article background

All
Artificial Intelligence Software Development Guide

All

What is Artificial Intelligence Software

Artificial Intelligence software is a type of program that can perform different tasks that humans usually do – answer questions, understand speech, suggest products based on what you like, etc. Such software can learn from data, recognize patterns, and make decisions based on this information. To put it short, it takes care of various problems and helps businesses be more efficient and devote more time to tasks where humans cannot be replaced. AI is all about making technology work for us.

article image

What Business Problems Can Be Solved With AI?

As mentioned above, the possibilities of Artificial Intelligence are vast, and it all starts with understanding your specific needs and how AI can help you meet them. Let’s take a look at some examples.

Imagine you're running a retail store, and you’ve noticed that some products fly off the shelves while others gather dust. You’ll likely think, “What’s the problem?” Figuring it out manually may take ages. AI can help you analyze customer buying patterns and understand the reasons behind this in minutes. Literally. For example, AI can analyze which products people often buy together, which is essential if you’re planning to build AI SaaS tools to enhance your business operations. And here you are – you now know how to create attractive product bundles to boost sales and increase customer satisfaction. 

Learn more about how AI is reshaping e-commerce in our article.

Now consider a customer support team that needs to answer tons of questions, most of which repeat. You could hire more staff so that people get answers faster, but that will cost you a pretty penny. That’s definitely a wasted investment. Instead, you can develop AI applications like an AI-powered chatbot to achieve the same goal efficiently. 

In manufacturing, equipment downtime can be a costly issue. AI-driven maintenance tools can analyze machine data and predict which piece of equipment is going to fail. This allows you to schedule repairs without waiting for a breakdown. As a result, you’ll cut your repair costs and keep a smooth production process.

Or think about a marketing team that faces trouble reaching its audience. You can create AI applications to check out social media trends and buying habits to launch highly personalized campaigns to boost engagement.  

By the way, one of our clients approached us to build an AI-powered data analytics platform. Check the success story here.

AI lets you work smarter, not harder. The tasks mentioned above are just a few examples. 

Here’s the list of key problems AI can solve:

  • Quickly process large amounts of data;
  • Automate repetitive tasks with AI-powered chatbots;
  • Detect unusual activity and flag potential fraud;
  • Streamline recruitment by identifying top candidates;
  • Predict and prevent supply chain disruptions;
  • Predict equipment failures before they happen;
  • Identify market needs to guide product innovation;
  • Predict future sales trends to help with planning, and a lot more.

Interested in how we revolutionized domain management for our client? Dive into our case study to explore our innovative solutions, the technologies we used, and the impact we made. Check it out here.

Types of AI Software

Different types of AI software are designed to tackle specific challenges. So you have to clearly understand what exactly you need to choose the right solution and develop AI software. 

Here are some common types of AI software:

  • Chatbots. These programs can simulate conversations with users. They can answer questions, provide customer support, and help with online shopping, etc.
  • Robotic Process Automation (RPA). RBA is similar to having a robot to do numerous repetitive, mundane tasks for you – processing forms, entering data, etc.
  • Speech recognition. That’s basically what enables your smart speaker to do when you give it a command like “Call John” or “Add milk to my grocery list”.
  • Image recognition. This software helps identify objects or features in images. It’s used in everything from sorting photos on your phone to tagging friends on social media.
  • Recommendation engine. This type of AI software can suggest stuff to you. A simple example is Netflix’s movie recommendations or Spotify’s playlist pickup. Recommendation engines learn from your past choices and offer things you’re most likely to enjoy.
  • AI-powered analytics. This type of software is mostly used by businesses. It helps them identify trends or patterns they may overlook and drives better decision-making.

All these types of AI software help both people and businesses do things better, faster, and with fewer errors.

Key Concepts in AI

Some key concepts of AI are as follows:

  • Machine learning. With ML, computers learn from data and improve over time. It’s like showing computers lots of examples and letting them figure things out on their own. In machine learning, features are manually selected from the data to train the model.
  • Deep learning. It’s a more advanced type than machine learning. The main difference is that deep learning requires larger datasets for higher performance. This type of software utilizes complex neural networks with multiple layers. In contrast to machine learning, deep learning can process raw data such as audio or images and needs no manual feature extraction.
  • Reinforcement learning. A type of ML that involves teaching AI through trial and error, rewarding and penalizing it for its actions.
  • Supervised/unsupervised learning. Supervised learning trains AI on labeled data, while unsupervised learning works with unlabeled data. This is how it works based on a simple example: Supervised learning is like teaching a kid with flashcards – you show them the card (data) and tell them the answer (label), like showing a picture of a dog and saying, “This is a dog.” In unsupervised learning, there are no labels. It’s like giving the kid a bunch of pictures and asking them to group similar ones together without telling them what the pictures are of. 
  • Natural language processing. This AI technique can understand human language. NLP powers chatbots, translation apps, or voice assistants like Siri or Alexa. It allows computers to read, listen, and respond to us.
  • Neural network. Neural networks help computers recognize patterns by breaking down the information into simpler parts and analyzing them step by step.
  • Computer vision. Facial recognition, self-driving cars, or X-ray analysis – all these are applications of computer vision. It enables machines to “see” and “understand” visual information and interpret it.
  • Generative AI. These systems can create new content – images, music, text – based on what they have previously learned. You know about deepfake videos, right? That’s what generative AI does.

