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How to Build an AI App: A Step-by-step Guide

How to Build an AI App
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The power and potential of artificial intelligence cannot be overstated. It has transformed how we interact with technology, from introducing us to robots that can perform tasks with precision to bringing us to the brink of an era of self-driving vehicles and rockets. And this is just the beginning. With a staggering 270% growth in business adoption in the past four years, it has been clear that AI is not just a tool for solving mathematical problems but a transformative force that will shape the future of our society and economy.

Artificial Intelligence (AI) has become an increasingly common presence in our lives, from robots that can perform tasks with precision to autonomous cars that are changing how we travel. It has become an essential part of everything, from large-scale manufacturing units to the small screens of our smartwatches. Today, companies of all sizes and industries are turning to AI to improve customer satisfaction and boost sales. AI is the next big thing, making its way into the inner workings of Fortune 500 companies to help them automate their business processes. Investing in AI can be beneficial for businesses looking to stay competitive in a fast-paced business world.

It shouldn’t be a surprise that artificial intelligence is expected to grow in market share due to organizations’ increasing adoption of AI. According to the latest data, the market for artificial intelligence was valued at $51.08 million in 2020, according to verified market research (2021). This number is expected to rise more than tenfold in eight years and reach $641.3 billion by 2028. The rising adoption of cloud-based services and the increased demand for AI-based virtual assistance are two of the key drivers of this remarkable artificial intelligence growth. Companies are beginning to rely on AI to provide personalized services to customers, and this trend is likely to continue as customer service grows in importance. This raises the next question – how to build an AI app?

This article describes the fundamentals of AI and a step-by-step guide to building an AI system.

What is artificial intelligence?

Artificial intelligence (AI) is a field of computer science that focuses on solving cognitive programs associated with human intelligence, such as pattern recognition, problem-solving and learning. AI refers to the use of advanced technology, such as robotics, in futuristic scenarios. There have been many definitions surfaced of artificial intelligence, but John McCarthy provides the following definition in his 2004 paper – ” It is the science and engineering of making intelligent machines, especially intelligent computer programs. It relates to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods.”

Artificial intelligence, in its most basic form, is a field that combines computer science with robust datasets to facilitate problem-solving. Artificial intelligence also includes sub-fields like machine learning and deep learning, which are often mentioned together. The algorithms of these technologies are used to build expert systems that can make predictions and classifications based on input data. AI encompasses many disciplines, including computer science, data analytics, statistics, hardware, software engineering, neuroscience, psychology, and philosophy.

What are the components of AI?

components of AI


Computer programs learn differently from humans. Computer learning can be further divided into many forms where learning for AI is one of the most important components. It includes solving of problems using the trial-and-error method. The program also keeps track of the positive moves and saves them in its database for the next time it faces the same problem. Learning in AI is memorizing individual items, such as vocabulary and solutions to problems. It’s also called rote learning. This learning method can later be applied using the generalization technique.


Until five decades ago, the art of reasoning was a skill limited to humans. The ability to distinguish makes reasoning an essential component of artificial intelligence. This ability allows the platform to draw inferences compatible with the given situation. These inferences can be classified as either deductive or inductive. There is a great success rate using deductive inferences via programming computers. Inferential cases provide guarantees that a problem can be solved. For example, the accident is an inductive case; however, always due to instrument failure.

Reasoning involves drawing inferences relevant to the current situation.


AI’s problem-solving ability is basic, including data where the solution must find an unknown value. AI witnesses many problems being solved on the platform. These methods are essential components of artificial intelligence that divide queries into general and special purposes. A special-purpose method is a solution that is tailored to solve a particular problem which is often done by leveraging some of the features found in the case in which the problem was embedded. A general-purpose approach can solve many different problems. At the same time, AI’s problem-solving component allows programs to reduce differences step-by-step between goals and current states.


Artificial intelligence’s ‘perception’ component allows the element to scan any environment using different sense organs. The internal processes allow the perceiver the ability to examine other scenes and determine their relationship. This analysis can be complicated and similar objects might appear differently at different times depending on how the angle is viewed.

