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AI applications are becoming increasingly important in every modern team’s toolkit. They are transforming the way businesses operate across various industries.
We’ve all witnessed the meteoric rise of OpenAI’s ChatGPT since its launch in November 2022. In the first 5 days a million users downloaded the app. Crazy, right?
Meanwhile, the global AI software market is projected to grow at a CAGR of 21.43% from 2025 to 2034. AI applications are truly reshaping how businesses and individuals work: boosting productivity, enhancing decision-making, and providing a competitive edge.
However, developing an AI application can be a daunting challenge. In this guide, we simplify the AI application development process by walking you through each step. We’ll also explain the different types of AI applications and later discuss the hottest trends shaping what's next in AI application development.
By the end, we hope you’ll feel confident to embark on your own AI application development journey... and soon enough share your first AI app with us and the world 😀
AI application development is the process of creating software applications that use artificial intelligence algorithms to mimic human intelligence. These AI solutions don’t necessarily erase humans from the loop but minimize their intervention.
AI software excels at handling complex and time-consuming tasks like pattern recognition, data analysis, and surfacing insights, enabling humans to focus on strategic thinking and creative problem-solving.
Rather than replacing human decision-making, AI improves it by revealing trends and opportunities we might not have noticed on our own.
Different technologies underlying AI applications are:
AI apps are more than hype. They are changing the way businesses across industries carry out internal functions and serve their customers.
Here’s why AI apps are more than just hype:
AI adoption is not just theoretical, businesses across industries are actively integrating it to solve real world challenges.
A Forbes study highlights how companies use AI for diverse applications, from improving customer service and detecting fraud to automating supply chain operations, demonstrating its tangible impact beyond the hype.
There are three types of AI applications:
The AI underlying these applications are developed and trained in-house. Usually businesses that have huge funds or raise large sums from investors with a unique vision custom build models as per their requirement.
With proprietary models you can:
However, they require significant technical expertise, high development costs, and ongoing maintenance.
On the other hand, third-party models from platforms like OpenAI (GPT-4), Google (PaLM or Bard), and Anthropic (Claude Sonnet 3.5) offer pre-built, cutting-edge AI capabilities accessible through APIs.
Building AI applications on top of these models is not only cost-effective but also highly scalable because all the heavy lifting from developing and updating the model to setting up scalable, secure infrastructure is handled by the model provider. This makes these models particularly appealing to businesses and individuals with limited resources.
However, relying on third-party models comes with trade-offs, such as reduced control over customization, potential data privacy concerns, and dependency on the provider’s pricing and terms of service.
A hybrid approach often offers the best of both worlds. Many businesses use proprietary models for critical or sensitive tasks where control and security are paramount, while leveraging third-party models for more generalized or time sensitive features.
The choice largely depends on your specific use case, resources, and goals.
Here's a step by step guide to help you along your AI application development journey:
The first step is to clearly identify the specific problem your AI application will solve. Start by understanding:
By isolating the core value your app will provide, you can define its critical offerings and determine the precise role AI will play in solving the problem effectively. This clarity sets the foundation for building a purpose driven, user focused AI application.
If this is your first building an app, take some time to define what your MVP will be and "spec out" what pages and features you'll need on your app. This will give you the structure you need to build something viable.
Once you have a clear outline of your application, the next step is to select an AI model that fits your need.
For most projects, especially if you’re starting out, leveraging a pre-trained model is a practical and efficient choice. These models come in various shapes and sizes, often optimized for tasks like language processing, image recognition, or speech transcription.
For example, Jasper AI is built on OpenAI's models, specifically utilizing GPT-3.5 and GPT-4 for its text generation capabilities.
Compare different models or APIs based on performance benchmarks, cost, latency, and compatibility with your chosen tech stack.
Here are some popular pre-trained models:
The list goes on and new players are constantly added.
As newer and more advanced AI models become available, you can seamlessly integrate them into your system.
Choosing the right tech stack is key for AI application development. Your choice of tools should align with your app's requirements, development timeline, expertise, and resources.
Prompt engineering is a critical skill. The results you obtain from an AI depend on the clarity, context, and detail of the instructions or questions you provide. Follow these tips to create strong, impactful prompts:
By following these practices, you can guide the AI effectively, ensuring your application delivers consistent and valuable outputs to users.
Even the most powerful AI application will struggle to succeed if end users can’t effectively interact with it. A thoughtful user interface (UI) and user experience (UX) design keeps your audience engaged and helps them understand how to use your AI solution.
Start with simple wireframes to outline how users input data and receive results. Strive for clarity, responsiveness, and an intuitive flow.
