Are you constantly finding that customer support struggles to handle user inquiries effectively? Or perhaps you’re drowning in vast amounts of data, spending endless hours on analysis? Since OpenAI launched the first version of ChatGPT in 2022, making a breakthrough in artificial intelligence, these challenges can be easily addressed by developing your own AI assistant. Right now, they are handling over 85% of user requests!

 

But you might be wondering: how do you create a solution like this? We have the answer. Recently, our team developed an AI assistant specifically for the healthcare sector, capable of analyzing patient data, providing emotional support, and ensuring users adhere to their treatment plans.  Keep reading and find answers to your questions, all backed by our hands-on experience in building AI and ML solutions.

 

TL;DR

  • Overview the whole AI assistant development process, inspired by our recent healthcare project.
  • Walk through necessary things in every step: from data gathering to deployment. 
  • AI assistants handle 85% of user requests, reducing workloads and improving user experience.
  • Data gathering, model training, interface development, testing, and deployment are the five most crucial steps in AI assistant development. 
  • AI assistants evolve to "think" like humans and recognize emotional cues, enabling more empathetic interactions.

Examples of successful AI assistants:

  • The AI assistant Erica has helped Bank of America increase its revenue by 19%. In 2024, the solution surpassed 2 billion interactions by offering personalized financial advice. 
  • Mayo Clinic reduced routine task time by 50%, improving patient engagement and symptom tracking. 
  • Carnegie Learning has grown revenue eightfold thanks to its strong emphasis on AI-powered educational tools. 
  • We at Inoxoft presented our own case. The project resulted in increased patient adherence to treatment by 30%, and reduced emergency hospitalizations by 20%, with 85% user satisfaction.
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Contents

How We Developed an AI Assistant to Help Patients Stick to Their Treatment Plans

A medical company recently approached us because their patients were struggling to stick to their treatment plans. After analyzing the issue, we suggested developing an AI assistant as a solution. While we can’t share all the details due to an NDA, let’s take a look at how it works. 

The Challenge

The issue of patients not following their doctors’ instructions is nothing new. Our observations show that more than half of medical institutions face this problem, which in turn leads to increased treatment costs.

To find a solution, our team started with a discovery phase, where we surveyed both patients and doctors. We identified several key issues:

  • Patients often struggle to understand medical instructions.
  • They lack motivation and support, especially for long-term treatments.
  • Emotional problems frequently go unnoticed, reducing the effectiveness of treatment.
  • Doctors often don’t have enough time to communicate adequately with patients due to high workloads.

Based on the findings from the discovery phase, we recommended developing an AI assistant with the necessary functionality to address these issues.

The Implemented Features

The AI assistant we developed addresses several key issues. Firstly, it frees up valuable time for doctors, and secondly, it helps patients receive quality care.

Here are the features we implemented:

  • Natural language understanding. The assistant “translates” complex medical terms into simple, understandable language for patients.
  • Personalized recommendations. We integrated the AI assistant with electronic medical records, following all HIPAA regulations. This allows it to track medical history and individual health factors to provide personalized advice.
  • Medication reminders. We set up push notifications to help patients remember to take their medication.
  • Emotional support. At any time, patients can talk to the AI assistant to share their concerns. If needed, the assistant can switch to facilitating communication with the doctor.

Discover more about the topic:  How to Build a Virtual Health Assistant

How to Create an AI Assistant: A Guide Based on the Inoxoft’s Real Project

Facing a similar challenge or looking to solve your business problem with an AI assistant? Let’s discuss your project. 

To provide more clarity, we’ll walk you through the step-by-step process of creating such solutions and share the challenges we encountered during this project.

How to Develop an AI Assistant: Our Experience 

As we mentioned earlier, everything started with the discovery phase, where we conducted interviews with patients and doctors. This later allowed us to meticulously design the functionality of our AI assistant. Now, let’s walk through what else is needed to create a similar solution.

