How to Build an AI App: The Ultimate Guide

It seems as though AI is all over the place recently, right? Well, to be fair, artificial intelligence is revolutionizing various industries and is forecasted to shape the future.

Although the AI market is already quite crowded, the field has lots of potential business-wise. But it's not just about coming up with cutting-edge algorithms. You have to think big and aim precisely at harnessing the power of artificial intelligence to tackle complex problems unsolved before. This requires specific industry knowledge, in-depth market research, and an experienced team of developers to make it happen.

But let's take it one step at a time. If you have an idea for an AI-based application that can defy the boundaries of innovation but aren't sure how to approach its creation, this page holds answers to many of your questions. Keep reading to find out whether AI app development is worth it now and learn how to develop AI applications with tips from Upsilon's CEO and CTO.

What Is an AI App?

Artificial intelligence refers to any software or machine that mimics human intelligence. Applications using AI are powered by artificial intelligence algorithms and use them as one of their fundamental movers. The AI apps' possibilities are virtually limitless, and this is a transformative area of technology with broad usage.

Such platforms, software, or apps apply AI to perform various tasks that usually require human input. Artificial intelligence app development leverages various AI techniques like machine learning and natural language processing for data analysis, allocating patterns, making predictions, or interacting with people.

What are the aims of AI software development? Mainly to augment human capabilities. AI can be applied to automate processes and provide intelligent solutions to problems.

The best part is that artificial intelligence-based applications are designed to learn. They use data and are capable of improving over time as they adapt to changes and figure out how to cater to the preferences of users better. This self-improvement allows them to deliver better results, making the technology more sophisticated over time.

Where Is It Applied? (Popular AI Industries)

Artificial intelligence can be used in lots of ways. In fact, people are getting more and more creative about tech startup ideas in the sector and how else to make use of AI's capabilities. In which industries is AI applied? Here are some domains where you can find AI apps:

• Healthcare
• Finance
• Business and legal services
• Logistics, supply chain, and manufacturing
• E-commerce and vendor management
• Education
• Construction
• SaaS (tools for marketing, human resources, customer support, analytics software, cybersecurity, and so on)
• Transportation (e.g., autonomous vehicles and delivery drones)
• Environmental science

These are just some of the spheres where artificial intelligence can bring value. What kind of solutions are already out there?

• digital voice assistants and smart virtual assistants such as Siri;
• speech recognition and smart speakers like Alexa on Amazon Echo;
• chatbots and personal tutors like Elsa;
• biometric tools like face or fingerprint recognition;
• image generation tools such as Midjourney and editing tools;
• text generation and language translation tools;
• augmented and virtual reality (for gaming, virtual try-on, virtual reality for deaf, and other use cases);
• music creation tools;
• sales acceleration solutions (e.g., personalized shopping suggestions);
• and plenty of other solutions.

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Benefits of AI to Note

Why do startups build AI apps? And what makes this technology so attractive for entrepreneurs and enterprise-sized businesses? There are multiple advantages of creating AI software. Here are a few notable AI benefits.

• Enhanced efficiency and productivity — Artificial intelligence allows for automating various repetitive work and routine tasks, it speeds up different processes and operations, upgrading product functionality and letting people save time and cut costs.‍
• Improved experience and higher revenue — AI helps users achieve their goals faster and more effectively. As such, personal recommendations and personalized assistance can noticeably boost user satisfaction and lead to better sales.
• ‍Large data volume processing — AI apps can handle lots of data and crunch up numbers rather quickly, plus they continuously learn.‍
• Less bias and better decision-making — processes handled by AI also mean fewer human errors and unbiased decisions backed by data.‍
• Seeking opportunities and trends — better, more realistic predictions and spotted opportunities thanks to in-depth data analysis that could have otherwise been missed by the human eye.

By the way, Upsilon has been interviewing many aspiring entrepreneurs and startup founders. Our collection of Startup Stories has many inspiring interviews with founders who are currently building AI products or integrating AI into their solutions. They share insights about their journey, noting the ups and downs and challenges, give first-hand tips on how to build AI apps, and tell about the lessons they've learned while launching and growing their business.

