technology Archives

Healthcare MVP Development: Top Mistakes to Avoid for Successful Product Launch

Posted on November 25, 2024November 25, 2024 by Volodymyr Marynenko

A minimum viable product (MVP) is a product development approach that allows startups to realize their potential. This method has demonstrated excellent results in social media, marketplaces, and entertaining ventures (like Instagram, Amazon, or Spotify). Currently, one of the most profitable spheres of startuping is healthcare. According to Crunchbase, this industry still accounts for over 50% of U.S. Series A funding in 2024.

However, developing an MVP healthcare product in today’s highly competitive market is no easy task. There are plenty of risk factors that can be easily forgotten or underestimated. Moreover, MVP development in this niche is particularly challenging due to regulatory requirements, data sensitivity, and scalability demands.

Therefore, we at Devtorium, with hands-on experience in healthcare MVP development, want to share practical advice for minimizing those risks. This blog will outline critical mistakes and explain how to avoid them with the right strategies and expert advice.

Mistake 1: Mishandling Healthcare Data

Problem: Healthcare MVPs often manage enormous volumes of data like patient health records, diagnostic information, treatment histories, etc.

Thus, improperly designed data systems can lead to severe problems. Your MVP can be limited in delivering actionable patient insights and predictive analytics. Moreover, failure to organize data effectively causes slow performance, duplication of effort, or even loss of critical information. Healthcare MVP must conform with the FHIR (Fast Healthcare Interoperability Resource) standard developed by HL7 (the Health Level 7 standards organization). This standard lets the exchange of healthcare e-data between different systems securely and privately.

Teams without domain expertise may also underestimate data interoperability challenges like integrating with existing electronic health records (EHRs).

Solution: Our experts recommend implementing FHIR-compliant encryption, as prioritizing this standard will ensure robust data protection while supporting seamless interoperability in healthcare systems. Additionally, you can try to involve data scientists in MVP development, as they can provide your system with tools like advanced analytics, predictive modeling, and personalized recommendations.

Mistake 2: Neglecting Compliance Requirements

Problem: Some startups underestimate the importance of adhering to regulatory frameworks, as they think they can address compliance after the MVP is live. They focus on building features and functionalities but often overlook the need to comply with regulations for handling sensitive data, such as personal health information (PHI). Failing to integrate compliance like HIPAA (Health Insurance Portability and Accountability Act), GDPR (General Data Protection Regulation), or similar ones from the start can lead to costly delays, fines, or product rejection. Regulations aren’t optional— they are foundational in healthcare MVPs.

Solution: Firstly, you should integrate compliance early in MVP development, as retrofitting it into an existing product can be costly and inefficient. Secondly, it is better to maintain detailed records of how your MVP collects, processes, and stores data. Regulatory audits often require transparent documentation. Last, the most critical point is implementing secure storage, encryption protocols, and a controlled access system. Collaborating with a tech partner experienced in healthcare compliance can significantly reduce risks and ensure your MVP is ready for market.

Mistake 3: Imbalance between Simplicity and Scalability

Problem: The most challenging task while creating MVP is to find the right balance between a functional and a scalable foundation for the project. Typically, inexperienced companies either overload the MVP with too many features or focus solely on short-term goals without planning for future product expansion. These risks occur when the team does not focus on the main goals and their development. Both these issues cause high chances of failure due to poor user experience, bugs, and wasted time and money.

Solution: To prevent these extremes, you should design the MVP simple enough to launch quickly but robust enough to accommodate future growth. Also, it would help if you avoided “scope creep” by identifying the MVP’s minimum viable goals. At the same time, you better work with a technical team experienced in designing scalable architectures such as cloud-based infrastructures. We advise using cloud-based infrastructures intended for healthcare, like AWS HealthLake or Microsoft Azure, to manage large-scale data.

Mistake 4: Ignoring User Feedback, Testing, and Iteration

Problem: Many startups underestimate the importance of testing and iteration in MVP development, believing that users will not mention bugs and technical issues since it’s “just an MVP.” They often launch their product without proper validation channels and treat the MVP as a one-time release rather than an iterative process. This mindset leads to poor first impressions, decreased user engagement, and missed opportunities. When teams fail to establish proper feedback mechanisms, they risk losing users permanently.

Solution: Our experts advise implementing a comprehensive testing strategy before launch. It suggests establishing clear feedback channels such as in-app surveys, customer interviews, and email questionnaires to collect user insights systematically. Regular iteration based on real user insights is the key to creating a successful MVP that meets user needs.

Mistake 5: Choosing the Wrong Development Team

Problem: Building an MVP is a time-sensitive and resource-intensive process, especially in the healthcare industry, where compliance, scalability, and precision are critical. That’s why hiring an inexperienced or lacking domain expertise team is not a good idea, even though they propose low prices for complex development services.

Hiring the wrong team can lead to costly delays, sub-optimal results, and technical debt. While some businesses try to assemble teams through freelancer platforms, managing scattered individuals can result in miscommunication and fragmented development efforts. This risk is amplified for healthcare MVPs, as the lack of expertise in regulatory requirements like HIPAA or GDPR can jeopardize the project entirely.

Solution: To ensure effective MVP development, partner with a specialized company that understands the healthcare sector. Look for a team with a proven track record of building compliant, scalable solutions for similar industries. Evaluate their portfolio to be certain of compatibility with your project’s goals. Working with experienced professionals gives you access to a full-cycle service that covers discovery, prototyping, design, development, and testing.

Conclusion: How to Make a Successful MVP Launch

Developing an MVP requires careful planning and expertise in the highly regulated healthcare industries. You can create a product that paves the way for long-term success by avoiding common pitfalls.

Our team has deep domain expertise in MVP development for healthcare to deliver high-quality solutions. From compliance and scalability to data security and user-focused design, we’re here to guide you through every stage of the MVP process.

Ready to take the first step toward building a successful MVP? Contact our team for a free consultation and receive personalized advice from our experts.

AI Law Regulations in EU & US

Posted on October 22, 2024November 25, 2024 by Volodymyr Marynenko

Every time new technologies enter our lives, we must become pioneers and adapt to the new rules of the game. AI is not an exception. This innovation has already made its way into every sphere, from entertainment to science. Moreover, there are countless ways to use AI in real-life business. However, AI cannot remain unregulated without specific frameworks and rules. If such a powerful tool appears in the wrong hands, it can be used for selfish or harmful purposes.

The prospect of AI being used in deep fakes, fraud, and theft of personal data or intellectual property is not just concerning but an urgent issue. The Center for AI Crime reports a staggering 1,265% increase in phishing emails and a nearly 1,000% rise in credential phishing in the year following the launch of ChatGPT. This highlights the urgent need for AI regulation.

In response, significant regions such as Europe and the US have started developing principles regulating AI to protect their citizens, companies, and institutions while maintaining technological development and investment. The regulations contain critical nuances that must be considered when developing or implementing AI technologies. In this blog, we will explore and compare European and American AI regulations.