How to Create an AI Assistant: A Guide Based on the Inoxoft’s Real Project

Step 1: Data Collection and Preparation

To ensure a high level of personalization for your AI assistant, you need data. Its type depends on your use case. For instance, in customer service, you might need standard responses or a knowledge base. For marketing, having a clear tone of voice is key.

Since we were working in the healthcare sector, we had to integrate the AI assistant with EMRs. This gave us access to:

  • Lab results

  • Medical history

  • Demographic data

  • Lifestyle habits

  • Personal routines

However, medical data is highly confidential and protected by HIPAA and GDPR. To ensure security, we designed a data processing policy early on. Every patient had to consent, and all collected information was anonymized before being used to train the model. This made it impossible to identify any specific patients from the data.

Step 2: Training the AI Model

The AI assistant needed to understand complex medical terminology while explaining everything in a simple and accessible way for patients. To achieve this, we chose a pre-trained GPT model, already trained on large volumes of text data.

But just using pre-trained models wasn’t enough—we needed to ensure clarity for users. So, we created parallel datasets that contained complex medical terms alongside their simpler explanations. We also fine-tuned the models with examples of patient questions and answers. This way, our AI assistant can match medical terms with more understandable wording.

explains Nazar Kvartalnyi, our COO.

Step 3: Designing the Mobile App Interface

Solutions for healthcare projects should be accessible to all users, regardless of their device’s operating system. That’s why our team recommended developing a cross-platform AI assistant for mobile devices using React Native.

We often advise clients to choose cross-platform development because it:

  • Reduces time to market by 20-30%. The functionality is built for both platforms at the same time.
  • Saves costs. You’re essentially creating one app with a shared codebase instead of two separate ones, which is particularly beneficial for startups with tight budgets.
  • Simplifies maintenance. Updates and bug fixes can be made in one codebase, making it easier to manage.

When developing the AI assistant, we also put a strong emphasis on UI/UX design. Since the app will be used by people of various age groups and different levels of tech-savvy, it needs to be as straightforward and user-friendly as possible.

Step 4: Comprehensive Testing

Bugs are inevitable, but the key is to catch them before your solution reaches the users. That’s why we ran extensive testing on our AI assistant early in the development phase.

Our testing process included:

  • Functional testing. Our QA team ensured the AI assistant was intuitive and easy to use. We tested all the app’s features, found a few bugs, and fixed them quickly to ensure a smooth user experience.
  • Load testing. When a large number of users access the solution simultaneously, it can cause slowdowns or crashes. To prevent this, we designed the project architecture to handle high traffic efficiently, and our load testing results confirmed the system’s stability.
  • Security testing. Since we’re working with sensitive medical data, security was a top priority. Our team analyzed potential vulnerabilities, conducted penetration testing using multiple scenarios, and verified that all data is encrypted during both transmission and storage.

Step 5: Deployment

“For deployment, we went with a cloud-based solution. Since users might require information in emergencies, the app needed to remain accessible at all times. We also wanted it to be scalable and reliable, knowing the user base could grow,” explains our COO.

When selecting a cloud platform, you have options like AWS, Microsoft Azure, and Google Cloud. We chose AWS for its reliability and performance.

These are the key steps to building an AI assistant. However, every project is unique, and what works for one might not work for another. That’s why partnering with an experienced team is the best way forward.

If you have any questions or want a free consultation for your project, we’d be happy to help!

How AI Assistants Work for Companies: Inspiring Examples from Different Domains 

The great thing about AI assistants is their flexibility—they can be customized to fit any task you need, whether it’s employee training, customer communication, handling routine tasks, or even virtual shopping. You can simply reach out to a developer, like us, explain your needs, and get a solution tailored specifically to your request

Bank of America and Erika 

Banks constantly compete for client attention, with major institutions controlling over 85% of the global market. Meanwhile, 30% of consumers feel that banking products are the same. If you struggle to stand out, developing an AI assistant could be a game-changer. Just look at how it worked for one of the world’s largest banks.

