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How to build an AI app from start to finish

Whether you are a startup or an established business looking to integrate AI into your operations, read on to discover valuable insights that will help you get started.

15 March, 2023
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We need to acknowledge that Artificial intelligence has become one of the most promising technologies of the 21st century, and it's rapidly transforming industries across the globe, especially those actively working to stay ahead of the competition.

Look at industries like healthcare or transportation, proving how much potential AI has to revolutionize how we live, work, and interact with the world around us.

Since more and more businesses and entrepreneurs are now looking to build AI applications to improve their operational efficiency or enhance customer experience, we want to discuss the steps involved in creating an AI-powered app.

So, whether you are a startup or an established business looking to integrate AI or chatbot functionality into your operations, read on to discover valuable insights and practical tips that will help you get started.

Reasons to integrate Artificial Intelligence into your business

Let’s first explore why building an AI-powered application is crucial for companies in today's digital age. AI itself is a broad term that refers to the simulation of human intelligence in machines that are programmed to learn from data and perform tasks that typically require human-like intelligence.

From virtual assistants to chatbots, image recognition to natural language processing, AI-powered applications practically dominate today's digital landscape. According to a recent report by Grand View Research, the global AI market size was valued at USD 136.55 billion last year and is expected to reach a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030.

Benefits of integrating AI

Using AI in an application can provide numerous advantages for businesses and their applications.

One of the most significant benefits is improved efficiency and productivity. Companies can automate routine tasks and processes using AI-powered applications, freeing employees' time to focus on more critical tasks requiring human input.

Another obvious benefit is better customer experience. Having the ability to analyze large chunks of data, AI gives a unique chance to provide more personalized recommendations. For example, an e-commerce platform that uses AI can recommend products to customers based on their previous purchases, browsing history, and preferences.

Other benefits include enhanced accuracy and precision when analyzing data that, in turn, reduces errors and increases the quality of results, as well as cost savings and competitive advantage by delivering better products and services.

Where is AI used the most today?

There are 5 fields where applications powered by Artificial Intelligence are used the most nowadays:

Fields AI used the most today
Fields AI used the most today
  1. Healthcare

AI-powered applications are used in healthcare to improve patient outcomes, reduce costs, and enhance the quality of care. For instance, AI-powered diagnostic tools can analyze medical images to detect abnormalities and assist clinicians in making accurate diagnoses, while AI-powered chatbots can deliver 24/7 assistance to patients, helping them to manage their conditions and provide timely advice in case of emergencies.

  1. Finance

The finance sector uses AI to spot patterns and trends in financial data, make predictions, and automate routine tasks. For example, chatbots already assist customers in making transactions, managing their finances, and providing personalized financial advice. In fraud detection systems, AI can analyze large volumes of data to identify fraudulent activities and prevent economic losses.

  1. Retail

Retail benefits from AI through personalized customer experiences, inventory management optimization, and supply chain efficiency enhancement. Recommendation engines analyze customer data to make customized product recommendations, while AI-powered inventory management systems analyze sales data to predict demand, optimize stock levels, and minimize waste.

  1. Manufacturing

Next is manufacturing, where AI is already known to optimize production processes, reduce costs, and improve product quality. Examples: predictive maintenance systems that analyze data from sensors and other sources to detect equipment failures, AI-powered algorithms that make predictions to guide design decisions and optimize product performance, and quality control systems that prevent faulty products from reaching the market.

  1. Education

Finally, education - another field that has massively benefited from AI integration, with examples being: AI-powered adaptive learning platforms that analyze student data to identify knowledge gaps and provide personalized recommendations for further study, chatbots that provide 24/7 assistance to students, and algorithms that analyze data from scientific studies to make predictions and guide the development of new treatments and therapies.

Designing the AI model

Phase one of building your own AI-powered application is the process of designing the AI model. Let’s go through the steps required for this part.

Define the problem you’re solving

The first step in building an AI-powered app is to define the problem your app will solve and determine if AI is the best solution. This will require a thorough understanding of the problem domain and knowledge of the various AI techniques and algorithms that can be applied to the problem.