The EU AI Regulation: AI Act

Regulation on a European approach for AI

The AI Act by the European Union is the first global and comprehensive legal framework for AI regulation. Basically, it is a set of measures aimed at ensuring the safety of AI systems in Europe. The European Parliament approved the AI Act in March 2024, followed by the EU Council – in May 2024. Although the act will fully take effect 24 months after publication, several sections will become applicable in December 2024, primarily focusing on privacy protection.

In general, this act is similar to the GDPR — the EU’s regulation on data privacy — in many respects. For example, both cover the same group of people — all residents within the EU. Moreover, even if a company or developer of an AI system is abroad, if their AI software is designed for the European market, they must comply with the AI Act. The regulation will also affect distributors of AI technologies in all 27 EU member states, regardless of where they are based.

The risk-based approach of the AI Act is comparable to the GDPR’s. It divides AI systems into four risk categories:

The minimal (or no) risk category is not regulated by the act (e.g., AI spam filters).
Limited-risk AI systems must follow transparency obligations (e.g., users must be informed when interacting with AI chatbots).
High-risk AI systems are strictly regulated by the act (e.g., using AI systems to enhance critical infrastructure).
Unacceptable risk is prohibited (e.g., biometric categorization).

Non-compliance with certain AI practices can result in fines of up to 35 million EUR or 7% of a company’s annual turnover.

The US AI Regulation: Executive Order on AI

Although the United States leads the world in AI investments (61% of total global funding for AI start-ups goes to US companies), its process for creating AI legislation is slower and more disorganized than the EU’s. There is no approved Congress policy on AI systems regulation in the US for now. However, the White House issued an Executive Order (EO) on Safe, Secure, and Trustworthy Artificial Intelligence in October 2023. It sets federal guidelines and strategies for fairness, transparency, and accountability for AI systems. As with the AI Act, the EO aims to balance AI innovation with responsible development.

The AI Executive Order also focuses on guiding federal agencies in implementing AI systems and outlines a series of time-bound tasks for execution. It directs federal agencies to develop responsible AI governance frameworks. The National Institute of Standards and Technology (NIST) leads this effort by setting technical standards through its AI Risk Management Framework (AI RMF). This framework will shape future guidelines while aligning with industry-specific regulations. Federal funding priorities further emphasize AI research and development (R&D) to advance these initiatives.

The most important thing to mention about EO is that it does not have the same enforcement power as a law. Instead, EO should be viewed as a preparatory stage of AI regulation, and its recommendations should be gradually implemented if you plan to work in the US market. For example, any AI software development company should start conducting audits, assessments, and other practices to ensure their safe approach.

Comparison Table

Legal Force:

The AI Act will become a binding law across all EU member states once 24 months pass. After that, mandatory compliance will be required from everyone providing AI systems in this region. In contrast, the US Executive Order has less legal force. It sets essential guidelines for federal agencies, but it lacks the binding legal authority of a law passed by Congress. The EO’s enforcement is limited to federal government activities and impacts the private sector less. Thus, even a change of president can provoke future revocation.

Regulatory Approach:

The AI Act applies to all AI systems, categorizing them from unacceptable to minimal risk to ensure that every AI system across industries falls under specific regulations. The US OE focuses on sector-specific regulations, targeting high-impact industries like healthcare, finance, and defense. While this approach fosters innovation, it may lead to inconsistent risk management across sectors.

Data Privacy:

The AI Act uses practices from GDPR to enforce strict rules around data processing, privacy, and algorithm transparency. The US privacy regulations remain fragmented, with state-level laws such as the CCPA and BIPA applying at the state level but no federal AI-specific privacy law.

Ethical Guidelines:

The EU AI Act emphasizes ethical AI development, focusing on fairness, non-discrimination, and transparency. These principles are embedded within the legislation. The US Executive Order promotes similar values but through non-binding recommendations rather than legal mandates.

Support for Innovation:

The EU AI Act aims to balance strict regulation with promoting innovation, offering AI research and development incentives within an ethical framework. These actions help foster AI innovation while ensuring public safety. The US supports innovation through federal funding and AI research initiatives, but companies have more flexibility to self-regulate and innovate without the stringent compliance measures seen in the EU.

Conclusion: Challenges of Current AI Regulations

The EU and the US face global challenges in balancing AI regulation and innovation. The EU AI Act imposes numerous restrictions that limit the possibility of developing revolutionary AI software, while the US EO, although offering more flexibility and encouraging innovation, lacks comprehensive regulations. The evolving nature of AI technology makes it difficult for regulations to keep pace, and businesses must navigate complex compliance requirements across different regions. However, for developers working on projects, adhering to these regulations is crucial to avoid legal risks and ensure the ethical use of AI.

At Devtorium, we help businesses navigate these challenges by ensuring compliance with the necessary AI regulations. Our team can guarantee that your AI solutions meet both EU and US standards, allowing you to focus on innovation. For more details, contact us today and let Devtorium’s experts guide your AI development toward full regulatory compliance.

If you want to learn more about our other services, check out more articles on our website:

Generative AI Comparison: Best AI Models Available in 2024

Posted on September 5, 2024September 5, 2024 by mkoftielieva

AI is a revolutionary technology, and its rapid growth is why you need some generative AI comparison sources right now. This tech has spread and evolved so fast that it’s hard to understand exactly what the solutions available on the market are capable of. Despite having some similar functionality, generative AI tools differ quite a bit. So, read on to learn the best time to use each top AI model.

Who Needs This Generative AI Comparison Guide?

If you use the Internet today, you will benefit from reading this simple guide on AI model comparison. This technology is quickly spreading to different areas of our daily and, most of all, professional lives. Therefore, knowing which AI tool to use and when is key to staying ahead.

There are numerous areas of business where you can implement AI, so you will definitely find ways to use this technology to boost your outcomes.

Today, the market provides a variety of large language models (LLMs). Each of them has different tools and capabilities. Some are best used for coding only, while others perform exceptionally well in creative tasks. As a result, it’s pretty confusing and complicated to pick the right AI tools for your purposes. That’s why Devtorium R&D experts prepared this short guide on four of the most effective LLMs and their best use cases.

Comparison of Generative AI Tools: Benefits and Uses

ChatGPT

ChatGPT stands for “Chat Generative Pretrained Transformer.” OpenAI developed this LLM and currently offers three models: GPT-3.5, GPT-4, and GPT-4o.

Chat GPT-3.5 is a free version that anyone can access. However, it has many limitations, like no image input or complex task processing.
Chat GPT-4 is a $20/month subscription version designed for professional use. The model has excellent contextual understanding and creative reasoning. Among its drawbacks is slower task processing speed due to model complexity.
Chat GPT-4o (or ChatGPT-4 Turbo) is a brand-new version of ChatGPT-4 that offers similar capabilities but is cost-efficient and speed-optimized. This tool has free and paid plans with varying limits. Also, among its inputs can be text, images, audio, and video. Even though GPT-4o has a bit worse context retention than Chat GPT-4, this model still balances exceptional outputs with processing speed.

