How to build an AI app: complete development guide

If you think AI application development is a trend for which you have to get prepared, it's time to update your outlook: these technologies have already reshaped our world.

According to Forbes Advisor research, last year, 97% of founders believed artificial intelligence would help their business. And most of them were right: the recent study revealed that in 2024, 72% of organizations indeed adopted AI at least in 1 business operation. Take a look – and that’s not even all the possibilities!

This isn’t just a statistic, it’s a signal that AI is becoming a driving force in how we innovate and interact. AI app development isn’t just for tech giants anymore – it’s a game changer for businesses of all sizes. 

The hype around these smart technologies has skyrocketed demand, with everyone eager to launch their own startup with that coveted "AI" label. Now it's a dynamic sector where ideas can spark excitement one moment and fade into obscurity the next, so let’s explore how to build an AI app that not only creates a buzz but also makes a lasting impact! 

What is an AI app?

An artificial intelligence application is a software program that leverages machine learning, natural language processing, and other AI technologies to perform tasks that typically require human involvement. 

Unlike standard applications, which follow predefined rules and workflows, AI apps can adapt to new data, learn from user interactions, and improve their performance over time.

Why build an AI app now?

The time to invest in AI application development is now, and there are compelling reasons for this urgency. Market data shows explosive growth in AI technologies, with startups raising a staggering $23 billion in capital in 2023 alone. Over 200 AI companies have achieved unicorn status, valued at $1 billion or more, highlighting the immense potential for innovation.

"If you’re wondering how to create an AI app that will become truly competitive, understanding the landscape is crucial."

Ilia Kiselevich, CEO at SolveIt

Before you dive into building an AI app, take a moment to assess your resources, goals, and market demands. Many are eager to chase the allure and opportunities those two letters bring, but it's essential to avoid following trends blindly. A thoughtful approach to building AI applications according to your overall business strategy can resonate with the audience and boost operational efficiency.

So, are you ready to explore how to build an app with AI features that won’t just be a fleeting star?

Benefits of AI app development 

When it comes to building an AI app, the advantages are not just abstract concepts – they are tangible benefits that can transform the way you lead the business, the same way as your future product can enhance millions of people's lives around the whole world. 

• Automation of repetitive tasks

Some operations can be replaced or, at least, partly streamlined by AI. What does this mean for your business internally? Saving time, and consequently, money. What does this mean for your app's end users if we talk about the AI features? The same thing – the daily routine exhausts.

• Enhanced user experience

In a world where user expectations are sky-high, AI app development enables you to create personalized experiences that resonate with your audience. Tailored interactions foster loyalty, turning casual users into dedicated fans, which can drive the achievement of valuable KPIs for your app. 

• Data-driven decision making

Gone are the days of guesswork. AI empowers you to harness data insights, enabling informed decisions that align with market needs and user preferences. This strategic approach can set your AI application apart from the competition and prevent you, as an entrepreneur, from costly mistakes.

• Predictive analytics

What if you could foresee trends before they happen? AI's predictive capabilities allow you to anticipate user behavior and market shifts, giving you a proactive edge that keeps your venture, again, one step ahead by identifying potential product demands or predicting customer churn. 

• Scalability

As your user base grows, so do your demands. AI application development allows you to scale effortlessly by, f.i., automatic distribution of the server load or optimized search algorithms. As a result, your app handles increased traffic and improves performance without a manual hitch. 

• Robust security measures

In an era where data breaches are all too common, AI enhances your app’s security. With advanced threat detection and response capabilities, you can protect your users and build trust in your brand. Especially if we talk about finance or healthcare industry requirements.

Which industries use AI the most

As you explore potential tech startup ideas and consider how to build an MVP with AI functionalities, focus on sectors where such applications are more likely poised for success.

1. Healthcare: The industry is transforming through advancements in diagnostics, personalized medicine, and patient care management, creating opportunities for building AI applications that leverage predictive analytics and imaging tools.

