If you’re considering creating an AI application, these facts and statistics may prove that you’re moving in the right and prospective direction. Here, you can see the tangible benefits artificial intelligence apps and solutions give to businesses.
In this article, you can read the thoughts, deep insights, and real-life experiences of Oleksandr Bachynskyi. He has been holding the role of Chief Technology Officer, or CTO, at our AI application development company for more than 10 years. Under his leadership, our team created about 100 digital products in 25 countries, including diverse AI solutions and applications. Oleksandr has made a significant contribution to putting Linkup Studio on the list of Clutch’s Top 100 Fastest-Growing Companies globally in 2023. From 2022 to 2023, our company had an impressive growth rate of 192.5%. Our CTO is an expert in many areas, including mobile and web development, no-code platforms, solution architecture, AI app development, integration, as well as software testing.
AI-based apps are the type of applications that use diverse artificial intelligence technologies in order to automate some operational processes, analyze data, interact with users as humans would, and deal with other tasks depending on the existing business needs or the set plans for advancement.
These AI applications work based on clever algorithms that can learn from data and then make decisions based on it. Such technologies are already used in diverse industries, such as healthcare, finance, customer services, logistics, retail, and others.
Here are the main types of AI-based applications. In real life, AI-based applications can have one, two, or more of these features and technologies mentioned in the list below. It’s up to the business needs and requirements.
This type of AI-based app uses specifically designed algorithms to analyze and learn from data and improve its performance over time, even without being programmed for every specific task.
Such applications are truly great at making predictions. A good example of how these apps can be applied in life is banking apps. They can predict stock trends, execute algorithmic trading, create customer personalization finance strategies, and manage risk in financial institutions. We worked with a similar product at Linkup Studio — read our Delfi Labs case study.
It is the type of artificial intelligence app that processes and then interprets visual information from different types of environments, such as image recognition, object recognition, and sometimes even complex scene reconstruction for specific tasks.
At Linkup Studio, we developed a Data Recognition White Label Solution that is an expert in OCR (Optical Character Recognition) and works based on computer vision and machine learning technologies. This system scans all the printed materials and images of invoices and then intelligently transfers all the information it gathers to a spreadsheet. Dive into details in our video. Another popular example of using these technologies is facial recognition in our smartphones. These systems identify and verify individuals.
NLP applications enable products to process human language in text and voice formats alike and allow digital products to communicate with their customers so they get responses that feel very natural and lively to them. They are able to perform it because they use complex models to understand syntax, the context of language, sentiments, and more.
Natural language processing is very popular for customer service in the form of integrated chatbots or virtual assistants, which can reply, manage, and support people in their inquiries and problems which are more or less common for the product. Additionally, NLP is a game-changer in the healthcare industry which allows more efficient and personalized service to patients. They’re also used to monitor and analyze the sentiments of people on social media platforms if the product requires these tasks for business development.
If the business has processes that require people to manage repetitive tasks, it’s worth considering integrating an RPA application. This automation can help increase the productivity of executing high-volume tasks that still need humans. RPA technology can learn to interact with digital systems and software in the exact same way that human workers would, but without rest and unavoidable human flaws.
RPA is often used in industries like banking to automate customer account management. It’s also used by insurance companies for claim processing and healthcare for managing information about patients.
These are the applications that are trained to automatically make or suggest decisions for some specifically set tasks. Usually, such decision-making apps use real-time data.
They are frequently used in banking apps for credit scoring and fraud detection based on users’ recent transaction behavior and overall client history.
These apps are helpful in cases where your business needs to use AI for generating some content, such as text, video, and visuals based on the data that users “feed” them.
As a rule, many media outlets have adopted content creation apps and now generate news reports and produce content for their platforms. E-commerce platforms can use such solutions for making product descriptions for their site and doing some typical marketing content, as it is more profitable than holding departments for doing the typical job, which AI can easily do.
To create something influential or to upgrade the existing platforms so they can be called great, it’s important to know and have the examples of the products that have achieved success in the technology your business is going to adopt. In this list, you’ll see the iconic products and what exactly sets them apart, and what we can get and hanker for in our products.