How to Create an AI Assistant: A Guide Based on the Inoxoft’s Real Project

In 2018, Bank of America introduced Erica, an AI chatbot designed to help customers manage their finances. You can ask it to check your account balance, transfer money, or even provide your FICO credit score. But the real value lies in its personalized advice. For example, you can ask Erica to create a savings plan or suggest ways to cut monthly expenses. It uses predictive analytics, meaning it looks at your unique situation to offer advice that makes sense for you.

The results have been remarkable. Since its launch, over 2 million users have interacted with Erica daily, and 1.2 million have received personalized recommendations to help them reach their financial goals. By 2024, it surpassed 2 billion total interactions! Not only has this lightened the workload for customer service teams, but it’s also attracted new clients to the bank. What’s more, Erica has helped the bank to boost revenue by an impressive 19%. 

How to Create an AI Assistant: A Guide Based on the Inoxoft’s Real Project

Aditya Bhasin, Chief Technology & Information Officer at BofA, shared some insight into this success:

“Bank of America has invested $3 billion or more on new technology initiatives each year for over a decade, including significant investments in AI that allow us to deliver a seamless user experience and industry-leading personalization for our clients banking online or on their mobile devices. Our continued investment in Erica’s AI-powered capabilities enables us to quickly respond to voice, text chat, or on-screen interactions from clients who need assistance with financial transactions, and to proactively deliver personalized insights and advice at key moments.”

 

Erica is the definition of how Bank of America is delivering personalization and individualization at scale to our clients,

said David Tyrie, Chief Digital Officer and Head of Global Marketing at Bank of America. 

Mayo Clinic and its groundbreaking AI assistants 

As a company specializing in healthcare solutions, we’ve noticed a growing trend in telemedicine—just in 2024 alone, over 116 million users sought online consultations with doctors. But this is only the beginning, as AI assistants can now help you check symptoms, provide basic recommendations, and even book appointments with doctors.

Mayo Clinic has been a pioneer in this area. Back in 2017, they launched a voice-enabled application, Mayo First Aid, which could offer basic first aid advice. Then, in 2019, the company, in collaboration with technology provider Orbita, launched a web-based AI assistant to handle common medical situations. Now, such assistants are a routine part of the clinic. They help patients track symptoms and quickly schedule appointments with doctors. As a result, the time spent on routine tasks has decreased by 50%.

In a press release, Cris Ross, the CIO of Mayo Clinic, shared some insightful thoughts about the intersection between AI and medicine:

Our prioritization of patient safety, privacy, and ethical considerations, means that generative AI can have a significant and positive impact on how we work and deliver healthcare.

Carnegie Learning and Advanced Math Learning with AI Assistants

There’s no one-size-fits-all approach, especially when it comes to education. During his TED talk, Sal Khan, the founder of Khan Academy, said:

We’re at the cusp of using AI for probably the biggest positive transformation that education has ever seen. The way we’re going to do that is by giving every student on the planet an artificially intelligent, but amazing personal tutor. We’re going to give every teacher on the planet an amazing, artificially intelligent teaching assistant

Math is arguably one of the toughest subjects for students, but Carnegie Learning has shown that the experience can be truly unique. The company began developing AI-powered solutions for education back in the 2000s, but their breakthrough came with MATHia, a tool that continues to evolve today. This assistant acts as a personalized math coach, guiding students with individualized feedback and adapting the curriculum to their needs. So far, MATHia has been implemented in over 1,000 schools across America, and those who have already tried it report a 20% increase in test scores compared to traditional methods.

Thanks to investing in AI-driven products, the company has seen remarkable growth—boosting its revenue eightfold and expanding its team five times since 2016.

 

“There is no silver bullet for education, but artificial intelligence will allow us and does allow us to differentiate among students in a very personalized way”, says the company’s CEO Barry Malkin.

Our Predictions for the Future of AI Assistants 

Artificial intelligence and its potential have been hot topics in recent years, with tech giants like Google, OpenAI, IBM, and Microsoft working on advancing the field. We’ve been following the latest AI trends and want to share our vision for the kinds of AI assistants that could be developed in the near future.