2. eCommerce: B2B and B2C retailers are leveraging AI for inventory management, personalized shopping experiences, and demand forecasting. AI-driven recommendations help enhance customer engagement and boost sales.

3. Real estate: AI is transforming property valuation and market analysis. AI algorithms can analyze vast amounts of data to predict trends and optimize pricing strategies. Сhatbots streamline communication by providing instant responses.

4. Finance: In the finance sector, AI plays a crucial role in fraud detection, algorithmic trading, and risk assessment. Machine learning models sift through extensive datasets to identify patterns and mitigate risks effectively.

5. Manufacturing: Looking to optimize supply chain management, predictive maintenance, or quality control? As you explore how to build an AI app for the manufacturing business, consider using smart technologies to streamline production and reduce downtime.

6. Transportation: AI powers autonomous vehicles, enhances traffic management, and improves logistics. Companies use custom algorithms to optimize routes and reduce fuel consumption, making operations more efficient.

7. Marketing: Now it’s not enough to simply create a product or service; those who win have better marketing data and use it properly. Building an AI app that automates outreach, personalizes content, or predicts users behavior is a path to being noticed.

How to create an AI app: key elements and components

Building an AI app is like assembling a high-tech puzzle – each piece matters, and they all fit together to create something extraordinary. Let's break down the key elements and components that you'll need to craft your mobile or web product.

1. Machine Learning (ML)

Think of ML as the brain of your AI app. It’s the domain that enables systems to learn from data, adapt, and improve over time – without needing a programmer to hold their hand. This capability is essential for creating an AI app that evolves with its users.

2. Natural language processing (NLP)

NLP is the magic that lets machines understand human language. It’s the tech behind chatbots that can actually hold a conversation or algorithms that translate languages. Mastering NLP is crucial for ensuring your app can effectively interact with users.

3. Deep learning

If ML is the brain, deep learning is its brainiac cousin. Using multi-layered neural networks, deep learning processes vast amounts of data to uncover patterns that are often invisible to the naked eye. This is vital if you're intended to build an AI app that is advanced.

4. Expert systems

Expert systems are specialized programs that utilize extensive knowledge bases to solve specific problems. They mimic the decision-making process of experienced professionals in a particular domain.

5. Computer vision

Computer vision is your AI’s eyes. It enables machines to interpret and understand visual information – like recognizing faces in a crowd or identifying objects in a photo. This technology is key for any application that interacts with visual data.

6. Data collection

You can't build an AI without feeding it data. Data collection is the crucial first step, where you gather all the information your application will need to learn. This foundational step sets the stage for the entire development process.

7. Data security

AI app development should not lead to a machine uprising, so security is the number one priority here. It measures to protect sensitive information and ensure privacy in your app. Building robust security protocols is critical for user trust and government/industry compliance.

8. AI model training

Now comes the fun part: training your model. This is where you take all that data you collected and use it to teach your AI to make predictions or decisions. Mastering this process is key to creating an effective AI application.

9. Ethics and bias

Finally, let's address ethics and bias. As you consider how to create an AI app, it's essential to recognize potential biases in your data and algorithms. This awareness is crucial for ensuring fairness and accountability in your AI software.

AI app development: SolveIt experience

At SolveIt, our approach to AI development is centered on innovation, quality, and user-centric design. We leverage fitting your business model technology to create intelligent solutions that address your unique challenges. Our expertise in integrating AI capabilities is evident in our successful projects, including LifePal and Hemie.

AI productivity app 

LifePal is a productivity app designed to help users manage their daily schedules effectively. By integrating AI technologies such as ChatGPT and a custom algorithm, we created a personal virtual assistant that recognizes speech, formulates queries, and organizes plans. 

This project exemplifies our commitment to blending AI with user needs, offering features like automated scheduling and smart reminders. The development process involved:

• AI model training: We trained the AI model to understand user preferences and improve interaction quality.

• User-centric features: LifePal offers personalized insights by integrating with Apple’s pre-built utilities, ensuring that users have a seamless experience across their devices.