The basis for Google Assistant's success is Natural Language Processing technology. AI developers designed it to go far beyond basic commands. In fact, this NLP is capable of understanding user intents and context. So, a conversational interface can manage many tasks.
It is useful to note how Google Assistant has trained its deep learning models to interpret and even predict human speech. Undoubtedly, this solution is a benchmark in user interaction and technology for product personalization.
It is a very famous AI tool that helps businesses make better decisions. It achieves this by understanding and processing language just as humans do. IBM Watson can analyze vast amounts of structured information in databases or unstructured like emails, social media posts, and other places.
For instance, banks may receive thousands of inquiries from customers every day through emails, chats, and phone calls. If the bank has a large base of clientele, maintaining a robust level of customer service can be really challenging. For this case, IBM Watson can provide to such clients automated customer support. For example, if a client asks about their bank balance, the AI understands the essence of the request, assesses the customer’s account details, and provides the needed information.
This tool can also detect sentiments. Let’s imagine this situation – one customer writes a complaint email. Then, the AI analyzes the frustrated tone of the text and flags this email in red to draw the human agent’s attention to it for a faster response.
Kiva Systems are used by Amazon, which is a large online retailer and marketplace. This company uses AI-driven robots to make operations at their warehouses and order delivery to customers significantly faster. Basically, these robots drive around, pick up required products, and deliver them to human workers.
As a result, people feel less tired and can work more efficiently and faster. Additionally, during the holiday seasons, these clever AI robots can optimize their paths in the warehouse and adjust product placements based on the increased demand for popular items.
The lesson from Kiva Systems is that if you can find a process within your business that can be automated by AI tools – try it, test it, and become more efficient.
This platform has set a high standard with its language model that can generate human-like content. ChatGPT can create texts, articles, essays, write posts, generate code, and even make arts based on the prompts given by users.
AI is able to assist especially marketing teams as it can generate an effective draft for the information requested in seconds, and then optimize this content based on additional prompts. What can we learn from OpenAI GPT-4? The most important point is the high level and quality of understanding human language and responding to the requests accordingly.
There are many tools in the healthcare industry which have proven their efficiency and use. Our today's Zebra Medical Vision helps radiologists read medical imaging more efficiently. This platform helps to detect anomalies like tumors, liver density issues, and other conditions which a human doctor can overlook.
This tool operates based on vast datasets of medical images collected over years of practice in this field. This way, AI can recognize the disease and the problem and help to provide more efficient treatment.
The lesson from this tool is to make sure that your product uses the best and the most trustworthy and vast dataset existing to solve the business problem.
At Linkup Studio, we use diverse programming languages, frameworks, tools, and other technologies to make AI applications. For each product, it is very important to define the exact functionality needed from the very beginning of development. Otherwise, this may result in additional expenses, time losses, and even business failure. Choosing the right tech stack is a crucial question that our team helps to solve after a deep discovery phase, learning about your product, objectives, and other details.
Python is one of the most important programming languages in AI application development. It is simple to operate, flexible, and has extensive support from diverse libraries and frameworks for multiple tasks, such as data analysis and machine learning. Python allows our team at Linkup Studio to build both complex and simple solutions.
Our AI developers also use JavaScript, especially for web-based AI apps. Even though this programming language is not as popular in the context of AI development, its ecosystem includes TensorFlow.js, which allows for the implementation of AI directly into the web browser. This is useful for products that need to execute tasks like speech or image recognition directly in the user interface.
PyTorch is known for its flexibility and ease of use in research settings. It supports dynamic computation graphs that allow us to make adjustments on-the-fly during learning processes. This tool is very helpful for experimental AI projects or developing prototypes to test ideas.
On top of TensorFlow, which was mentioned above, we use Keras. It is a high-level API that helps us simplify the creation of deep learning models. These two tools are essential for developing production-ready AI solutions that require complex data computations. These technologies can be applied in many ways, from diagnostics in healthcare to financial forecasting.