From Prompt Responses to Human Thought Models

In September 2024, OpenAI introduced a new series of artificial intelligence models called o1. Their main feature is that they can “think” before answering a question. This promises to be a true breakthrough in AI and the development of smart assistants.

Simply put, o1 provides answers based on the concept of a thought chain—much like how humans think through a problem before responding. This means that in the future, AI assistants may, for example, suggest new treatment plans or analyze how each student learns to offer the most optimal learning path.

As Sam Altman, CEO of OpenAI, said at T-Mobile Capital Markets Day 2024:

I believe that the most sustainable economic growth in the world comes largely from scientific progress. And if AI can accelerate that—if it can help us invent new things, cure diseases, find better energy sources—that will be a huge win.

AI Assistants with Empathy

In 2005, futurist Ray Kurzweil published The Singularity Is Near: When Humans Transcend Biology. In it, he claimed that shortly, computers would reach a level of “superintelligence” that would surpass humans. While this seemed unimaginable then, by 2024, the idea of empathetic robots is becoming a reality.

Most AI models today excel at tasks like data analysis and automation, providing standard responses. However, there are fields—such as customer service, mental health support, and marketing—where emotions are crucial. And scientists are already working on this!

For instance, former Google employee Dr. Alan Cowen founded the project Hume AI. Here, researchers, scientists, and ethicists are developing systems that don’t just process language but also understand emotional context. They can recognize emotional cues from text, voice tone, and facial expressions. So, AI assistants capable of supporting people even in difficult life situations are just around the corner.

To Conclude 

AI assistants are a versatile solution for businesses, capable of tackling almost any challenge you might face. Whether you need to lighten the load on your support team, enhance customer service quality, analyze data, or address a variety of other needs, AI can help. These solutions are applicable across different industries, including healthcare, finance, and education. The key is to clearly understand what you need and to partner with an experienced development team.

Are you looking for a reliable partner? We’re here to help. Our team specializes in creating AI solutions that address real business challenges. With us, you can leave the technical details to the experts.

Share your project vision with us, and let our team handle all the details.

Frequently Asked Questions

What challenges can I face when developing an AI assistant?

If you don’t have experience in developing such solutions, it can be quite a challenge. Companies often encounter the following issues:

  • Poor data quality. Your AI assistant learns from the data you provide. If it’s subpar or incomplete, the results will be unsatisfactory. Keep a close eye on the quality of the data you use.
  • Natural language understanding. Users may communicate with the assistant using slang, jokes, or complex phrases. This can lead to the AI misinterpreting their requests. That's why creating the solution is just the beginning; you'll need to continually enhance the natural language processing algorithms.
  • Insufficient personalization. Ideally, the AI assistant should provide context-aware responses. However, many companies struggle with this. To improve personalization, consider implementing recommendation systems, developing algorithms to remember past interactions, and leveraging user data to refine your models.
  • Data security. Confidentiality is vital for any AI-based solution. While data is essential for functionality, it can also be vulnerable. Our recommendation is to stay vigilant about security measures.

As you can see, developing an AI assistant requires a diverse set of skills. It’s always a good choice to collaborate with an experienced development team. You can rely on us to help with that.

How can you ensure the ethical and unbiased behavior of an AI assistant?

This is a fairly common issue that arises from poor data management. Here are a few tips to avoid it:

 

  • Develop feedback mechanisms so users can report biased responses.
  • Collect data from various sources to diversify it. Clean the data as needed.
  • Regularly test the model.
  • Help users understand how the AI model works and how it draws conclusions.

How do you evaluate the performance of an AI assistant?

To evaluate performance, you can use various methods:

  • Implement performance metrics. For example, analyze response accuracy, response time, completion rates, and user satisfaction.
  • Gather feedback. Add rating buttons for responses or analyze interaction time with the AI assistant.
  • Create test cases. Develop a set of typical questions and tasks that users might ask the assistant. This way, you can test its ability to handle real-world scenarios.