The result was a high-quality iOS and macOS MVP that achieved a 5-star rating on the App Store within months of launch, showcasing our ability to deliver effective solutions quickly.

Rental app with AI features

In the Hemie project, we focused on rescuing a property rental app from the previous provider that required significant enhancements. While stabilizing and enhancing performance was our primary goal, we also explored AI functionalities that allow you to select your dream apartment through personalized recommendations, much like a dating app.

Key aspects of included:

• Backend optimization: We shifted the app's logic to the backend, enhancing performance and allowing for the integration of AI functionalities that enable personalized property matches.

• Innovative features: The introduction of a third-party exchange feature allows users to rent properties among themselves, facilitated by AI-driven recommendations.

Our partnership resulted in a successful launch of cross-platform mobile apps for both iOS and Android, which led to ongoing work on the Hemie web app.

How much does it cost to build an AI app?

Let's delve into the most sensitive topic – what does app development cost in fact? It’s time to lift the veil and discuss concrete figures for an MVP and full-fledged solutions.

"As you decide to build an AI app, it's essential to consider several critical factors beyond initial development costs.

• Infrastructure charges: Leveraging cloud services to host your AI models can make or break your budget, so be prepared.

• Maintenance and updates: The journey doesn’t end at launch – continuous maintenance and updates are essential to keep your app running smoothly, and this requires money.

• Data acquisition: Gathering or purchasing quality data for training your AI models can also add to the overall investment.

• UX/UI design: Best practice dictates that development is a one-time process, so prototyping and wireframing must be included in your budget to visualize and test the functions.

Understanding these aspects will help in planning a more comprehensive budget for your AI application project."

Ilia Kiselevich, CEO at SolveIt 

MVP AI app development 

• An MVP typically includes essential AI functionalities, such as simple chatbots, basic data analysis, or straightforward machine learning models.

• The cost for developing such an MVP generally ranges from $35,000 to $60,000. This investment allows businesses to validate their concepts and gather user feedback early.

Full-fledged AI app development 

• A full-fledged AI application may incorporate advanced features such as personalized recommendations, sophisticated natural language processing, or computer vision capabilities.

• The development costs for these artificial intelligent applications can range from $80,000 to $250,000 or more, depending on the complexity and scale of the project.

To sum up, recognizing the impact of app design cost, alongside infrastructure, maintenance, and data acquisition, will enable you to formulate a thorough budget for your AI app development, staying on planned course.

How to build an AI app: step-by-step process

To successfully navigate the complexities of the development, it's essential to understand how to build an AI app through a systematic, step-by-step process that transforms the initial ideas into functional products.

Step 1: Define goals

Begin by clearly outlining the objectives of your AI application. What specific problem do you want to solve? Identifying the goals will guide your AI development process and help you measure success. Ensure your goals are measurable and aligned with user needs to create a focused foundation for your app.

Step 2: Research and plan

Conduct thorough discovery phase that includes research on the market landscape, user expectations, and technological advancements. Develop a strategic plan that includes timelines, milestones, and resource allocation. This will not only help you to step into the competitive environment in time but also clarify the features that will set your AI app apart.

Step 3: Data preparation

Data is the lifeblood of any AI application. Gather relevant datasets, ensuring they are clean, well-organized, and representative of your target use cases. This step involves data cleaning, labeling, and augmentation to enhance the quality and diversity of your data, which is crucial for training an effective AI model.

Step 4: Choose AI model, technologies, and tools

Select the appropriate AI model based on your goals and the nature of your data. Consider technologies and tools that facilitate the development process, such as machine learning frameworks (e.g., TensorFlow, OpenAI GPT) and cloud services (e.g., AWS, Google Cloud). The right combination will streamline the app development process and enhance performance.

Step 5: Design and train AI model

Craft your AI model by defining its architecture and parameters. Train the model using your prepared data, iterating on the design and adjusting hyperparameters to optimize performance. This phase often requires experimentation from your development partner, so be prepared to refine your model based on training outcomes and validation results.