The choice of AI technique will depend on the nature of the problem, the type and volume of data available, and your desired performance metrics.

Frame the issue in a way that allows for creative solutions and the exploration of multiple approaches - for example, a data-driven approach to problem-solving or using techniques such as reinforcement learning or deep learning to discover patterns and insights in the data.

Collect the data and choose the algorithm

Collecting and organizing relevant data for your AI model will need acquiring and preprocessing large volumes of data, which may come from various sources and in multiple formats, and you might need to use specialized techniques such as data augmentation, normalization, and feature engineering.

You might collect data from multiple sources or generate synthetic data using techniques such as generative adversarial networks (GANs). Ultimately, the goal is to collect and prepare a high-quality dataset that can be used to train and validate the AI model.

Then you need to choose the algorithm that best suits your problem - for example, supervised, unsupervised, or reinforcement learning. The chosen algorithm should be capable of generating diverse and varied outputs that can capture all the nuances and complexities of the domain.

This part may also require specialized algorithms such as recurrent neural networks (RNNs) or attention mechanisms that can handle variable-length inputs and generate outputs that vary in length and complexity.

Train your AI model with data

The next step in building an AI-powered app is to train and optimize the AI model using the selected algorithm and the prepared dataset. You’ll need to feed the dataset into the algorithm and adjust its parameters to minimize the error or loss function, which measures the difference between the predicted and actual outputs.

Design the training process very carefully to promote exploration and variability in the model's outputs while maintaining accuracy and consistency. You may be required to use techniques such as dropout, early stopping, or adversarial training to prevent the model from becoming too rigid or biased.

Build the app

Phase two is building the application itself. You need to go through the following processes.

Choose a development platform and your tech stack

When it comes to choosing a development platform, consider the complexity of your app, the size of the dataset, the level of your dev team’s expertise, and your scalability requirements.

TensorFlow is one of the more widespread choices for building complex neural networks, while PyTorch is known for its ease of use and flexibility.

Keras is another user-friendly tool that offers a high-level interface for building deep learning models, and Caffe is a fast and efficient platform ideal for building image and video recognition applications.

Building an AI-powered application also requires a specific tech stack that includes programming languages, SDKs, and toolkits to enable the application's functionality. Here are a bunch of helpful tools:

  • Programming languages like Python, Java, R, and C++;
  • Natural Language Processing (NLP) libraries - Natural Language Toolkit (NLTK), Stanford NLP, and Spacy;
  • Cloud computing platforms - Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform;
  • Data Storage and management tools - MongoDB, Cassandra, and MySQL;
  • Integrated development environments (IDEs) such as PyCharm, Spyder, and Visual Studio Code.

Integrate the AI model

This next step of building an AI-powered app is all about integrating the trained AI model into your app's architecture and user interface. You must design and implement the software components that interact with the model, such as data input and output, preprocessing, and postprocessing. The model may also need to be optimized for deployment, such as reducing its size and memory requirements, to ensure it can run efficiently on the target platform.

Integrate the model, allowing for user input and feedback, as well as for creative exploration and experimentation with its outputs. For example, you can design interactive and adaptive interfaces that allow users to customize your app's behavior and preferences or use techniques such as reinforcement learning to enable your app to learn from user interactions and adapt its behavior over time.

Test the application and deploy

First, test and validate your app's performance and functionality by evaluating its accuracy, reliability, and usability under different scenarios and conditions. The testing process may include using synthetic or real-world data and manual or automated testing techniques such as unit testing, integration testing, and acceptance testing.

You can also include test cases that challenge the app's capabilities, explore its potential weaknesses, and solicit feedback and suggestions from users and domain experts to improve the app's performance and functionality.

And finally, deploy and maintain the app in the production environment. Begin by preparing the app for deployment, such as packaging it into a container or a virtual machine and deploying it onto the target platform, such as a cloud service or a mobile device.

You’ll also need to set up infrastructure and resources, such as servers, databases, and security measures, to ensure the app runs reliably and securely in the production environment.