Perception is a component of artificial intelligence that can propel self-driving cars at moderate speeds. FREEDY is one of the first robots to use perception to identify different objects and assemble artifacts.


Language can be described as a collection of system signs that are consistent with each other. Language understanding is a widely used component of artificial intelligence that uses distinct types of language to understand natural meanings, such as overstatements. Human English is one of the most important characteristics of languages allowing us to distinguish between objects. AI is designed in such a way that it can understand English, the most common human language. The platform makes it possible for computers to understand different computer programs that are executed on them easily.

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Different elements of AI

Artificial intelligence encompasses many techniques. Let’s learn more about the main subfields within AI.

Elements of AI

Machine learning

Machine learning is a very important field in advanced technology. It is a must to have term company introduces a new product that uses ML algorithms and techniques to deliver to the consumer highly creatively. This technique allows computers to learn without being explicitly programmed and used in real-life use cases. It is fundamentally the science that allows machines to interpret, execute, and analyze data to solve real-world problems. Programmers use complex mathematical knowledge to design machine-learning algorithms written in a machine-readable language to create a complete ML system. Besides, ML allows us to decode, categorize and estimate data from a dataset.

It has provided self-driving cars, image and speech recognition, demand forecasting models, useful search, and many other applications over the years. It focuses on applications that can adapt to experience and improve their decision-making or predictive accuracy over time.

Data professionals also choose types of machine learning algorithms as described below depending on data availability.

  • Supervised learning: Data experts feed labeled training data into algorithms and assign variables to the algorithms to access and find correlations. Both the input and output of the algorithm are particularized.
  • Unsupervised learning: These types of learning use algorithms that train with unlabelled data. An algorithm analyzes datasets to draw meaningful connections or inferences. Cluster analysis, for example, uses exploratory data analysis to find hidden or grouping patterns in data.
  • Reinforcement learning: Reinforcement learning is used to teach a computer to follow a multi-step process with clearly defined rules. Programmers create an algorithm that will perform a task. They then give the algorithm positive or negative signals to execute the task. Sometimes the algorithm decides for itself what actions to take.

Neural network

The neural network combines cognitive science with machines to complete tasks. It is a branch of artificial intelligence that uses neurology, a part of biology that deals with the nerve and nervous system. The neural network is a way to simulate the human brain, where there are infinite numbers of neurons.

A neural network, in simple terms, is a collection of algorithms used to discover the elemental relationships among the data sets. It mimics the human brain’s operating process. A neural network is a system of neurons that are either artificial or original in nature. A neuron is a mathematical function in a neural network whose job is to collect and classify information according to a specific structure. The network strongly implements statistical techniques such as regression analysis to complete tasks. They are used extensively for everything from market research to forecasting, fraud detection, risk analysis, and stock exchange prediction.


This is an emerging field of artificial intelligence and a fascinating field of research and innovation that focuses mainly on the design and construction of robots. Robotics is an interdisciplinary field of science and engineering that incorporates mechanical engineering, electrical engineering and computer science. It is the study of designing, manufacturing, operating and using robots that involves computer systems that control them, produce intelligent outcomes and transform information.

Robots are often used to do tasks that would be difficult for humans to complete repetitively. For example, the majority of robotics tasks were related to assembly lines for automobile manufacturing and the transportation of large objects in space by NASA. AI researchers are also working on robots that use machine learning to enable interaction at the social level.

Expert systems

The first successful AI software model was the expert system created in the 1970s and became more popular in the 1980s.

An expert system is a computer system that imitates human experts in decision-making. This is done by using its knowledge base to derive knowledge and then applying reasoning and insights rules to the terms of user queries. Expert systems’ effectiveness is dependent on the knowledge of the expert. The more information the system has, the greater its efficiency will be. The expert system offers suggestions for spelling and grammatical mistakes in the Google search engine. The system can be used to solve complex problems by reasoning with proficiency. This is especially true when using “if-then” rules rather than traditional agenda to code. Expert systems are highly responsive, reliable, understandable, and efficient in execution.

Fuzzy logic

Fuzzy logic is a type of mathematical logic that deals with approximate reasoning rather than fixed and exact reasoning. It simulates the ambiguity and uncertainty that frequently exists in real-world situations. Fuzzy logic is used to process and analyze data from various sources in order to make decisions.