Step 6: Develop an MVP

MVP development for startups are a valuable practice that can serve as an excellent starting point if you're considering how to make an AI app. This product development strategy helps focus on core functionalities while enabling quick feedback and effective resource management. MVP allows you to validate your idea and refine your app based on real user insights.

Step 7: Integrate AI model into MVP

Seamlessly integrate your trained AI model into MVP. Ensure that the model can interact effectively with the app's user interface and backend systems. This involves developing APIs and ensuring that the application responds promptly to user inputs, providing a smooth and engaging experience.

Step 8: Test and iterate

Conduct rigorous testing to identify any bugs or performance issues. Gather user feedback to understand how well the AI features meet expectations. Use this feedback to make iterative improvements, refining both the model and the user experience. This step is critical if you want to build an AI app that safeguards your reputation and ensures trust.

Step 9: Launch and monitor

Once testing is complete and adjustments have been made, launch your AI app to the public. Monitor its performance closely, tracking key metrics such as user engagement, accuracy, and response times. In the fast-paced startup landscape, staying responsive to user feedback is key to keeping your app on the top of users' preferences and ratings. 

Step 10: Improve and update

After your launch, dive into a cycle of continuous improvement. Regularly update your AI model with fresh data and fine-tune algorithms based on user feedback and emerging trends. A strong AI development partner will provide ongoing support, ensuring your app not only performs at its best but also keeps users excited with new features.

Step 11: Ensure ethics and data privacy

Throughout the AI development and after the app's launch, prioritize ethical considerations and data privacy. Implement robust security measures to protect user data and ensure compliance with regulations. When users feel confident that their data is handled responsibly, they're more likely to engage with your app and advocate for it. 

Final words

AI has become the magnet that every startup founder chases, convinced it will elevate their solutions. Yet, the reality is that sometimes traditional methods are just as effective. Only a trusted development vendor can communicate this openly, offering honest options tailored for your business.

We understand that speed dictates the market – what you have in mind may already be in the works by competitors. That’s why we specialize in developing MVPs with integrated AI features, enabling our clients to quickly validate their product ideas and launch in record time. 

At SolveIt, we don’t aim to create just another item in our portfolio; we genuinely want your users to receive real value and the overall project to be successful. If this approach resonates, welcome to create an outstanding AI app together! 

FAQ: How to build an AI app 

1 | How to build an AI app?

To successfully build an AI app, follow this step-by-step process:

1. Define your goals. 

2. Research and plan.

3. Prepare your data.

4. Choose AI model, technologies, and tools.

5. Design and train AI model.

6. Develop an MVP.

7. Integrate AI model into MVP.

8. Test and iterate.

9. Launch and monitor.

10. Improve and update.

11. Ensure ethics and data privacy.

2 | What are the key elements of an AI app?

The key elements of an AI app include:

1. Machine learning (ML).

2. Natural language processing (NLP).

3. Deep learning.

4. Expert systems.

5. Computer vision.

6. Data collection.

7. Data security.

8. AI model training.

9. Ethics and bias.

3 | How much does it cost to build an app with AI?

The cost to build an AI app varies based on several factors. Here’s a breakdown:

• MVP AI app. Developing an MVP with essential AI functionalities, like simple chatbots or basic data analysis, typically ranges from $35,000 to $60,000. This allows you to validate the idea and gather valuable user feedback. 

• Full-fledged AI app. For a more advanced AI application featuring personalized recommendations, sophisticated natural language processing, or computer vision, costs can range from $80,000 to $250,000 or more, depending on complexity and scale.
  

4 | How long does it take to make an AI app?

The timeline for creating an AI app depends on several factors, such as the complexity of the AI model, the quality and volume of training data, and the specific app goals.

• Basic AI features (e.g., image recognition, chatbots): 3 to 6 months.

• Medium complexity (custom AI models): 6 to 12 months.

• High complexity (extensive research or specialized models): 9 to 12 months.