Best ChatGPT applications:

Cost-effective solution
For budget-conscious projects, models like ChatGPT-4o offer a balance between performance and affordability.
Hard prompts
Advanced versions like ChatGPT-4o would be effective if complex or nuanced responses are necessary. Moreover, according to the LMSYS Chatbot Arena Leaderboard, the best hard prompt performance out of 126 AI models shows ChatGPT-4o.
Longer queries
ChatGPT excels at understanding context and coherence across extended conversations, making it ideal for in-depth discussions or multi-step tasks.
Versatile applications
From creative writing to code generation, ChatGPT developed its available functions evenly.

Claude

Not a common name in most AI comparison guides, Claude is a family of AI language models developed by Anthropic. These LLMs focus on providing safe AI interactions. Claude 3 Haiku, Claude 3 Opus, and Claude 3.5 Sonnet are among the models currently available to general users.

Claude 3 Haiku has the highest response time of all Anthropic models. It’s ideal for concise prompts and fast tasks. It’s also more affordable compared to others. However, it has limited creative capabilities and contextual understanding. It’s best suited for mobile application chatbots and instant messaging.
Claude 3 Opus is a mid-range AI tool with moderately fast latency. It balances creativity and accuracy, offering strong contextual retention and versatility.
Claude 3.5 Sonnet is the first release in the forthcoming Claude 3.5 model family. It’s one of the most advanced Claude models at the moment. This model is outperforming competitors in different spheres. However, it meets the same problem as Chat GPT-4: slower workflow speed due to more complex processing for richer output. Claude 3.5 Sonnet is now free on Claude.ai, while Claude Pro and Team plan subscribers can access it with significantly higher rate limits.

Best cases to use Claude:

Code generation
Claude 3.5 Sonnet generates optimal, almost bug-free code across 20+ languages, optimizing for project-specific needs and best practices. Also, according to the LMSYS Leaderboard, Claude 3.5 Sonnet is the best coding and math task-solving AI today.
Visuals analysis
Claude 3.5 Sonnet can analyze images, documents, and PDFs, extracting essential information for diverse tasks. It’s free with basic features, but paid plans offer enhanced capabilities and higher usage limits.
Ethical AI applications
Every Anthropic’s model is built on nuanced AI principles, prioritizing safety. It also means that all responses Claude provides must adhere to them. Claude is forthright about its limitations and potential biases, promoting responsible AI use.
Complex decision-making
Claude can handle intricate scenarios with multiple variables. Moreover, it is ideal for tasks that require deep contextual awareness.

Meta LLaMA

LLaMA (Large Language Model Meta AI) is an open-source LLM developed by Meta. Its main feature is its small resource intensity, which enables researchers and developers to meet complex requests on smaller hardware. At the moment, Meta offers three models of LLaMA: LLaMA 2, LLaMA 3, and LLaMA 3.1.

LLaMA 2 is a free-to-use OSS model of AI. It is the first openly available LLM instruction-tuned for text. It’s also great for commercial use if you struggle with huge budgets. However, this model is a bit outdated, so you can find inexpensive alternatives that provide higher performance.
LLaMA 3 is the next generation with some significantly upgraded features. This model is multilingual and has high prompt understanding. Unfortunately, it delivers bad performance in reasoning and math.
LLaMA 3.1 is a recent model built on LLaMA 3. It has improved reasoning and coding capabilities. Also, LLaMA 3.1 is the largest openly available model right now. So, if you want the best free-to-use AI model, this one will be a top hit according to our AI comparison.

When to use LLaMA:

Commercial applications
This AI model is ideal for many business applications without additional costs.
Meta integration
LLaMA can be easily integrated into Meta AI, Facebook, Instagram, and WhatsApp, providing advanced AI capabilities for content generation, customer interaction, and personalized user experiences.
Multimodal tasks
The model offers robust support for diverse languages and media formats, making it a versatile tool for global and cross-platform applications.

Comparison of generative AI models on the market

Gemini

Gemini is an AI model developed by Google DeepMind. It is positioning itself as a competitor to advanced LLMs like GPT-4. Four Gemini models made it to our I comparison guide: Gemini Ultra, Gemini Pro, Gemini Flash, and Gemini Nano.

Gemini 1.0 Ultra is Google’s largest model, designed for complex AI tasks. It offers maximum computational power for enterprise-level solutions and advanced AI research. This AI tool is great for advanced app integration.
According to ratings, Gemini 1.5 Pro is the best Google AI model. It excels in general performance across a wide range of tasks. Gemini Pro can process hard prompts and follow instructions almost perfectly, making it suitable for professional-grade tools and large-scale applications.
Gemini 1.5 Flash is a lightweight model of Gemini Pro designed for fast data analysis.
Gemini 1.0 Nano is the most powerful on-device model available. It is ideal for mobile apps, IoT devices, and edge computing with minimal resource usage.

Top Gemini use cases:

Overall best app
Currently, Gemini 1.5 Pro has the best results, outperforming all listed competitors.
Factual accuracy
Google’s AI relies on enormous databases and searches, ensuring its output is reliable and trustworthy.
Gmail integration
Using Gemini, you can enhance email management by providing smart reply suggestions, drafting assistance, and content generation directly within the platform.

Bottom Line: Which Model Is Best in AI Tools Comparison?

To sum it up, the current tech landscape offers a diverse range of AI solutions tailored to various business needs. From the advanced capabilities of ChatGPT and Gemini to the specialized performance of Claude and LLaMA models, each of these tools can help you.

Therefore, the best model for your specific case is the one that has the most advanced capabilities in the niche your business requires. If you want to benefit from AI integration, contact our experts for a free consultation today. We’ll help you choose a suitable AI model and develop the best implementation to enhance your business. If you want to learn more about our strengths, be sure to check our Devtorium’s case studies and verified Clutch reviews from our customers.

Will Small Business Be Affected by the AI Bubble Burst?

Posted on August 14, 2024August 13, 2024 by mkoftielieva

With the AI bubble burst and the stock market crash on everyone’s mind, it’s no surprise that many people are getting anxious. They do have due cause because even leading market strategists and analysts aren’t sure exactly how this situation will end.

Will there be a recession? Almost definitely. Will AI drop back into the obscurity of specialized tech? Certainly not!

Therefore, anyone using AI for small businesses in any capacity should not worry about any tech issues with these solutions. In fact, the global economic recession should provide more incentive to develop and implement AI solutions. In this kind of volatile market, a small business needs every advantage to actually stay in business.

Benefits of using AI for small business today.

When Will the AI Bubble Burst?