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Is It Worth Building AI Applications in 2024 and Beyond?

With the overall startup failure rate being extremely high over the past few years, entrepreneurs wonder whether AI is worth a shot. Artificial intelligence app development has definitely been trending. As we've mentioned, AI adoption is widespread in lots of industries, and its implementation is only expected to continue growing.

Recent statistics on the market size of AI are very promising, with an expected 826.76 billion USD by 2030 at an annual growth rate of 28.46%. The same resource suggests that the generative AI market size is expected to scale the most: from 36 billion USD in 2024 to around 415 billion USD in 2027, and to as much as 184 billion USD in 2030.

What about raising funds? Surely, global monthly average funding has visibly dropped since its peak in 2021: by 62%. Yet, according to the latest findings, there has been a large interest in the AI sector from investors, in fact, a much bigger interest than in any other sector. Global VCs provide a large share of startup funding to such companies. For instance, over 4.7 billion USD was given to AI companies in February, that is more than one-fifth of the total venture funding investment that month. Impressive, huh?

So, is building AI software of your own worth it? Looks like the future of AI is optimistic. It's an auspicious field to invest in, provided you create a quality product that brings value and that people actually need, of course. We'll overview how to make AI software in detail later on on the page.

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Key Components of AI

What are the components of AI? Artificial intelligence is based on several primary pillars. Let's overview them a bit closer.

Learning

As briefly mentioned, AI is designed to learn practically without continuous human input or programming to teach it. It labels data, looks for patterns, and uses trial-and-error as well as other methods to keep improving its skills and abilities to solve problems. It notes what worked and what didn't to perform better if it encounters similar cases in the future.

AI can enforce its features by receiving feedback (for instance, ChatGPT analogs request thumbs-up ratings or feedback to assess replies to prompts and questions). AI can also rely on rote learning, meaning that the AI model memorizes, stores, and then reproduces information without fully getting to the bottom of the topic.

Decision-Making and Reasoning

How do AI products make decisions? They rely heavily on logic. To come to conclusions, they need algorithms or probability models that'll help achieve rational results. These let AI distinguish what it's looking for keeping in mind the given situation. Hence, it uses reasoning to deduct, infer, and reach its "key takeaways".

Solving Problems

Although artificial intelligence is a rather universally applicable tool, problem-solving is its essence. "I've got a problem," you say. "Challenge accepted!" replies AI. It sees your problem as an unknown value it needs to find a solution for. It analyzes data to understand the intent and the possible moves.

For example, it could apply the special-purpose method to solve a specific problem. It's similar to what a car navigator app does: it analyzes the most recent traffic data in real-time to modify the route based on the changing circumstances to help you reach your destination most optimally.

Perceiving

Artificial intelligence is also capable of perception, that is using AI "sense organs" for scanning. This is exactly what autonomous delivery robots or self-driving cars do: their AI system scans the environment to examine and map it to understand which objects surround them. Likewise, image detection, face recognition, or analysis work based on perception too. 

Understanding Language

Plus, artificial intelligence is perfecting its ability to comprehend human languages, identify signs, and get what is implied. It already has quite an impressive command of so-called "Human English" in both text and voice. Who knows which languages it'll master next?

Major Elements of AI

What are the subfields of AI? And which elements is it comprised of? Let's go over the main artificial intelligence branches also called subfields of AI.

Machine Learning (ML)

Machine learning is one of the subfields of artificial intelligence which aims at imitating human intelligence. ML uses historical data to learn new patterns, make decisions or predictions, and classify information. It develops models and algorithms written in machine-readable language which help it learn.

This technology is widely applicable. It has been used in various apps, including those for image recognition, personal shopping recommendation functionality in online stores, or self-driving vehicles, which all need to be adaptive in order to progress.

ML is capable of unsupervised learning and can self-improve (that is, it doesn't necessarily require "training" from humans via intentional programming). Likewise, it can be trained, there are several approaches:

• supervised learning (when a machine receives labeled data for training);
• reinforcement learning (when it follows a specific step-by-step sequence with set rules and draws conclusions based on received signals that are either positive or negative). 