If you're new to app design, our guide on 5 essential frontend design principles is an excellent starting point.
Your backend acts as the intermediary between the user facing interface and the AI model. It can truly make or break your app.
Key tasks include:
A solid backend doesn’t just keep everything running, it also makes future updates and scaling much easier.
Configure the AI logic in the backend to:
The processed data is then displayed to users in a clear and user friendly way.
To keep your app fast, you can save commonly used results (caching) and reduce the size of the data sent to the AI model (to lower waiting time).
A well optimized AI workflow is the foundation of a successful AI powered application.
Thoroughly test your application to ensure it performs as expected. Test the app across multiple devices to check for compatibility and responsiveness.
Evaluate the AI's output to various inputs, including edge cases and inputs beyond its intended scope. Observe if the AI "hallucinates" by generating inaccurate or irrelevant outputs.
Also, assess the app's performance to ensure it isn’t slow or laggy.
Based on what you find, edit your prompt and improve the AI's accuracy and reliability.
Pro tip: at this stage, it always helps to put your app in the hands of a small group of beta testers that you trust to give you constructive feedback.
Deploying your first AI application is a significant milestone, one to be proud of!
Once you’ve fixed all known bugs and thoroughly tested the app, it’s time to launch it on your desired platforms, such as iOS, Android, or as a web application.
After deployment, share your app with your network, including friends, family, and colleagues, to spread the word. Encourage users to provide feedback on their experience, and use these insights to improve and evolve your app continuously.
Deployment is just the beginning of your app’s journey, so embrace the feedback loop to refine and grow your creation.
According to a McKinsey Global Survey on AI, 72% of organizations have adopted AI in at least one business function. Significantly up from 55% the previous year.
Here are some key trends shaping AI application development:
In the early days of AI adoption, businesses primarily experimented with language models to create chatbot based applications. However, AI app development has now progressed beyond simple chat interfaces. Companies are ideating on software solutions atop LLMs (large language models) that are not standalone chatbots tools.
As we move into 2025, AI applications are evolving beyond text-based interactions. The rise of multimodal AI capable of processing and generating text, images, and audio is gaining momentum. OpenAI’s recent unveiling of Sora, a video-generation model, and ElevenLabs' human-like voice synthesis technology exemplify this shift.
The focus is no longer just on chat based AI. But on fully integrated, multimodal AI solutions that enhance user experience across various domains.
Since late 2024, there has been a growing interest in AI agents.These agentic systems are capable of executing tasks autonomously, making real time decisions, and adapting to their environment.
Specialized AI agents are emerging across industries. Such as AI Sales Agents that automate lead generation and communication, and AI Developers that assist in coding software projects.
Today these agents are not fully autonomous, human oversight is still critical. The evolution of agentic AI points toward a future where AI systems take on increasingly complex responsibilities without requiring human intervention.
As AI becomes more autonomous, ensuring security is critical to prevent misuse, protect sensitive data, and ensure reliability.
AI applications often handle vast amounts of sensitive personal, financial, and corporate data, making them prime targets for cyberattacks.
Weak security measures can lead to data breaches, model manipulation, or adversarial attacks. The AI can be tricked into making incorrect decisions. Additionally, unauthorized access to AI models can result in misinformation, fraud, or deepfake abuse.
To build trustworthy AI applications, builders are prioritizing security. This includes implementing strict access controls, regular audits, and adversarial testing to safeguard both users and the integrity of the system.
From the very start Mike had a bold vision, to create an AI-driven skip tracing platform for the real estate industry. A keen design enthusiast, he had already reverse engineered the skip tracing process. But, turning that complex algorithm into an intuitive user experience proved to be a massive challenge.
Mike wanted to focus on business growth rather than getting lost in technical development. He initially outsourced the project to traditional developers and agencies. However, despite spending significant time and money, the results were far from what he envisioned. Timelines dragged on, and he lacked the ability to make changes himself.
Then he discovered WeWeb and Xano, two powerful visual development platforms to build frontends and backends that scale.
With the support of the WeWeb team and its partner agencies, Mike found the right experts to bring his vision to life. And by leveraging WeWeb for the frontend and Xano for the backend, he gained full control over his application.
Powered by AI and machine learning, SkipMatrix scours multiple data sources to deliver real estate professionals highly accurate contact information of property owners. Today, Skipmatrix generates over six figures every month.
Mike’s journey proves that building AI applications doesn’t have to be overwhelming. With the right approach, even complex solutions can be developed efficiently.
If you have an idea that could make an impact, take the first step. Start building with WeWeb for free.
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