Since the COVID-19 pandemic, there have been many discussions about the global economic crisis. However, the stock market crash came as a shock anyway, and many leading experts and market strategists commented on it. The good news is that if we look at authoritative sources like Bloomberg, the expert prognosis is not favorable, but it’s not panic-inducing either.

Yes, economists are concerned, and there is a chance this is just the beginning of a massive recession. However, some leading experts, like Diana Iovanel, a senior markets economist at Capital Economics, say that instead of the AI bubble burst, we should expect its strengthening after this shakeup. The level of investment in AI technology by leading companies, such as Microsoft and Apple, continues to grow. Moreover, the technology itself evolves and attracts more users every day. Therefore, while a global recession might be an issue for the world economy, AI will remain one of the leading market forces for years to come.

How to Use AI for Small Business to Stay Safe in the Volatile Market?

The truth of the matter is that as a small business owner, you will be affected regardless of whether the AI bubble burst comes to pass. Events like stock market crashes are indicators of volatile global economic processes. This volatility alone is a major threat to the livelihood of small business owners worldwide. The best way to cope with this danger is by managing risks, and AI is a handy helper for this specifically.

Using AI for small business can help you achieve crucial outcomes, such as:

Cost reduction
Using AI tools and chatbots, you can automate processes and even replace some outsourced services, like customer support.
Innovation and competitive advantage
By implementing innovation, you can increase your value proposition for your customers. For many startups, innovation in the process itself becomes a business. For example, check out our case study of an insurance platform with a widget that can remake the entire process of buying a policy online.
Scalability and enhanced efficiency
Using AI for small business gives you some freedom to scale up or down as needed with minimal disruptions to the overall business processes. Moreover, automating some of the routine tasks reduces human error and frees up time for your qualified employees. They can use this time to work on solutions that will help increase your business resilience.
Better decision-making
AI goes hand in hand with data analytics services, which can unlock your access to invaluable insights. Making decisions based on concrete data will enable you to achieve the best possible results.

Learn more about practical implementations of this tech in our article How to Use AI in Small Business.

Bottom Line: Reduce Business Risks and Increase Resilience with AI

We don’t know when or if the AI bubble burst will occur. However, we know for sure that the practical value of implementing AI solutions in business processes will only grow. As the markets grow more competitive, the one who has an edge has the best chance of survival. Therefore, whether you launch a chatbot to enhance customer service or supplement your security with AI, you are moving in the right direction.

Business owners need to be proactive to stay ahead. In these times, this means using cutting-edge tech to its maximum benefit.

Are you interested in learning more about the topic? Check out more of our articles about AI and ideas on how to implement it for various businesses here.

If you are ready to start implementing AI solutions in your own company, set up a free consultation! Our experts will make a detailed analysis of your business and ideas. Then, we’ll give you a proposal on how to achieve the best results with the project.

Data Science Uses in Business, Healthcare, Finance & Engineering

Posted on July 24, 2024July 24, 2024 by mkoftielieva

Do you know how many valuable insights a company’s data hides? Data science uses are innumerable, and your business can’t afford to miss out on these opportunities. This complex study applies various practices from mathematics, statistics, programming, and artificial intelligence (AI) to analyze vast volumes of data. Data scientists use analytics to explain past causality and predict the future. Some things you can quickly improve with data science services include operational efficiency, decision-making, planning, and many more.

Unsurprisingly, data science has become one of the fastest-growing fields in every industry. According to the LinkedIn Emerging Jobs Report, data scientists have seen 37% annual growth in demand, and it keeps rising. Businesses use data science to gain an advantage over competition and achieve maximum efficiency. Devtorium data science experts utilize the power of data to optimize our clients’ performance and help develop AI-powered solutions. In this post, we will dive into diverse data science applications, focusing on their uses in business, finance, and healthcare.

Data Science Uses Across Industries

Some time ago, we posted a blog explaining what data science services are. Summing up that post, you can divide the general data science uses in any industry into three categories:

Predictive Analytics
Predictive analytics uses specific historical data to analyze captured patterns and forecast the future. These forecasts are in high demand across various industries today. With their help, you can anticipate market trends or customer behaviors. For example, retailers can predict inventory needs based on seasonal trends, while manufacturers can forecast demand to optimize production schedules.
Risk Management
Data science helps organizations identify and mitigate risks. This crucial function can prevent significant losses or disruptions by analyzing rash decision consequences. In addition, fraud detection is one of the excellent data science uses. You can analyze transaction patterns to identify anomalies indicative of fraudulent activities.
Process Optimization
Another way to implement data science is to analyze operational data to identify weaknesses and inefficiencies and optimize processes. The absence of good process optimization causes huge money waste. For instance, logistics companies need data science to optimize delivery routes, reduce costs, or improve service levels.

Benefits of using data science in business.

Data Science in Business Analytics

The benefits of data science in business come primarily from the fact that it allows you to understand your performance and market trends much better. As a result, you are able to make data-driven decisions and have a greater chance of success. Considering this, the best practical data science uses in business would be:

Strategic Planning and Decision-Making
By analyzing market trends, competitive landscape, and internal performance data, your company can drive growth and innovation while avoiding potential pitfalls.
Supply Chain Management
Data science benefits supply chain management through forecasting, inventory management, and logistics planning. When companies use data-driven insights to manage their supply chains effectively, they can reduce costs and improve service delivery.
Marketing Strategies
Marketing teams leverage data science to analyze customer data and optimize campaigns. Techniques like customer segmentation and sentiment analysis enable targeted marketing efforts, which increase conversion rates.

Data Science Uses in Finance

The financial sector is the main beneficiary of the many data science uses. Some key applications in this area include:

Algorithmic Trading
Algorithmic trading is a techniques that uses complex algorithms to execute trades at high volumes fast. Data science enables the development of these algorithms. Therefore, the users can analyze market data and execute trades based on predefined criteria. The result is increased efficiency and profitability of the business.
Credit Scoring and Risk Assessment
Financial institutions, such as banks, use data science to assess credit risks by analyzing many data points. These include credit history, transaction patterns, and social media activity. The results of such analyses lead to more accurate credit scoring and better risk management.
Customer Segmentation and Personalization
Financial institutions use data science to segment customers based on their behaviors and preferences. This application provides personalized financial products and services, enhancing customer satisfaction and loyalty.

Uses of Data Science in Healthcare

The number and diversity of uses of data science in healthcare seem to be growing by the day. From personalized marketing of healthcare services to analysis of X-rays, data science services help reduce mistakes and make us healthier. This field is developing rapidly, but for now, key areas of application in healthcare include:

Personalized Medicine
Data science enables personalized medicine by analyzing genetic data and medical histories to tailor treatments to individual patients. This approach increases the effectiveness of treatments and reduces adverse reactions. Moreover, healthcare providers use predictive analytics to forecast patient outcomes based on historical data. It can help in the early detection of diseases and timely intervention, improving patient prognosis.
Operational Efficiency
Hospitals and clinics use data science to optimize operations, such as patient flow management, staff scheduling, and inventory control. This approach leads to cost savings and improved patient care.
Drug Discovery and Development
Pharmaceutical companies use data science to accelerate drug discovery and development. By analyzing large datasets, they can identify potential drug candidates faster and more accurately, bringing new lifesaving drugs to market.

