How to Create Artificial Intelligence Software

Industry experts compare the launch of AI to the invention of the printing press or the first personal computer. It has become one of the leading software development areas with vast growth potential. 

Over 50% of businesses have already adopted AI to their operations, and 76% of enterprises report increasing investments in AI in 2023. Statista’s data  suggests that the global AI market will reach nearly two trillion USD by 2030 compared to 95.6 million USD in 2021. 

But building AI software is still challenging, not least because of the rapid advances in technology. Like Alice from the well-known fairy tale, you have to run twice as fast to get where you want and keep up with progress.  

We have prepared a guide to tell you how AI software development looks from the inside and what steps you as a business should take at the preparation stage. 

What Business Problems Can Be Solved With AI? 

AI has a vast potential to solve various problems and improve business operations. Every company has its reasons to turn to innovation and develop artificial intelligence software. 

According to recent IBM research,  while adopting AI solutions, companies address many issues such as needs for cost reduction and automation (42%), competitive and consumer pressure (31% and 25%т respectively), labor shortages (22%), or environmental pressures (20%).

About half of the organizations already notice benefits from using AI. These include cost savings and efficiencies (54%), improvements in IT or network (53%), better customer experiences (48%), and improved employees’ focus on high-priority tasks (46%).

The experience of CHI Software supports the research statistics. Our clients from different industries and markets efficiently meet various  business requests while adopting AI solutions. The most common of them are: 

• cost optimization in day-to-day operations,
• better customer experience through personalization,
• higher customer engagement and thus improved customer loyalty,
• sales and revenue increase,
• improved brand differentiation on the market.

Let us look at particular cases of AI adoption and the benefits companies gain when developing AI software for their business. 

Successful Use Cases of AI Integration Software: CHI Software Experience

The practical application of artificial intelligence is a constantly expanding field. Today, there are thousands of examples of AI implementation.

We at CHI Software have focused on AI/ML technologies since 2019, creating sophisticated solutions in various domains. 

Compliance Automation Solution for Grant Seekers

Many organizations rely on grant support to finance their operations. Applying for grants involves developing and submitting a large pile of documents requiring much knowledge and focus. Procedures can be so complex that many organizations hire a dedicated employee to handle these tasks. 

Our AI-powered solution aims to simplify and streamline all grant compliance procedures, ensuring that all necessary documentation and requirements are met in one location.

The AI-powered personal agent assists grant seekers in several ways:

• guides users through compliance procedures,
• provides real-time updates and notifications about deadlines and changes in regulation, 
• reviews the documents provided by users, identifies errors and typos, and provides feedback and suggestions for improvements,
• smoothes out communication between grant seekers and compliance authorities and ensures transparency in compliance-related disputes.

As a result, organizations can successfully process grant applications, simultaneously optimize their time and employee resources for the application process, and focus more on their core activities.

AI-Based Virtual Companion 

During COVID-19 lockdowns, our client, a USA-based media startup, launched a social media networking platform styled as an online nightclub with opportunities for social interaction, like hosting parties, enjoying music and videos, and chatting. 

As the new possibilities with ChatGPT emerged, we incorporated a Virtual Companion, an AI-powered conversational agent that can hold a natural and engaging conversation. 

Our team has been training the Virtual Companion on the vast amounts of conversational data to enable it to perform several roles depending on user needs and expectations. 

The Virtual Companion is trained to follow four different conversational patterns:

• emotional support with active listening and an empathetic approach;
• personal assistance which helps a person with to-do-lists, reminders, appointments, and informational support;
• language practice which helps master new languages and train in grammar, vocabulary, and pronunciation. 
• entertainment by engaging in social interactions like trivia, online board games, storytelling, and jokes.

Users warmly met the Virtual Companion. The client reported several positive outcomes, such as customer retention growth, boosted engagement, improved brand differentiation on the market, and increased cross-sales due to personalized suggestions made by the Virtual Companion. 

How to Build Software With AI? The 4 Key Steps to Consider 

What does it take to create an AI software solution?  What resources, people, and stages do you need? Let us look under the hood. 

First, you should know that creating artificial intelligence solutions is an iterative process. The development pipeline in its basic form can be represented as: 

1. Research, discovery, and team planning, 
2. Model learning,
3. Minimum viable product (MVP) development and further product improvements,  
4. Launching and support. 

Now let us look closely at the first two (and the most important) stages of AI software development. 