How To Build AI Software: Key Steps

Let’s take a look at how to create an AI software solution. There are 7 key steps to plan:

Step 1. Discovery

The first step is to clarify why you’re creating AI software and what you need to get started. In this phase, you identify the following: 

  • What specific issue are you aiming to address with your AI solution?
  • What challenges or frustrations do the intended users face that your software can help with?
  • What unique benefits will your AI offer that sets it apart from other solutions?
  • Which tools, frameworks, or programming languages will you need to build AI tools?
  • What key stages will you outline to measure the progress during development?

Step 2. Data Gathering

Once you have a clear problem in mind, it’s time to collect the data needed to train your AI model. This data can come from various sources, such as databases, online repositories, or even user-generated content. The quality and relevance of the data are essential, as they directly impact the AI’s effectiveness.

Step 3. Preparation for Model Training

Raw data often comes with inconsistencies, missing values, or irrelevant information. So at this step, you have to clean the data, format it properly, and remove potential errors. You may also need to reorganize the data into a format that the AI can understand.

Step 4. Tech Stack Choice

Now it’s time to decide which AI technology fits your needs best. Choose one from what we described above – deep learning, NLP, etc.

Step 5. Developing & Training the Model

At this stage, you build the AI model – select proper algorithms and configure them. During training, the model will analyze the data, learn patterns and make predictions. 

Step 6. Model Testing

You take a separate, new set of data to evaluate how well your model performs. This way you can find issues and identify areas for improvement.

Step 7. Model Deployment.

Once you’ve tested the model and are satisfied with how it works, you can integrate it into your existing systems. Ta-da! Don’t forget to monitor its performance regularly to ensure it delivers the desired outcomes.

article image

Typical Roles on AI Solution Development Teams

To build AI software, some roles are critical to getting the project off the ground. There are also additional ones; they are important for support and optimization. Here's a breakdown of key and additional roles:

Key Roles

  • Project Manager – to oversee the project and coordinate the team.
  • Data Scientist – to analyze data and find the insights that will drive the AI model’s development.
  • Data Engineer – to collect, process, and prepare the data. 
  • AI Engineer – to design and build AI models.
  • Machine Learning Engineer – to train the AI models.

Additional Roles

  • Domain expert – to provide industry-specific knowledge that helps the team understand the problem.
  • UI Designer – to create user-friendly interfaces for interacting with the AI software.
  • Software Engineer – to integrate the AI model with other software and make sure everything works well together.
  • QA engineer – to test the AI software for bugs and ensure it functions as expected.
  • DevOps – to handle deployment.
  • Legal Advisor – to ensure that the AI software complies with regulations and protects user data.

Successful Use Cases of AI Integration Software

AI integration has made a significant impact across various industries. We’ve gathered a few real-world examples of how companies are successfully using AI integration software for you to get inspired even more:

1. Retail

Walmart uses AI in various ways – for voice shopping, for customer support through chatbots, and to help in-store associates with tasks like locating items, checking prices, and even managing their schedules. 

2. Healthcare

Zebra Medical Vision leverages AI and deep learning to analyze medical images like X-rays or MRIs and help healthcare professionals detect diseases and conditions such as cancer or cardiovascular diseases earlier and more accurately.

3. Finance

Companies like PayPal use AI to monitor transactions in real-time, analyze patterns in data, flag suspicious activities, and prevent fraudulent transactions before they happen. This helps significantly reduce financial losses.

4. Logistics

FedEx uses AI-powered robotic arms to help sort packages in their warehouses. These robots can pick up and sort boxes by size, shape, or destination using cameras and smart software. This technology helps FedEx speed up the sorting process, reduce mistakes, and cut costs. 

5. Hospitality

Hilton Hotels uses AI to enhance guest experiences and streamline operations. Their hotel concierge robot Connie developed by IBM Watson answers guest questions 24/7. AI also personalizes stays by remembering preferences like room type or amenities. Additionally, AI helps Hilton manage energy use more efficiently. This way they can cut costs and reduce their environmental footprint.

How much does it cost to create AI software?

The cost of building AI software depends on your project’s specific needs, the team you hire, and the complexity of the solution you’re after. Below is a rough estimate of what you might expect in 2024. Data obtained from open sources (Clutch, G2, and similar):

  • For simple, small-scale AI solutions like a basic chatbot or recommendation engine, the cost can range between $10,000 and $100,000. These projects typically require less development time and fewer resources.
  • More advanced, mid-scale projects, such as AI-powered analytics or predictive maintenance tools, can cost between $100,000 and $500,000. This range includes more complex machine learning models and integration with existing systems.
  • For sophisticated, large-scale AI applications like self-driving car software, deep learning systems, or large-scale natural language processing, expect costs to soar beyond $500,000 to $1 million or more. These projects require massive datasets, specialized teams, and long-term development.