Data science uses in various industries.

Applications in Engineering

Engineering fields leverage data science to drive innovation, improve quality, and enhance efficiency. For instance, an automotive manufacturer can use data science to optimize its production line, increase productivity, and reduce production costs. Other notable applications include:

Predictive Maintenance
Data science helps predict equipment failures before they occur. This can be achieved by analyzing sensor data and maintenance records. This feature reduces downtime and maintenance costs, improving operational efficiency.
Quality Control and Defect Detection
Manufacturers use data science to enhance quality control by analyzing production data to detect defects early in the process. It can lead to higher product quality and reduced waste.
Design and Simulation
Engineers use data science to improve design processes through simulations and modeling. This application allows for testing and optimization of designs before building physical prototypes, saving time and resources.

In Conclusion

In our data-driven world, data science is a superpower that can transform businesses across various industries. By leveraging data-driven insights, companies can make better decisions, optimize operations, and drive innovation.

Are you ready to harness the power of data science for your business? Contact us today and let Devtorium’s data science experts help you unlock the full potential of your business.

If you’re interested in learning more about our other services, check out more articles:

What AI Cannot Do: AI Limitations and Risks

Posted on May 23, 2024May 23, 2024 by mkoftielieva

Looking at some articles right now, one could think that AI is omnipotent. However, it’s essential to remember that AI limitations exist, and there are many. Therefore, you should not expect it to be a universal cure for all problems. Unfortunately, it’s still brand-new technology, and its functionality has to be improved.

There are some incredible things you can achieve using AI. It’s also true that it can help your business save money by automating multiple processes and offering valuable analytics. However, some businesses take risks and apply AI in every situation. Such reckless use of tech can badly damage your business security and income.

According to the AI Incident Database, the number of AI misuse incidents in 2023 increased by 32.3% compared to the previous year. Nowadays, businesses must be realistic when considering the pros and cons of implementing AI. Devtorium Business Analysis and Information Security departments have the expertise to forecast probable risks caused by AI or other digital systems. In this blog post, our specialists will outline AI limitations and risks of implementing it without a system of fail-safes.

AI Limitations and Risks by Category

What AI cannot do: AI limitations track back to the quality of data

Data Dependency

Data is the main resource on which any AI system runs. Algorithms train on the provided data. Therefore, AI heavily relies on data quality, bias, and availability, which can impact performance and decision-making.

AI limitations caused by data:

Creativity
While AI is good at generating content based on existing data, it struggles with original or innovative thinking.
Flexibility
AI has limitations in adapting to new or unexpected situations outside its training data.
Bias
Data bias can occur at various stages of the AI lifecycle. However, bias often originates from the data used to train and test the models.

Contextual Misunderstanding

What AI cannot do is understand the context. At least, this isn’t possible with the current level of technology development. AI’s lack of contextual understanding refers to its limits in interpreting information. In other words, AI can fail to realize societal context or grasp the subtleties of nuance.

AI limitations caused by context:

Natural Language Processing (NLP)
While working on NLP tasks, like text analysis or translation, AI may have difficulty understanding language nuances such as idioms, slang, and dialects.
Visual recognition
AI algorithms can fail to recognize objects within their broader context in computer vision tasks.
Social interactions
AI-driven chatbots may struggle to catch the nuances of human conversation, including tone, sarcasm, or implied meanings. If you want to learn more about the capabilities of an AI-powered voice bot, click here.

Ethical Concerns

AI limitations is ethics are impossible to fathom because this technology doesn’t operate in a context that can be governed by ethics. Therefore, programming AI algorithms that could make ethical decisions is nearly impossible. The machine struggles to replicate feelings and emotions. It cannot make moral judgments in the same way humans can.

AI limitations caused by ethics:

Lack of empathy
AI lacks emotional intelligence and cannot empathize with human emotions. Therefore, as an example, it cannot prioritize emotional well-being as a factor.
Cultural contexts
AI systems may struggle to understand human cultural diversity. This can lead to biased or culturally insensitive outcomes, like stereotypes. As a result, implementing AI in some areas might work to reinforce existing inequalities.

The Black Box Problem

The Black Box Problem refers to the opacity of AI decision-making processes. AI algorithms are so sophisticated that it is hard to realize how they arrive at their conclusions. Therefore, a human might not be able to trust them completely. As a result, implementing AI in any position where the machine can make decisions that impact human life becomes a huge risk.

AI limitations caused by transparency:

Error correction
When AI systems make errors or produce unexpected outcomes, understanding why those errors occur is crucial. However, without a clear view of the internal workings of black-box AI models, diagnosing errors becomes much more difficult.
Trust
Users may find it challenging to rely on AI when they cannot understand how systems make decisions. The black box problem can be particularly concerning for critical applications such as healthcare or criminal justice.

Privacy and Security

As AI cannot function without data, concerns arise regarding collecting, storing, and using personal data. AI technologies also introduce new cybersecurity risks. Malicious actors may exploit vulnerabilities in AI systems to launch attacks, which presents new threats to financial systems, critical infrastructure, and national security.

AI limitations in the security field:

Tracking
AI-powered surveillance technologies, such as facial recognition and biometric systems, threaten privacy by enabling constant monitoring and tracking of individuals without their consent.
Malicious use
AI technologies can be leveraged for malicious purposes, including generating convincing deepfake videos, launching sophisticated phishing attacks, and automating cyberattacks.
Personal data
AI systems may analyze and process personal data without adequate safeguards. This could lead to unauthorized access, identity theft, financial fraud, and other cases of data misuse.

Bottom Line: How to Avoid Reckless Risks and AI Limitations?

It’s impossible to avoid risks and AI limitations entirely with the current level of technology. Therefore, it’s imperative to address them responsibly to maximize the benefits of AI implementation. Devtorium professionals are always ready to help you understand risks and develop efficient, safe, and secure AI applications for your business. Contact our team for a free consultation on how to use AI to your best advantage.

To learn more about the Devtorium Team and the multiple capabilities of AI, check out our other articles:

Use of AI in Cybersecurity: Modern Way to Enhance Security Systems

Posted on April 18, 2024April 18, 2024 by mkoftielieva

Now is definitely the time to use AI in cybersecurity. In fact, those who don’t do this put their businesses at great risk. While some reports aren’t highly detailed, according to cybercrime statistics, the damage caused by it amounts to $12.5 billion. This number is growing every year because complex systems are inherently vulnerable, and criminals use every tech innovation they can. So, no one can afford not to use AI’s help to build the best possible security system while hackers use this technology to exploit your weaknesses.