Natural language processing (NLP)

In layman’s terms, NLP is a part of computer science and AI that allows communication between humans and computers using natural languages. It allows computers to understand and read data mimicking natural human language. NLP refers to a method of searching, analyzing and understanding text data. Programmers use the NLP library to teach computers how useful the information is from text data. NLP is commonly used to detect spam. At the same time, computer algorithms can look at the subject or text of an email to determine if it is junk or not.

How does AI work?

As we have mentioned in the previous section, AI is a superset of machine learning and deep learning, it can be used as a decisive instrument for these techniques. AI works through patterns learned from data sets. The intelligent and iterative collection process allows for accumulating large amounts of data which the AI tool uses to learn patterns. The AI model then predicts the outcome based on the learned patterns. Many iterations are associated with building the AI model, and each iteration is used to test its activity and measure the accuracy level. It can process huge amounts of data quickly due to its computational power. AI empowers a computer to solve problems by itself.

As a method, artificial intelligence can be classified into the following categories:

  • Artificial Narrow Intelligence (ANI): This form of artificial intelligence is used in most practical applications. This concept is primarily about making the computer learn how to solve a specific problem by itself.
  • Artificial General Intelligence (AGI): AGI is concerned with computers that mimic human cognition.
  • Artificial Super Intelligence: This is an abstract form of AI.

The following subfields of AI help AI models work using data patterns:

  • Machine learning: It automates the building of analytic models that draws on methods from statistics, physics, and neural networks to uncover hidden insights in data.
  • Neural network: This machine learning type comprises interconnected units like neurons that process information in each unit. This process involves multiple passes at the data in order to identify connections and derive meaning from undefined data.
  • Deep learning: It utilizes huge neural networks with many processing units that take advantage of advancements in computing power and better training techniques to learn complex patterns from large amounts of data. Image and speech recognition are two common applications. Algorithms of deep learning, such as generative adversarial networks (GAN) and variational autoencoders (VAEs), are widely used in generative AI to generate highly realistic data similar to existing data.
  • Computer vision: It uses pattern recognition and deep learning to recognize what is in a photo or video. Machines can process, analyze, and understand images utilizing computer vision. Alongside, they can capture images and videos in real-time and interpret the surroundings.
  • Natural language processing: NLP allows computers to understand, analyze and create human language, including speech. Natural language interaction is the next stage in NLP which allows humans to use everyday language to communicate with computers to accomplish tasks.

A variety of technologies enable and support AI, namely

  • Graphical processing units: They are key to AI as they provide the high computing power required for iterative processing. Big data and computing power are required to train neural networks.
  • Internet of Things: This technology generates large amounts of data via connected devices, although not all are analyzed. AI-based models will enable us to generate more of it.
  • Advanced algorithms: They are used to combine data at different levels and analyze it faster. Intelligent processing is crucial for identifying and predicting rare events, understanding complex systems, and optimizing unique situations.
  • APIs: They are portable code packages that allow adding AI functionality to existing products and software packages. For example, they can be used to add image recognition capabilities for home security systems and Q&A capabilities that provide data descriptions, headlines, and interesting patterns.

Artificial intelligence applications in business

AI is an emerging technology whose full potential benefits are yet to be realized. AI innovation is just one of many forces disrupting markets and creating new opportunities for digital businesses. AI can also be applied to various industries, functions, and organizations in various ways. Here are some business applications of AI:

