot of market potential regarding monetization, growth, and prospects. Any business looking to invest in an AI application should get started by researching for a reliable software development company. Such a company should be able to provide consistent and quality AI Software Development and ML Services backed by a rich portfolio of successful projects. OpenXcell is an AI app development company providing stellar AI-integrated software products to many happy clients. Its solutions are successful and innovative and set a benchmark for the competition in the market.


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Samragyi Chamoli

Zealous, enthusiastic, and consistent, Samragyi is an ardent reader and an experienced writer. She loves animals and adventure.


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