Modern threats need up-to-date solutions. Devtorium offers a wide range of cybersecurity services, and our security and R&D specialists are working to identify the best AI applications in this area. At the current level of technology, AI can automate tasks, improve threat detection, predict future attacks, and more. In this blog, you will learn about the benefits and drawbacks of using AI in cybersecurity systems.

Use of AI in cybersecurity: areas of implementation.

Use of AI in Cybersecurity: Applications in Various Systems

Network Security

This type of security protects a computer network from unauthorized access, misuse, or attacks. It involves developing a secure infrastructure for devices, users, and applications to work safely. The tools used in network security include firewalls, VPNs, and data loss prevention (DLP), as well as intrusion detection and prevention systems.

AI applications in network security:

Anomaly detection.
AI can analyze network traffic patterns to identify unusual activity that might indicate a cyberattack.
Automated threat mitigation.
AI systems can automatically take steps to isolate threats, such as blocking malicious IP addresses or quarantining infected devices.

Information Security

This system protects digital information, such as data stored in databases, files, or other repositories. It includes data encryption, access controls, and data backup and recovery. Devtorium is an ISO/IEC 27001:2013-certified company and our specialists are able to ensure complete security of your systems both with and without AI.

AI use in cybersecurity of information systems:

Data Loss Prevention (DLP).
AI can analyze data content to identify sensitive information and prevent unauthorized data exfiltration.
Threat intelligence analytics.
AI can collect and analyze threat data from various sources to predict future attacks and improve security planning.

Application Security

This security system aims to secure software applications from being stolen or hacked. Application security can reveal weaknesses at the application level, helping to prevent attacks. AI implementation in application security can include secure coding practices, vulnerability scanning, and penetration testing.

Static application security testing (SAST).
AI can analyze code to find potential vulnerabilities before application deployment.
Runtime application self-protection (RASP).
AI-based RASP systems can monitor applications in real-time and detect suspicious behavior.

Cloud Security

Cloud security protects cloud-based assets and services by keeping data private and safe across online infrastructure, applications, and platforms. It is a shared responsibility between the organization and the service provider.

Uses of AI in cybersecurity of the cloud:

User and entity behavior analytics (UEBA).
AI can analyze user activity in the cloud to identify potential threats or compromised accounts.
Cloud workload protection platforms (CWPP).
AI can continuously monitor and secure cloud workloads from evolving threats.

Identity and Access Management (IAM)

This security system manages user access to IT resources. IAM systems ensure that only authorized users can access specific resources and that their access is logged and monitored.

Possible AI applications:

Risk-based authentication.
AI can analyze user behavior and context in order to determine the appropriate level of authentication required for access.
User behavior anomaly detection.
AI can detect unusual user login attempts that might indicate a compromised account.

Internet of Things (IoT) Security

This security system is the practice of securing devices connected to the internet, for example these are smart home hubs, wearables, and industrial control systems. IoT security is a growing concern as there are over 17 billion connected devices, and each of them is vulnerable.

Examples of AI uses for IoT:

AI can analyze data from IoT devices to identify suspicious activity that might indicate a cyberattack.
Predictive maintenance: AI can predict potential device failures and help prevent security breaches caused by vulnerabilities in IoT devices.

Benefits and Drawbacks of Using AI in Cybersecurity

There can be no doubt that AI can enhance security systems in many ways, for example:

Detecting threats.
Analyzing vast amounts of data to identify subtle pattern changes that might indicate malicious activity.
Automation.
Automating repetitive tasks such as log analysis. This will increase overall efficiency by allowing the security personnel to focus on other strategic tasks.
Faster incident response.
By automating threat detection and mitigation, AI responds faster to security incidents.
Scalability and adaptability.
AI systems can accommodate growing networks and data volumes. Additionally, AI can adapt to new threats and security landscapes, ensuring continuous protection.

However, you shouldn’t forget that AI itself is still vulnerable. Moreover, using it can introduce additional weaknesses to your system, such as:

False Positives and Negatives.
AI systems can generate false positives (flagging harmless activity as threats) and false negatives (missing actual threats).
Data bias.
AI algorithms are only as good as their trained data. Biased data can lead to corrupted AI models that miss certain threats or unfairly target specific users.
Insufficient transparency.
AI decision-making processes are quite complex and difficult to understand. Therefore, it might be challenging for us, as users, to debug errors and trust the system’s recommendations.
Security risks of AI itself.
AI systems can be vulnerable to attacks. Malicious actors could potentially exploit vulnerabilities in AI models to launch targeted attacks.
The lack of legal regulation.
The lack of clear regulations around AI use in cybersecurity can create uncertainty regarding liability in case of security breaches or misuse of AI systems.

Bottom Line: How to Implement AI in Cybersecurity?

Eventually, as the technological landscape continues to evolve, new cyber threats will appear, too. However, specialized professionals are always ready to secure your services. Contact our team for a free consultation on how to best implement AI power in your cybersecurity systems!

To learn more about the Devtorium Security Team and the multiple capabilities of AI, check out our other articles:

Prompt Engineering Basics: How to Talk to AI

Posted on April 4, 2024April 4, 2024 by mkoftielieva

Being proficient with prompt engineering basics has become an essential skill nowadays. Many of us talk to AI almost daily. Sometimes, it’s even without our knowledge as the number of voice chatbots increases. However, today, we’ll talk specifically about how to talk to generative AI.

Generative AI prompt engineering can be a bit tricky because you aren’t just ‘venting’ to a machine or going through some customer service routines. The goal here is to word your command in such a way that you get the most accurate result. You can use the knowledge of AI prompt engineering to complete a great variety of tasks, from generating an image to developing and programming an AI voice bot.

Moreover, all of these tasks are becoming more relevant with every passing day. According to the CompTIA IT Industry Outlook 2024 report, 22% of companies insist on AI integration in the workflow. The percentage of using AI in daily work by usual employees is even higher. However, only a few know how to interact with AI most efficiently.

Our highly qualified specialists maintain that prompt engineering is the main thing that most GenAI users need to improve. So, with their help, you’ll be able to learn the basics of prompt engineering.

Prompt Engineering Basics: What Is Prompt Engineering?

Prompt engineering is creating inputs as specific instructions for large language models (LLMs, more on that here).

Generative AI models generate specific outputs based on the quality of provided inputs. We call these inputs prompts, and the practice of writing them is called prompt engineering.

Prompt engineering helps LLMs better process the incoming tasks to produce desired outputs.

How knowing AI prompt engineering basics benefits you.

Where You Can Apply Prompt Engineering Basics

AI software development

Prompt engineering now plays an active role in software development. You can save a great deal of time time by giving the AI model a clear prompt describing the desired functionality. It suggests code snippets or even complete entire functions. That is very helpful, especially for repetitive tasks. Trained on developer prompts, AI can also analyze existing code and identify potential bugs. If you want to read the opinion of Devtorium`s developers on AI code generation tools, check out this post.

