Car insurance is one of the oldest and most popular fields of the insurance industry. Given the prevalence of vehicle policies, insurers can access a plethora of opportunities by mastering the digital world and exploring their options online. In the last decade, insurers have begun to do just that, and the insurance industry landscape has changed markedly. More and more...
The mobile app industry is booming! Thanks to growing internet usage and a smartphone nearly in every pocket, mobile apps have become indispensable for customer service.
Yet, the competition is fierce. More than 5.7 million apps are available on Google Play and App Store, and more than 485,000 mobile apps are downloaded every minute. To stand out, your mobile app should be fast, smart, and fit customers’ needs better. AI can be the solution.
People eagerly use AI for their routine operations: about half of Americans are interested in AI to conduct online searches, generate recipes, and use smart assistants. So, it is not surprising that the astonishingly rapid advancement of artificial intelligence technologies has been one of the key drivers supporting the mobile app market growth.
However, AI app development is one of the most challenging tasks in the software industry, and you may need help on your way to innovations. We have compiled a consolidated guide to walk you through the main technical aspects of AI mobile app development and help you figure out how to create an AI application from scratch to success.
What Are the Benefits of Implementing AI in Mobile Apps?
The rapid development of AI technologies gives vast possibilities for transforming the mobile experience. Today, many apps provide AI-enhanced features for learning languages, getting new contacts, choosing what to cook for dinner, and unlocking devices with biometrics. With AI, mobile apps become more engaging, exciting, and empowering.
As mobile apps with AI-enhanced functionality appeal to users, businesses can utilize their emotional power to achieve numerous benefits.
The advantages companies can gain vary a lot. We can categorize them as follows:
- Improved user experience through deep personalization of content, ads, and recommendations.
- Automation of day-to-day tasks helps reduce costs and decrease the chance of human-made mistakes.
- Increased efficiency in operations, data processing, and customer support.
- New revenue stream opportunities due to better recommendations and consumer satisfaction.
- Enhanced security of sensitive data and mobile apps in general.
Our experience proves that adopting AI to a mobile app naturally increases brand awareness and perception. Users like and warmly greet innovative, smart, and intuitive apps that understand them and serve their needs better.
Successful Use Cases of AI Mobile Apps: CHI Software Experience
There are no two businesses alike, so AI adoption is always a one-of-a-kind story. We have built AI mobile apps from scratch and added AI-powered features to working apps. And every time, it is a unique and exciting experience.
Chatbot Powered by the GPT Model
Choosing an internet plan can be boring, but discussing options with a penguin is fun. Therefore, our client, an internet and mobile communication provider and a subsidiary of the largest telecommunication holding in Japan, decided to introduce a cartoon-like character as a client support assistant.
We created an entertaining mobile app with an emperor penguin named Kopenchan. Based on the Tamagochi principle, the penguin can evolve, learn new words, and use them in communication.
Kopenchan is trained to interact with clients, using previous context from a chat history, respond to client requests, and announce the company’s updates and newsworthy events through push notifications.
Though the penguin chatbot looks like an entertaining app addition, it has had a significant positive impact on business metrics. The client reports increased customer satisfaction and better engagement, as well as operational cost savings of up to 20% due to customer support automation.
Computer Vision Solution with a Recommendation System
Getting expert advice while choosing skincare and beauty products is essential for many shoppers.
In an ideal world, a beauty expert should keep up with the market’s latest trends and quickly and carefully identify consumer needs. She or he should make personalized and judgment-free recommendations, considering clients’ preferences and product availability. But who could it be? AI is the guy!
To help shoppers make buying decisions, our client, a cosmetic retailer from the US, decided to build an AI mobile app. It should analyze customers’ selfies, examine their needs, and recommend products that serve them best.
In response to these requirements, CHI Software developed a mobile app based on Computer Vision, photo face recognition, and ChatGPT capabilities.
Workflow in more detail
So how does it work? First, a person takes a selfie with the app. Then, the AI-based mobile app identifies a face in the photo and analyzes it. Generally speaking, the app compares the image to numerous samples in its huge library to specify the problem and find possible solutions. Users can give more information by text or voice to achieve better results.
Based on the data received, the app generates personalized recommendations. ChatGPT-based chatbot wraps all the useful data in a friendly chit-chat with a user. This way, a person can express concerns and doubts and receive more detailed explanations. The app also collects feedback to improve the model for more satisfying results in the future.
After the solution went live, the retailer reported improved customer loyalty, optimized inventory management, and an increase in sales (+10%) and revenue thanks to upselling and cross-selling.
Tech Stack for AI Application Development
Of course, there is no universal tech stack that fits every case of AI mobile app development. Our team determines the technologies for any project exclusively, considering requirements, desired features, and best development practices.
Programming Languages, AI Platforms, and Libraries
Typically, Python, Java, and C ++ are go-to options for building applications based on artificial intelligence. In some cases, engineers can also turn to C#, R, Lisp, or Prolog.
To optimize development costs, our software engineers use third-party AI and Machine Learning (ML) platforms, including the most popular ones:
- Google TensorFlow is an innovative data science environment for numerical computation using dataflow graphs and creating AI-based projects from idea to launch. The functional and portable TensorFlow has a flexible architecture that allows computing to be deployed across multiple processors applying a common API.
- Microsoft Azure. The platform’s advantages are a wide range of algorithms, advanced analytical mechanisms, and high-quality multilingual documentation. AI/ML capabilities include predictive modeling, recommendations mechanism, natural language processing, pattern recognition, and other services.