  • Machine learning is the backbone of human-like communication: ML drives common AI applications like chatbots, robots, and autonomous vehicles.
  • Deep learning: This method uses facial, voice, and neural networks to provide biometric solutions. These techniques hyper-personalize content using data mining and pattern recognition across large datasets.
  • Artificial intelligence in IT operations: Virtual support agents (VSA) provide IT support in IT service management alongside the IT service desk. AI can route tickets, pull information from knowledge management sources, and provide common answers.
  • AI in supply chain management: These use cases include predictive maintenance, risk management and procurement. Because AI is consistent and quicker than humans at certain tasks, it can be used for decision-making automation.
  • AI for sales enablement: AI identifies and nurtures new ideas and prospects based on existing customer data. It also uses guided selling to increase sales execution and revenue.
  • AI in marketing: AI acts as a tool that can assist with real-time personalization and content and media optimization, campaign orchestration, and other tasks otherwise limited by human resources and capabilities. AI’s ability to uncover customer insights and speed up the deployment of products at scale is the most compelling value proposition.
  • AI in customer service: Customers have 24/7 access to virtual customer assistants (VCAs), which include speech recognition, sentiment analysis and automated/augmented quality control.
  • AI in human resource: Use cases include recruitment (matching demand and supply or predicting success with recruitment) and selection of skills using NLP for consistent skill and job descriptions for next-generation match and search. HR also uses recommendation engines to find learning, content, mentors and career paths, and adaptive learning.
  • AI in finance: It includes reviewing expense reports, processing vendor invoices and complying with accounting standards.

AI in vendor management: While basic ML technologies can be used for contract classification and spend classification, more advanced use cases are emerging in areas like risk management, candidate matching, virtual purchasing assistance, voice recognition, and sourcing automation.

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How to build an AI app?

This step-by-step guide will show you how to build and use an AI app. Whether you are a researcher, business owner or just curious about AI technology, these instructions will help you navigate the steps of creating an AI system that can transform your industry.

Steps to build an AI app

Step 1: Problem identification

First, identify the problem to be solved before you build an AI app. Consider the functions and processes of the app in which you want to use the AI technology stack. What result should you expect from it? How will you benefit? Once you have identified the problem and the idea, you can start to create product requirements. Based on the requirement analysis, developers can understand the purpose of creating products and find technologies and tools to help them.

You will also need to do the following during the planning stage:

  • Decide the composition of the technical and non-technical team- from project managers and business analysts to data engineers and backend programmers.
  • Discuss your work schedule with professionals.
  • Start exploring the data needed to build an AI/ML model.

Step 2: Preparation of data

AI-powered apps are data-driven and typically require large amounts of data in order to function. However, prior to applying the data, it must be collected and prepared appropriately to create an accurate data model. AI labeling team of professionals specialized in AI, and ML-based software solutions can label the collected data. These software engineers carefully study the input information and sources to prepare the data for further use. They often use the Cross-Industry Standard Process for Data Mining (CRISP-DM).

The next step involves verifying the input data for any errors, missing values or incorrect labels and then preparing the data, which includes the following steps:

  • Uploading and selecting raw data
  • Selecting annotation tools
  • Labeling and highlighting the data
  • Processed data selection and saving in a file

Using the collected data, you can compare the solutions and move on to the modeling phase. The data previously collected is used to train the ML model via different methods.

Step 3: Choosing an algorithm

Now, we come to the core and arguably the most important part of building an AI system: choosing the right algorithm. While the technical details can be complex, it is important to understand the fundamental concepts involved in selecting the right algorithm for the task at hand. The algorithm can be of different forms based on the learning type.

There are two main types of learning: supervised and unsupervised learning.

Supervised learning involves providing the machine with a dataset on which it trains itself to provide the desired results on a test dataset. Several supervised learning algorithms are available, such as SVM (Support Vector Machine), Logistic Regression, Random Forest Generation, and Naive Bayes Classification. These algorithms can be used for classification tasks, such as determining the likelihood of a loan defaulting, or for regression tasks, such as determining the amount that might be lost if a loan defaults.

On the other hand, unsupervised learning differs from supervised learning because it does not provide the machine with a labeled dataset. Instead, unsupervised learning algorithms are used for clustering, where the algorithm tries to group similar things; association, where it finds links between objects; and dimensionality reduction, where it reduces the number of variables to decrease noise.

Choosing the right algorithm is crucial to building a sound AI system. By understanding the fundamental concepts of supervised and unsupervised learning and familiarizing oneself with the various algorithms available, you can ensure that your AI system is able to accurately and effectively solve the problem at hand.