Chatbot development

Prompt engineering allows chatbots to respond more naturally and informatively. You provide them with clear instructions and context for understanding customer inquiries. A better understanding of customer questions leads to improved chatbot responses, which means happier customers and shorter wait times. If you want to create your chatbot, read our blog about Assistant API.

Cybersecurity services

Cybersecurity is another field where understanding prompt engineering basics can help you. Security analysts can leverage prompt engineering to guide AI systems in analyzing network activity and logs. AI can efficiently scan vast amounts of data and flag potential security threats when given prompts with specific indicators of compromise (IOCs) or suspicious behavior patterns. Prompt engineering in cybersecurity empowers security professionals by harnessing AI’s analytical power to identify threats, uncover vulnerabilities, and respond to incidents.

Creative content generation

Prompt engineering allows writers to enhance their efficiency. You can give an AI model a starting point and direction for generating creative text formats like blogs, posts, scripts, or even musical pieces. This frees up the writer to focus on refining and polishing the ideas. The same goes for any kind of content, be it visuals, text, or even music.

Essential Tips on Prompt Engineering Basics

Prompt engineers do not only design and develop prompts. They also operate a wide range of skills and techniques that improve the interaction and development of LLMs. Their work encompasses the following:

Zero-shot prompting – instructing LLM without relying on any examples.
Few-shot prompting – giving the model a few examples before instructing.
Chain-of-thought prompting (CoT) – asking the model to explain its steps every time it performs the instruction.

Here are a few tips that will help you communicate with an AI as a prompt engineer on the basic level:

Use clear instructions and ask direct questions. Make the sentences as concise as possible.
Provide LLM with context. Use any relevant data for it.
Give examples in prompts.
Specify the desired output format and length.
Align prompt instructions with the task’s end goal.
Provide the desired output with styles such as bullet points, tables, numbered lists, inline/block code, quotes, hyperlinks, etc.
Let the LLM answer “I don`t know” if needed.
Break the complex tasks into subtasks.
Use a clear separator like “###” to split the instruction and context.
Experiment a lot to see what prompts work best.

Bottom Line: Are Prompt Engineering Basics Enough to Talk to an AI?

So, to sum up, everyone who uses GenAI can learn the easiest prompts to get desired but simple outputs. However, to get more complex results, you will need to have a really good understanding of programming and mathematics. Therefore, if you need to use AI in your project as more than a simple user, contact our team for a free consultation on how to best implement its power for you!

AI Chatbot Development: How Does a Voice Bot Work?

Posted on March 25, 2024March 25, 2024 by mkoftielieva

AI chatbot development is in high demand. Only about 20% of businesses use one, but over 60% think about adopting this technology. At Devtorium, we often work with chatbots because they are of interest to our clients. Today, we’d like to share one of our current cases, where we created an AI chatbot for a client that runs an innovative marketing solution.

The product is a CMS that gathers data through quizzes. The AI chatbot capabilities are required to collect and verify the collected data. In addition, we are expanding the product with an AI-powered voice chatbot that can schedule calls and generally substitute call center services.

Devtorium has a dedicated Research and Development Team that works with different types of AI services. In this case, our developers did all the work, creating the CMS, integrating a messenger chatbot, and developing a voice bot for calls.

How we used AI chatbot development for a case of a marketing CMS.

AI Chatbot Development for a Marketing Solution: Base Product Outline

In this project, the product is a quiz-based CMS that creates a variety of questionnaires based on user-set parameters. The quizzes are flexible so every user can custom-tailor the questionnaire for their business. The CMS makes collecting, processing, and visualizing data easy to help users understand their target audience.

The project aims to create an effective solution that will help businesses generate qualified leads. This product’s text chatbot collects data. The next phase, the voice bot, can schedule calls, initiate APIs, and send messages. Most importantly, the AI can use advanced ML models to understand what the customer says and proceed according to the information received in real-time.

During this project, our software development team used a variety of technologies:

Back-end:

Node.js

Front-end:

React

Databases:

MongoDB
PostgreSQL

DevOps:

AWS
Jenkins
GitHub

AI (phone service):

Vonage

ML models:

OpenAI GPT-3.5(GPT-4)
OpenAI speech-to-text (Whisper)
OpenAI text-to-speech

It’s essential to note that Devtorium always uses a combination of technologies and frameworks to achieve the best results. We discuss the client’s ideas and goals in depth to build a product that can achieve them while staying within budget.

Voice AI chatbot development; step-by-step AI tools involved.

Voice AI Chatbot Development Services Overview

In this article, we wanted to focus specifically on the voice bot designed for this project. Below, we will detail exactly how such a solution works and what it can achieve within the current level of technology. However, we’d also like to remind you that this tech is evolving fast. Almost anyone today can build an AI chatbot using LangChain or similar frameworks. You don’t need a coding degree for that. In fact, some platforms are so user-friendly that they can help you build a basic bot with limited capabilities with no tech knowledge.

However, if your business wants to invest in a solution that will give you a competitive advantage, you’ll need a professional-grade tool. The Devtorium’s lead AI specialist, Oleksii Makarov, outlines how to create an AI chatbot that can talk to your clients.

Voice AI chatbot development starts with VoIP

First of all, when creating a voice bot, we need to use a phone service. VoIP technology is quite advanced today, so this won’t be an issue. We chose to use Vonage because it is currently the best option regarding both quality and service versatility.

Speech-to-text: an essential part of AI chatbot development services

Speech-to-text technology is crucial for building an AI voice bot because it enables the machine to process audible information. We use Assebly.ai in our projects because it currently delivers the highest level of accuracy. Most importantly, it’s able to process information effectively in real-time. Therefore, it helps create an illusion that the user is talking to a person instead of a machine.

While working on this project, we noticed that lag is the biggest issue with these chatbots. Simply put, processing data takes time, so the pauses in their responses are too long. Assembly’s processing capabilities help us reduce this time to manageable levels.

Machine Learning models do the powerlifting in data processing

Devtorium uses the GPT engine versions 3.5 and 4 to build the most efficient chatbots for every application. This technology is the leading AI power behind any voice bot because the solution uses it to process data. Basically, it’s your bot’s ‘thinking power’.

We actively use advanced prompt engineering techniques while designing instructions for the bot’s conversations. The main task is creating instructions that enable the bot to learn and grow. The critical task is to make the conversation sound as natural as possible to a human.

Going back through text-to-speech

Now that the data has been processed and the machine has created the response, we use the GPT-provided text-to-speech tool. It works pretty well for the current technology level. However, we are excited because there are some announcements for more advanced features. In addition, we expect to see more voices and ‘emotions’ options quite soon.