Research & Discovery 

The first step is to clarify why we are going to create AI software and what we need to get started. Therefore, we identify:

• a problem-solving idea, 
• pain points of the intended product users,
• value proposition,
• technologies to use, 
• development milestones,
• AI metrics tied with business metrics. 

Team Planning  

To get a competitive product, we gather an experienced team for: 

• Management and research: a project manager and a business analyst; 
• Data analysis: data scientists, a dataset markup team, and ML engineers; 
• Development: a solution architect, frontend and backend developers, AI specialists, and DevOps engineers; 
• Testing: quality assurance (QA) engineers. 

Platforms 

AI platforms provide developers with ready-made tools for product building. They combine intelligent decision-making algorithms and data. Some platforms are easy to use, while others require deep coding expertise. Here are the most popular ones:

• Google platform. It consists of AI Hub (resources for developing artificial intelligence systems), AI Building Blocks, and AI Platform, a code-based data science environment for creating projects from idea to launch.  
• Microsoft Azure. AI capabilities include apps and agents, knowledge mining, and ML services. The platform helps create, train, and deploy models. You can also use cloud search with built-in AI capabilities of pattern identification in content, sentiment analysis, or key phrases extraction. 
• AWS Machine Learning. Its services help build, train, and deploy ML models of any complexity. Amazon’s AWS delivers out-of-the-box analytics for companies and enterprises and simplifies app development. 
• IBM Watson provides various tools to build, train, and deploy ML models. Additionally, the platform offers pre-trained models tailored for natural language processing, image recognition, and predictive analytics. 

Oracle AI cloud services and the H2O ML platform with linear scalability are other options for engaging AI.

Programming Languages, Libraries, and Frameworks 

The programming languages we commonly use for AI app development are: 

• Python. This language easily integrates with data structures, offers unique algorithms beyond standard programming, and allows developers to expand knowledge with libraries and tools such as NumPy, Pandas, Scikit, AIMA, etc. 
• Java. An object-oriented language that differs in a thoughtful approach to exception handling, the availability of tools for developing multi-threaded applications, and support for arrays, lists, and structures.   
• C++. One of the fastest compilation languages globally allows you to implement highly complex logic without loss of performance. C++ suites are for apps with high-speed animation and immediate user interaction with the rendering engine. 

There is no universal recipe. To make AI software, developers select an optimal set of technologies for each particular case. 

Here are more examples of helpful tools our developers recommend for creating AI solutions: 

• Frameworks: Caffe, Keras, PyTorch, Accord.NET, scikit-learn, and Spark MLlib;  
• ML-as-a-Service platforms that allow developers to use graphical user interfaces such as IDEs and Jupyter Notebooks; 
• APIs that get exposed as REST endpoints and return JSON with results (e.g., Azure Topic Detection API). 

Model Learning

Every AI algorithm, even the most advanced one, needs appropriately collected and prepared data. Most often, our engineers use the Cross-Industry Standard Process  for  Data Mining (CRISP-DM) consisting of the following steps: 

• Business understanding, 
• Data understanding, 
• Data preparation, 
• Modeling, 
• Evaluation, 
• Deployment.

 

In most cases, engineers apply steps cyclically and repeat them several times (besides deployment).

We usually have nothing except a project description at the discovery phase, but business and data understanding help us carefully learn an issue and find a solution. 

Then we proceed to the data preparation stage. It involves selecting and uploading raw data, picking annotation tools, highlighting and labeling data blocks, and selecting and saving file formats. 

The collected dataset allows us to compare solution options selected by the client. For this type of problem, choosing a metric for model comparison is crucial. So, it is necessary to establish success criteria and pick an option that best suits the client’s business goals.  

After preparing the data, we determine the platform and programming languages to create AI software. 

Modeling 

At this point, engineers use previously collected data to train ML models via various methods. For instance, we at CHI Software apply deep learning or reinforcement learning techniques. 

Evaluation & Deployment

When the model is trained, we have to evaluate it and make a decision to deploy it or go for another cycle of model learning. We can also deploy the current state of the model and continue to improve it. 

At the development and support stages, you can also optimize performance, improve and expand functionality, and adapt the product to updates of various operating systems.

Conclusion 

We briefly touched on the points to understand how to build AI software. But this field is incredibly wide, and it is simply impossible to cover all the details in one guide.  

Therefore, if you want to let artificial intelligence into your business, it is best and most appropriate to turn to professionals. A company focused on AI-based software development can help you build the most efficient, resource-saving, and customer-centric solution. So make the first step to the AI-driven success of your business and contact our highly competent experts today.