Neural Networks and Deep Learning (DL)

Deep learning (DL) is a subset of machine learning. It applies artificial neural networks (ANNs), which are somewhat similar to how neurons transmit signals to each other within the human brain. The nodes (a.k.a neurons) process and pass information through multiple layers of the network.

DL is rather effective at solving complicated tasks and extracting necessary information from data and statistics. It can also be highly self-sufficient when it comes to learning, yet requires really big datasets in order to do the trick. As such, DL is commonly used in AI application development for cybersecurity, various forecasting, or to enhance the NLP used in assistants like Alexa by Amazon.

Natural Language Processing (NLP) and GPT

Speaking of NLP, natural language processing revolves around teaching the system to understand written and verbal human language, interpret it, and generate responses to queries. NLP algorithms and models are designed to analyze data in the format of human text and speech, analyze sentiment, and communicate. For instance, text and speech recognition technology is applied in AI chatbot development and in voice assistants, as well as for developing AI for detecting spam.

Everyone has used or at least heard of GPT-3 developed by OpenAI, right? Well, it's a component of NLP or can be considered its tool. Generative Pre-Trained Transformer (GPT) is a type of large language model (LLM) forming one of the bedrocks of generative AI. It uses transformer architecture as its basis and is pre-trained with the help of large volumes of data and unlabelled text, which makes it possible for it to produce human-like content relevant to the context. As an example, it's applied in AI app development for systems that answer questions (often in a chat format), for content generation, making text summaries, or translations.

Computer Vision

Similarly, computer vision is all about systems that interpret visual information like images, photos, or videos. Such systems are trained to see what's depicted on a digital image, detect standalone objects, and perform advanced image classification. Lots of applications are already using this technology, including apps that are fitted with photo face recognition, image search, or tracking objects. Plus, it is widespread in robotics and augmented reality as well.

Robotics

As follows from the name, this AI technology concentrates on creating intelligent robots that perform autonomously or at least partially on their own. This implies the usage of not only data but the ability to manipulate physical objects, navigate, and communicate. They are widespread in multiple industries and have many use cases from manufacturing and retail to healthcare, space exploration, and beyond.

Expert Systems

Finally, expert systems are connected with solving complex problems in a specific niche or field like an industry or domain expert would. The system uses logic, rules, and a knowledge base to give expert-level recommendations or advice. This type of AI also requires very big data sets that are highly distinctive of the sphere. For example, the expert system could attempt to solve a scientific dilemma or programming problem or be used for financial or legal guidance.

How AI Works: A Simple Explanation

Before we move on to how to build AI software, we need to figure out a few basics. So, how does AI work? If you plan on building an AI tool, you'll need: 

1. lots of data (a must for training your solution);
2. algorithms (code to instruct the system and teach it to work with data).

At a basic level, an AI workflow works the following way:

1. You input data sets to train AI and get it to perform. Data should have defined context and indicated expected outcomes.

2. AI then processes the data, trying to allocate patterns and understand the given data.

3. The system then produces outputs that determine whether the data outcome was successful or not and measure accuracy.

4. In case AI has predicted a failed outcome, it makes adjustments or iterations to improve the process, learns through mistakes, and takes another shot at the process.

5. When the work is completed, AI assesses it by analyzing data to make predictions and draw conclusions.

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How to Create an AI App: Step-by-Step Guide

Let's learn how to make an AI app. These are the basic steps to follow during AI application development, yet the actual process may vary from project to project.

Step 1: Determining the Problem

Before you start creating app AI software, you have to be sure about which problem you're planning to tackle.

• Who faces this issue?
• Does the problem truly exist?
• Why do you need to create AI software to solve it? 

To find out, you'll need to go through proof of concept. In this event, an AI product is no different than any other kind of digital product.

After lining out the target audience that has this problem, writing out a product problem statement, and noting which solution you have in mind, it is also important to define your goals and objectives. What are you trying to achieve? Which metrics and KPIs can help you determine whether you're succeeding? Finding answers to these questions is just as important as deciding how to develop an AI application optimally. 