These are rough estimates. Still, the cost of developing AI software can be largely affected by several factors. Some of the crucial factors include:

  • Data requirements. More data means more processing and preparation, which increases cost.
  • Model complexity. Obviously, simpler models cost less.
  • Integration needs. The extent to which the AI software needs to work with your existing tools and platforms.
  • Team expertise. Specialized AI engineers and data scientists usually command higher salaries, impacting the overall cost.
  • Maintenance & scaling. AI solutions often need ongoing support and updates, which add to the overall expense.

The Future of AI Software Development

Seems like artificial intelligence is no longer a hype or a newfangled trend. It looks like it’s a new norm for our personal and business lives. The global AI market size shows no signs of slowing down. According to Statista, by 2030, the market is projected to surge beyond $826 billion. With this in mind, we can only expect AI to further evolve and be able to solve even more complex problems, even though it is already capable of things that once seemed possible only for the human mind. 

article image

It is expected that these emerging trends are going to play a huge role in shaping the future of AI software:

  1. A big push towards generative AI. It’s what we’re already seeing with things like ChatGPT and AI image creation. AI is not just about answering questions or recognizing patterns anymore. It’s actually creating content, designs, and even code.
  2. AI’s integration with everyday business tools, like automation or personalized experiences. More businesses are using AI to handle repetitive tasks or to provide better, more tailored customer service, and that’s only going to grow.
  3. AI ethics and regulations. Governments and companies are likely to get serious about making sure AI is used responsibly.
  4. AI and healthcare seem to be on the verge of something big. From predicting diseases to aiding surgeries, AI is making healthcare smarter and more precise.

To put it short, we’re just scratching the surface of how AI will impact our lives. If you ask, “Can I create my own AI software?”, the answer is yes – many tools and frameworks are available to help build your own solutions with the right expertise.

Partner with SapientPro

SapientPro is your reliable tech partner for Web3, ML, and AI development. Our team has 7+ years of expertise in the industry and over 200+ successful projects. 

For example, we created an AI-powered call-center with a <700 ms response time and $1 cost per agent that helped our client enhance their sales & customer experience. Looks impressive, doesn’t it? Discover more about our clients’ successes on our case studies page, or schedule a call with our experts to discuss your project today!

article image

Summary

As AI continues to advance, the question for founders isn’t if they should adopt AI, but when and how. Building AI software might seem daunting, but with the right approach, clear goals, and the right team, it’s entirely possible to harness its potential and transform your business. 

 

Start small, iterate, and scale – just like with any other technology. The opportunities are endless, and AI could be the key to unlocking the next level of growth for your company.

SaaSBLOCKCHAIN
related news
background image
SaaSSaaS Security: Risks, Challenges & Best Practices to Secure Your Data

SaaS is everywhere these days, and for good reason. It’s become the go-to solution for businesses looking for flexibility, lower costs, and easy scalability. Statista reports that in 2024, there will be approximately 9,100 SaaS companies in the United States alone. That’s a massive industry boom! But here’s the thing: while SaaS brings a ton of advantages, it also comes with its own set of challenges – especially when it comes to keeping your data secure. Our article lists the risks you can face with SaaS and – what’s most important – how to tackle them. Whether you’re a business owner who wants to keep things running smoothly or an IT manager responsible for your company’s tech, you’ll find useful advice here to safeguard your data and operations.

Illya

8 min read

background image
SaaSAll you Need to Know about SaaS Application Development in 2025

Have you ever wondered whether cloud-based applications can become key drivers of your company’s growth efficiency? With SaaS software solutions dominating the cloud computing market today, it seems obvious that a customer-prioritized engagement piece like an app or platform is gonna turn out a game changer. In this post, we invite you to delve into the keynote aspects of Software-as-a-Service solutions, such as their types and core features, development stages and challenges, examples of SaaS products, cloud software relevance in 2025, and many more. You will also find some snippets of the latest statistics that highlight key cloud apps trends, together with a couple of tips on how you can impede overspending in case you intend to invest in a SaaS app project. Read through our article for hot and helpful hints!

Illya

8 min read

background image
BLOCKCHAINHow to Create an NFT Marketplace in 2025: Step-by-Step Guide

NFTs are new to the world. Still, even today, the cost of a single NFT meme can reach several hundred dollars, and large brands are increasingly using NFT in marketing and PR. According to Statista, in 2025, the global NFT market will cover more than 11.6 million users. This makes NFT art marketplace development not only an exciting project, but also an opportunity for artists and collectors to monetize their presence in the digital arena. At SapientPro, we have diverse experience in working with NFT technology. For example, we developed an NFT minting website for the metaverse. Now, it is time to share our expertise in NFT marketplace software development. In this article, we discuss the NFT marketplace development process, how to create an NFT marketplace like Rerible, as well as how to create NFT marketplace like Opensea.

Max

10 min read

More related news