Step 4: Training the algorithms

Training an algorithm after selecting it is critical to verify its accuracy. Although you can not set any standard metrics or threshold to ensure model accuracy, it is important to ensure that the algorithm works within the chosen framework through training and retraining until it achieves the desired accuracy. As an AI system is data-centric, its efficiency depends solely on the data performance. So, the data is expected to be diverse enough to make the model perform as expected. So, investing time and resources into training the algorithm is beneficial and a mandatory step. This, in turn, will result in increased efficiency, cost savings, as well as a competitive advantage.

Step 5: Choosing the best language for AI

A clear set of requirements is essential for building an AI solution. It also requires the right choice of technologies and AI programming language that will make it possible to help create intuitive AI systems offering users a robust experience. There are many programming languages available, each with its strengths and weaknesses. Depending on your specific needs, you need to select the specific programming language for your AI project. While some AI programming languages are great at processing large amounts of data and crunching huge numbers, others excel at natural language programming. You can determine which language is best suited for your project by understanding the strengths and limitations of each language. Here are some of the most popular programming languages to consider when building an AI app.

  • Python
  • Java
  • C++
  • R
  • Prolog
  • Lisp
  • Haskell
  • Smalltalk
  • Rust

Step 6: Platform selection

While creating an AI app, we often use a wide variety of frameworks and APIs to create smart AI algorithms easily. These frameworks and APIs come with in-built features of deep learning, neural networks and NLP applications. Almost all major cloud platforms for AI provide these AI platforms and APIs, which make it easy to implement ready-made solutions for speech, image and language recognition, as well as provide high-level abstractions of complex machine-learning algorithms.

These are the main factors that influence your choice of APIs and platform for AI:

  • Selecting your preferred cloud, e.g., a hybrid cloud.
  • Data storage location and ownership details.
  • The selected language limitations.
  • Availability of APIs in a particular region.
  • Cost of AI development life-cycle.

Tech stack you can choose for step 5 and step 6

Parameter Technologies and Solutions
Programming Languages Python, Java,C++,C#,R,Lisp,Prolog
Frameworks CNTK, AML, PyTorch, Core ML/Create ML, Caffe2, Keras, Scikit-learn, SparkMLlib, Keras, etc.
API and SDKs Azure Topic Detection, Microsft Face, Google Vision, SiriKit, etc
AI and ML platforms Google TensorFlow, Microsoft Azure, Amazon Machine Learning, IBM Watson, Oracle AI cloud, etc.

Step 7: Final development

As mentioned above, creating an AI-driven software application is similar to other software development, except for CRISP-DM. The following steps are integral parts of AI development:

  • Architecture design of the solution
  • Design of the user interface
  • Frontend and backend creation

Also, during development, you can optimize performance, expand functionality, and adapt the product for updates.

Step 8: Testing, Deployment and Monitoring

Once the development stage is over, you must test the product with the help of QA engineers. They can use automated, manual or mixed tools. You can deliver the app only if it has been thoroughly tested and functions as expected. Once the testing is done, the product must be deployed to the production server. Post-deployment, the support team offers regular maintenance to your solution in order to prevent data drift. AI maintenance is unique in that it requires continuous data and concept updates. This will ensure that your algorithm accuracy does not suffer any degradation, including regular updates like security patches and version changes.


AI’s deep learning capabilities enable it to perform tasks with a level of sophistication that closely mirrors human intelligence, rendering it an indispensable tool for driving successful business development. Its adoption is gaining significant momentum across industries, particularly as a means to improve customer satisfaction, a key factor in helping businesses gain a competitive edge in the market. The versatility and potential of AI are being increasingly witnessed in diverse domains, including but not limited to fintech, social media, and telemedicine, where it is creating new opportunities for growth and innovation. However, all types of AI development require deep experience and extensive expertise in the field, which needs professional intervention.

Although this article offers a comprehensive guide on creating an AI app, AI app development is a complex process that requires advanced technical knowledge in AI, machine learning and data science. Hence, you must hire a good AI development company with experience in end-to-end AI app development for guaranteed success.

Wondering how to build a high-quality AI app? Contact LeewayHertz’s AI specialists for your requirement; we build robust AI apps with advanced features.

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Author’s Bio


Akash Takyar

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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