Don’t forget to continuously monitor and optimize your app's performance and behavior while also adding timely updates and enhancements to address issues and incorporate new features and feedback.

Examples of AI applications

Currently, virtual voice-activated assistants like Amazon's Alexa, Microsoft’s Cortana, and Google Assistant are among the most widely adopted AI applications, using natural language processing and machine learning algorithms to understand user queries and provide personalized responses.

Another popular use case for AI is image and object recognition, often utilized in self-driving cars and facial recognition technology. Additionally, chatbots and conversational agents are becoming increasingly common in customer support, enabling companies to provide 24/7 assistance to customers and improve response times.

For example, today’s most famous AI language model, ChatGPT, assists with generating human-like responses to natural language prompts for various applications, such as said chatbots, customer service, and language translation.

Since it’s one of the best AI language models currently available, various organizations and individuals use it to generate text for applications like writing news articles, analyzing customer feedback, etc.

Here are some examples of mobile and web applications that employ AI as a basis for their solutions:

Mobile and web applications that employ AI
Mobile and web applications that employ AI 
  • Grammarly - a writing assistant application that uses AI to detect grammatical errors, suggest synonyms and provide writing tips to enhance the quality of the text.
  • Duolingo - a language learning application that uses it to personalize learning paths and provide real-time feedback on pronunciation and grammar.
  • A personal finance application, Mint, has it categorizing transactions, tracking expenses, and providing personalized financial advice.
  • Waze - a navigation app that utilizes AI to optimize routes based on traffic data, user reports, and historical traffic patterns.
  • A meditation app called Headspace uses AI to provide personalized meditation sessions based on user feedback and preferences.
  • Canva - a graphic design app that uses AI to suggest design elements and layouts based on user preferences and content.
  • Adobe Sensei - a suite of creative tools that utilize AI to automate mundane tasks, such as removing backgrounds or colorizing images, and to suggest creative enhancements and optimizations.
  • And the last one on our list today is a photo-editing app called FaceApp that uses this technology to alter the appearance of faces in photos, such as adding a smile, changing the hair color, or making someone look older or younger.

How to choose an AI development company

Choosing AI companies can be overwhelming, given the rapidly evolving landscape of AI technology and the diverse range of companies offering AI services. Key factors to consider:

  • Look for a company that has a proven track record of developing AI-based solutions and has expertise in your specific industry or domain;
  • Make sure that the company has the technical capabilities required to build an AI-powered solution, such as expertise in machine learning, natural language processing, computer vision, and deep learning;
  • Ensure that the provider has robust data security protocols and adheres to industry standards such as HIPAA, GDPR, and CCPA, depending on your requirements;
  • Look for a partner that values your input and feedback, provides regular project updates, and can work with you to refine the project scope and requirements;
  • And last but not least, consider the company's pricing and project timelines - ensure they provide a transparent pricing model and deliver the project within the agreed timeline.

Did you know it's also possible to integrate a ready-made API into your business application instead of training your own AI model? In fact, many businesses choose to do this because it can save them a lot of time, money, and resources. First, you need to find a provider that offers the API functionality you need. By the way, Merge will be able to help with that soon.

Overall, many providers offer already pre-built AI models and APIs, and once you have chosen one, you will need credentials to access the API. This usually involves signing up for an account and following their instructions to get a key. After that, you integrate the API into your application.

Conclusion

Building an AI-powered application can be rather tricky, but the benefits far outweigh the challenges.  AI can bring advanced capabilities to your application, provide more value to your users, and increase business revenue.

Since AI-powered applications will continue gaining momentum and drive innovation and transformation across various industries in the coming years, we have closely followed this trend.

And as a design and development agency, we are committed to staying at the forefront of this exciting field. That’s why we are excited to announce that we will soon be launching AI-based services. So, stay tuned!

author

CEO and Founder of Merge

My mission is to help startups build software, experiment with new features, and bring their product vision to life.

My mission is to help startups build software, experiment with new features, and bring their product vision to life.

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