Step 2: Research and Planning

To be solid that this undertaking is worth it, you'll also have to conduct a thorough competitor analysis and do market research. This is time-consuming work but it is integral to do it at the earliest stages of the product development life cycle.

• What's the situation like in today's market?
• Which solutions are already available on the market?
• How do potential competitors solve this problem?
• Which technology do they use?
• Is there a chance that your idea will be brought to life by some established big players?

If you've found data and insights proving that your idea deserves development, you can proceed to other discovery phase and project planning vitals. As such, you can jot down the major product requirements, the approximate team composition required, the resources you have, and possibly even mark the milestones of the project timeline.

Step 3: Prepare Necessary Data

Your AI model, regardless of how intricate it is, won't be able to learn well if you don't have quality data. This means that you need to collect and prepare enough data for the AI to train effectively. And quality may be much more important than quantity at this point.

Mind how well-structured and properly formatted data is when forming the initial datasets for your AI app. It shouldn't have missing values, errors, or mistakes in labels. You can even browse data exchanges to find or mine relevant datasets that will be specific to your use case.

Step 4: Choose the AI Technology Stack and Tools

Selecting the best-fit AI tech stack and tools to build your application is also crucial. Which programming languages are optimal? Are there AI platforms, frameworks, libraries, third-party apps, integrations, or other existing solutions that can simplify your development process and free you from complex custom coding?

There are lots of approaches to how to develop an AI app from a technical perspective. You have options here:

• For instance, there exist NLP libraries in some programming languages, such as the Natural Language Toolkit (NLTK) in Python.
• Mentioning a few AI frameworks, Google AutoML, TensorFlow, or PyTorch are quite popular options.
• You can also consider platforms with ready-made stuff for your AI app development like Amazon's AWS machine learning models, Google's platform with an AI hub and building blocks, or the built-in AI capabilities and machine learning services of Microsoft Azure.

Applying these can save a lot of time on work that would otherwise take a lot of time to handle from the ground up. What is more, choosing in favor of cloud-based infrastructure can also let you be more flexible in the long run.

Which tech stack is common for AI application development? We've put the popular options for a technology stack of an AI application in this table for convenience.

Step 5: Build the MVP and Train the AI

Now to the actionable step of how to create an AI application. Deciding in favor of a minimum viable product (MVP) instead of enrolling in a large-scale project is a path commonly chosen by entrepreneurs. Such iterative development provides the needed flexibility to gradually build your product from its smallest, earliest version, improving it as you go.

At this point, you work on information architecture, as such, it is considered a best practice to opt for modular architectures, as they usually enforce the app's scalability. Teams also create the MVP design that's followed by feature development and security enhancement. Moreover, you might need to add embeddings or other functionality based on the peculiarities of the built app.

Simultaneously, the team also creates algorithms to train AI, for instance using model learning. The optimal approach to apply (unsupervised, supervised, or reinforcement learning) depends on the specifics of your solution and your goals. To train the model, in the course of time, you'll need to feed it the data you've prepared, ensure the parameters work as intended, and tweak them. Then, test the model's performance to evaluate how well it works, possibly falling back on the KPIs and metrics you chose earlier. The model gets trained and adjusted until it delivers the expected results.

Step 6: AI Integration and Testing

When developing an AI app, the solution gets thoroughly QA tested in terms of performance. If it's ready to go, the team moves on to integrating the created and trained AI into the system's front-end or back-end, which is often done with the help of APIs.

Conducting unit tests, integration tests, and user acceptance tests are common. But model testing isn't a one-time task, of course, it will continuously be refined over time. This is why it is common for teams to implement CI/CD pipelines that make it simpler to maintain the apps and run tests. Developers also usually link up a feedback loop to allow users to help the AI system improve.

Step 7: Release and Improvements

If everything works well, it's time for release and MVP launch. What happens afterward? The after MVP stage includes revision, fixing any issues that come up, optimizing performance, and improving the solution. This includes adding new features in consequent sprints or expanding the existing functionality that is next in the plan.