- Amazon AWS helps to create, train, and deploy models and intelligent apps of any complexity. The platform is based on simple, scalable, and flexible ML technology, which is used, among other prominent engineers, by scientists from the Amazon community.
Frameworks and Application Programming Interfaces (APIs)
AI, ML, and deep learning frameworks greatly simplify the development of complex, high-tech products and allow you to use third-party functionality. The most popular options are:
- Microsoft Cognitive Toolkit (CNTK) helps create various ML models, including recurrent and convolutional neural networks. Using the framework, we quickly process arrays of unstructured data, choose metrics and algorithms, and train machines to think almost like people.
- AWS Machine Learning assists in the development of highly complex applications with high performance. It can connect applications to cloud services and create forecasts using API.
- PyTorch. Combining the ML library, pre-trained models, and scientific computing structure, it allows us to quickly build complex systems such as deep neural networks. The Python-powered framework provides excellent flexibility and efficiency.
- Core ML/Create ML are well-suited for developing AI products for Apple devices in particular.
- Caffe2 helps build modular deep learning environments. ML’s open-source set of algorithms enables engineers to experiment with different models.
Other popular framework options are Keras, Accord.NET, scikit-learn, and SparkMLlib. Apart from frameworks, we use third-party APIs and software development kits (SDKs) )to speed up and optimize software development. The options include Azure Text Analytics API, Microsoft Face API, Google Vision API, Apple SiriKit, and others.
How to Create an AI-based Application: Key Steps
Though some of the steps are repeated many and many times while developing an AI app, we follow the basic pipeline:
- Discovery, requirements definition, and planning
- Model learning
- Minimum viable product (MVP) and final app development
- Delivery and further maintenance.
Let us guide you through the details of every stage.
1. Discovery, requirements definition, and planning
To understand how to build an AI app, we need to identify the issue first: decompose the problem and possible solutions, learn more about target users, discover market conditions and competitors, and identify desired outcomes. The discovery phase depends on the client’s requirements, information on hand, and available documentation.
Once the idea and problem are defined, we create a clear list of product requirements. Properly written and structured, they help developers understand the purpose of creating a product and identify technologies and tools for working.
At the planning stage, we also:
- determine a team of technical and non-technical specialists,
- draw up a work schedule,
- start exploring the data required to create an AI/ML model.
2. Model learning
Typically, AI-powered applications require large amounts of well-prepared structured data to operate correctly. Therefore, software engineers carefully study input information and its sources, following the Cross-Industry Standard Process for Data Mining (CRISP-DM).
Next, we check input data for errors, missing values, or incorrect labels and move on to the data preparation stage, which requires:
- Selecting and uploading raw data,
- Picking annotation tools,
- Highlighting and labeling data blocks,
- Selecting and saving file formats.
The collected dataset allows us to compare solution options and proceed with the modeling stage. The previously collected data is used to train ML models via various methods. When the model is trained, we have to evaluate whether it is ready for deployment or needs further training. Once we have a model deployed, we integrate it into a mobile app.
3. MVP (Minimum Viable Product) and full-fledged app development
AI development projects at CHI Software are based on the Agile methodology. It means conducting many cycles of software development and testing one after another. This way, we can find gaps as early as possible and fully control the product’s high quality and market relevance. We are gradually moving from a minimum viable product (MVP) to a final solution to be presented to end users.
An MVP allows us to present a basic product version to evaluate essential functionality along with the overall viability of the app’s idea. In general, creating an AI-driven application is no different from a typical software development life cycle (except for CRISP-DM), so we have to:
- think over the solution’s architecture,
- design a user interface and user experience,
- work on the frontend and backend parts (user interface development and server-side development).
At the development stage, we optimize the app’s performance, improve and expand functionality, and adapt the product to mobile OS updates.
4. Delivery and maintenance
When our system is ready, we can deliver it or publish the application on app stores. For native apps, we use the Play Market guide for Android applications and AppStore recommendations for iOS products.
We strongly recommend updating the system regularly (so that it supports the latest OS versions) and improving models by adjusting and supplementing the previously collected data.
How Much Does It Take to Build an AI Mobile App? Time and Budget Explained
AI app development cost depends on many factors, such as features implemented, product complexity, a tech stack, and the number and rates of people involved. Budgets to build a chatbot and a corporate data analysis system are strikingly different. However, there are general rules to consider.
The cost of an AI mobile app correlates with the time of tech and non-tech specialists involved. Here is our rough estimate of the time to build an AI mobile app:
From our experience, it takes about three months to develop a simple AI app. However, if we talk about an end-to-end solution with rich functionality, it will take about twice as long. Extra factors, such as data visualization, the minimum accuracy rate for predictions, and dashboard requirements, can add to the time and, therefore, the cost of development.
In general, developing AI software can cost at least 10,000 USD, but in most cases, budgets range from 30,000 USD to 100,000 USD, depending on the product’s size and complexity.
App support is another unavoidable cost, which represents around 30 percent of the development budget for the first year post-launch and 15-25 percent per year afterward.
Artificial intelligence has been landing on mobiles. AI-based apps help people be more productive, creative, and efficient in their daily chores and activities, so unsurprisingly, AI is warmly greeted by mobile users.
Adding AI features to your mobile app is a great way to overdo the competitors and gain a market advantage. Who would not want that?
To understand the latest AI trends and reach your goals right away, you need a professional guide in the field. The expertise of experienced software developers and business analysts can work miracles. So get the ball rolling!
Book your free consultation right now! Regardless of the size and available budget, we will help your business idea stand out.
About the author
Alex is a Data Scientist & ML Engineer with an NLP specialization. He is passionate about AI-related technologies, fond of science, and participated in many international scientific conferences.