When we work with computer vision tasks, we regularly use OpenCV. It supports real-time image processing and is very useful for AI applications that require visual data interpretation. Practically, OpenCV is used for facial recognition systems, customer behavior analysis in retail products, and other purposes.
For data manipulation and preparation, we use Pandas. For predictive analytics based on historical information, we use Scikit-learn, which offers diverse machine-learning tools.
In our team, we work with AWS, Azure, and Google Cloud. These platforms provide scalable and robust cloud computing capabilities that are crucial for natural language processing, machine learning models, and other AI development technologies.
When our team needs to deploy or efficiently scale containerized applications, we use Kubernetes. This tool ensures consistent environments across development, testing, and production phases.
Our AI developers regularly deal with the task of training deep learning models, which requires intensive and fast computational demands. Custom servers are perfectly optimized for such high-performance computing.
AI app development also requires tools for extracting insights from massive datasets, identifying specific patterns, and predicting trends. Such tools include BigQuery and Databricks, which can process and quickly interpret large datasets.
There are some significant challenges we face when we have to create and integrate AI technologies into mobile apps for our clients.
One of the most prominent issues is the performance of AI in mobile apps. Mobile devices have limited processing power, memory, and battery life, especially compared with servers or PCs. They usually don’t have the needed intensive computational resources. That’s why we always explain in our initial consultations the viability of the AI product idea and the ways of its realization.
The next point is data management. Collecting and maintaining information on mobile devices generally decreases product performance and worsens user experience. That’s why we use cloud services as much as possible to handle extensive information processing and storage remotely, reducing the burden on the device itself.
Additionally, for this task, there should be proficient designers who can integrate an AI interface into an aligned platform or product appearance so that it looks simple and user-friendly. For our team, it is not a problem, as we have experienced designers you can hire.
Another challenge is that the majority of AI models and tools require internet access and operate based on a server. Managing intermittent connectivity and ensuring that the app functions as intended both online and offline is a real challenge, which we always anticipate and address by discussing it with the client.
Our AI experts choose the algorithm for your AI application only after a deep discovery phase, which involves understanding your business, your current goals, metrics of success, budgets, and other fundamental information. With this information, we also consider the nature of the available data, assess its accuracy, and consider the available computational resources, and agree with clients on other details. That is the approximate roadmap of our process for choosing an algorithm.
As an example, we can take our SkinSpotter application. It is an innovative AI app for proactive skin health monitoring and early detection of various skin conditions.
First of all, we added the ability for users to upload photos of a skin spot with the potential problem. Then, the AI scans this photo using Object detection technology and compares the given photo with a database of photos that have been diagnosed by dermatologists. As a result, the user receives a potential diagnosis of their skin condition.
We need to stress here that such AI applications are great for initial testing but should not be trusted fully, as there can be other factors and nuances. Therefore, for AI healthcare applications, it's always necessary to provide disclaimers and advise people to seek the ultimate diagnosis from a human specialist.
Our AI app developers understand the importance of user data security and privacy in AI applications. That is why we protect it by encrypting it both at rest and in transit with strong encryption protocols to prevent and avoid unauthorized access. Our additional solution is to implement strict access controls and role-based access mechanisms to ensure that only authorized personnel can access sensitive information.
Where it is possible, we make such information anonymous in order to further enhance privacy and prevent it from being linked back to any individual. At Linkup Studio, our AI app development specialists conduct security audits to maintain compliance with relevant data protection regulations like GDPR, HIPAA, and others, depending on the country where the product works.
Before deployment, our AI app developers always test and validate AI functionalities within an app through a comprehensive multi-layered approach.
First of all, we perform unit testing to check individual AI components under various conditions for expected behavior. This is followed by integration testing to assess how these components interact with each other and the rest of the system. The next stage is when we conduct system testing to evaluate the entire application in an environment that simulates real-world conditions.
Before final deployment, we carry out User Acceptance Testing (UAT). This allows actual users to test the app with AI in a controlled setting to give us valuable feedback on functionality and usability. After deployment, we continue to monitor the application’s performance and gather user feedback to further refine the AI.
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