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How Upsilon Approaches AI App Development

Which best practices does Upsilon's team use when we build AI apps? Here are some good-to-knows and expert tips shared by our CEO, Andrew Fan, and CTO, Nikita Gusarov ⤵

Data Preparation for AI Training

What can't AI software development do without? Data, of course. Upsilon's CEO, Andrew Fan, notes the importance of preparing data for AI projects in advance.

"Let's assume you'd like to work on a project that'll allow you to take a photo of a paper receipt and automatically transfer the information into your CRM or bookkeeping system with the help of AI. To bring this to life, you'll need to have lots of examples of various receipt images that could possibly be used within your system, or at least know where to find many of them to train your AI. You also have to be certain about which data you want pulled from each receipt, as without this combo, you won't get far."

So it's not just about how to develop AI software, it's a lot about how to gather the most relevant data as early on as you can. The technical side may get rather complicated, however, without enough quality data from the outset, it'll be very difficult to train the AI to be effective at delivering what you expect.

Robust AI Testing Pipeline and Metrics

What is another fundamental step of early AI app development? Working on a solid AI testing pipeline and choosing your metrics. Here's what Upsilon's CTO, Nikita Gusarov, points out:

"When you're building an AI system, you should start with the AI testing pipeline. Way too often, teams make the critical mistake of attempting to improve their AI without using data and numbers for backup. You'd definitely want to avoid this. To do it the right way, you must have proper AI metrics to evaluate the quality of your AI. You can then try to enhance the AI, yet you have to collect data and compare it to the previous metrics to draw realistic conclusions. If the metrics improve, you're on the right track. But without meaningful AI metrics, all your improvements could be a complete waste of time and effort."

Nikita also gave an example describing the process if we hypothetically want to classify documents using ChatGPT. Let's say that we have various types of documents like invoices, tax declarations, and so on. You're aware of which types should be assigned to specific sets of documents. But how do you assess how well your classification works? One way to go is using the Confusion Matrix, which means that to test precision you'll need to calculate:

• how many documents were classified as a certain document type correctly (true positive);
• how many documents were mistakenly classified as a certain document type (false positive);
• how many documents weren't classified as a certain document type at all by mistake (false negative).

Based on these three figures, you'll calculate the fourth measure called the F-score. This procedure has to be done for each available document type, and as a result, you'll get a number showing how well the classification works for each type. Then, calculate the arithmetic mean value (or some other average of all measures) to get a single number from 0 to 1. The higher the number, the better the performance of the classification. This way, you can improve the AI using a numerical indicator as guidance, instead of proceeding blindly.

MVP Development for AI Products

How about developing minimum viable products? Upsilon has been building MVPs for over a decade now, including MVPs with AI, so here are a few points our CEO and CTO believe are worth noting in this respect.

"If you're planning to build a SaaS MVP, in most cases, it makes sense to focus on solving specific user problems using large language models (LLMs) that already exist. A good example of such AI algorithms is ChatGPT which uses large data sets and deep learning.

Why is attempting to build your own robust AI for your MVP most likely going to be a mistake? For one thing, there's a current tendency for all LLMs to become a commodity. Today the best one might be ChatGPT, so you can take advantage of it and save time. In a few years, it could be something else you can switch to."

With MVPs, your main aim is to hit the market with a quality working product as soon as you can, even if it has limited functionality. Therefore, when building an AI app, it is wise to give feature prioritization due thought as well as carefully consider where you can cut corners in terms of development.

Major Takeaways on How to Build an AI App

There, now you know more about how to create an artificial intelligence app. In general, AI has a promising future. VC interest in the sector as well as the market forecasts for the upcoming years are rather inspiring, which explains why many entrepreneurs are hurrying to jump on the AI train.

If you have a great AI-based application idea but need some assistance with the tech side, Upsilon will be glad to lend a hand. Our expert team has been providing MVP development services for over a decade, and we've helped bring numerous AI products to life. So, feel free to reach out to discuss your needs!