It’s great to see this technology developing and including the emotional aspect of conversations. This truly bridges the gap between machines and people. Most importantly, we are sure this will boost the bot’s ability to deliver higher-quality customer services fast.

Back to the phone service

AI chatbot development is a complex process that includes many steps. However, at the final stage, it returns to where it started. In our case, the Vonage phone service is where the bot talks to the customer.

Voice AI Chatbot Development Benefits

The extraordinary thing about using voice bots is that they do not only cut down the cost of outsourcing call center services. Even with the current technology level, we can create a bot that extracts data from spoken conversations in JSON format. In addition, it’s able to send out a call to third-party APIs.

In simple terms, the bot can trigger an application to run in response to your customer’s query. It will also automatically process all data from the conversation and show it to you in the way you choose. This offers limitless opportunities for studying your customers’ preferences, reactions, and interests. Therefore, a voice bot can become your single most valuable tool for interacting with and researching your target audience. It can also initiate various programs or connect the client to a human operator if the machine cannot process the query.

If you want to see how it could work in practice, set up a free consultation with the Devtorium AI team!

Devtorium R&D Department: Meet the People

Posted on January 25, 2024January 25, 2024 by mkoftielieva

As you already know, Devtorium has a Research and Development Department that helps our team understand and master cutting-edge technologies. Currently, they are focusing on AI and Machine Learning and have workshops that anyone willing to learn can join.

Today, we’d like to tell you more about the Head of R&D, Oleksii Makarov, and the amazing team the Department has already assembled.

Oleksii Makarov: Head of Research and Development Department in Devtorium.

Oleksii Makarov: Solution Architect/Head of Research and Development

Oleksii is Devtorium’s leading expert in AI and ML technology, who loves to share his knowledge as much as he enjoys learning new technologies. He has 24 years of experience in development, and over this time, Oleksii has worked with multiple projects and technologies, including:

Developing desktop applications for Windows (C++)
Web development (.NET)
Front-end development (HTML, JavaScript, React, NodeJS)
Deep Learning and AI (Python)

For the last seven years, Oleksii has been dealing with Machine Learning and Deep Learning technologies. He has experience developing several AI-related projects where he was actively working on the following:

Computer Vision: object detection, video processing, data filtering, behavior tracking.
LLM + prompt engineering + GPT Engine

Oleksii says that Deep Learning is one of his main interests in technology right now. It enables us to teach computers to perceive the world similarly to humans. This is one of the things involved in the work of the Devtortium R&D Department, and studying this technology offers excellent opportunities for personal growth. In addition, this looks great on your CV😉

Research & Development Team: Not Only for Developers

We’d also like to introduce some of the R&D Team members who have been studying new technologies with Oleksii for the last few months. We were surprised to learn that not all members of the Department are developers. Some Devtorium team members are very active and forward-thinking. Therefore, they want to discover everything they can about cutting-edge tech to stay sharp and expand their professional skills and opportunities.

Olha Romanets: Senior Front-End Engineer

Olha is one of the most active members of the R&D Department. Her drive is contagious, and her interest in the topic has no bounds. Olha has a successful career in front-end development, so we wondered what pushed her to take on this new challenge.

She said joining the R&D Team was essentially an ‘impulse purchase’. For some time, the topic of AI was popping up all around her, so when the opportunity to study it deeper presented itself, Olha took it immediately.

She is a highly dedicated person who enjoys studying things to their full potential. This is exactly how she approaches R&D workshops and never fails to impress her teammates.

Oleksandr Shapran: Strong Junior Business Analyst

As mentioned before, not all members of the Research and Development study group are developers. One of our business analysts, Oleksandr, is a good example of a person who doesn’t allow any perceived limitations to prevent them from reaching their full potential.

We asked him why he decided to join and spent some of his time studying something far removed from his primary specialty. His answers were truly inspiring.

First of all, Oleksandr highlights the importance of knowledge about AI for any professional working within the IT industry. This is the most cutting-edge technology that is already revolutionizing the world. Therefore, everyone must understand at least the basics of how it works and what it’s capable of.

Moreover, as Oleksandr wisely notes, the level of professionalism one has is determined by the depth and versatility of their knowledge. Simply put, any professional can bring more value if they are knowledgeable in many relevant areas. This way, they can be most helpful to the team, and that’s precisely how he wants to be.

That said, studying AI on your own is extremely difficult, especially for someone without a technical background. Joining the Devtorium Research and Development Department allowed him to learn from a great mentor and get support from his colleagues. They face challenges and grow together, each contributing something valuable to the work of this Department.

Also, Oleksandr notes that the knowledge and skills gained while working with the R&D Team help predict future trends. By learning these complex topics as an analyst, he can better understand changes in technology trends. Therefore, he can see what to pay attention to and how to benefit his current and future projects.

Ivan Danyliuk: Senior front-end endgineer

Ivan Danyliuk: Senior Front-End Engineer

Ivan is one of Devtorium’s leading front-end developers. He has shown tremendous talent and interest in working with AI through our Research and Development Department workshops.

According to Ivan himself, joining the R&D Team aligns with his passion to stay up-to-date with any changes in the industry. Due to his participation in this Department, Ivan broadened his horizons significantly. His direct quote:

Being a part of the R&D team has shown me new perspectives, encouraging me to approach everyday matters from a fresh and innovative standpoint.

Excited to be on this journey and eager to explore the limitless possibilities that our AI endeavors bring, I am confident that our collective efforts will shape the potential of our company.

Serhii Bevz: Senior Full-Stack Engineer

Serhii Bevz joined the Devtorium team not long ago but has proven to be an exceptionally talented full-stack engineer. However, despite being busy with his project, he makes time to actively participate in the work of the Research and Development Department. When we asked why he chose to join this team, he said that, first of all, it’s a highly promising direction for professional development. His second reason is that it’s a fascinating field for developers. Studying and working with AI enables you to move up to a completely different level of skill and knowledge.

In addition, Serhii mentions how he loved math in school and how working with AI requires a deep knowledge of mathematics. Moreover, studying a new programming language is much easier when you have real-life tasks for practice. Therefore, he enjoys mastering Python with the R&D Team and is progressing quickly in this area.

Serhii notes that expanding your personal tech stack is an outstanding professional opportunity. Also, he very much enjoys the way Oleksii teaches these complex subjects. The subject matter is very complicated, so it’s crucial to have someone who can explain it in a way that’s easy to understand. As a full-stack developer, Serhii knows he must know AI and how it works. This knowledge is vital for any ambitious developer today.

The only thing that Serhii does complain about is that he wishes to have more time for this exciting project. However, he hopes that working with the Research and Development Department on these topics will allow him to be near the start of changes in tech trends. Similar to how it was with NodeJS, AI has launched a revolution now, and those who are proficient with technology have a much better chance of professional success. Serhii also notes how important it is for a development company to have a team of professionals with these trending skills, as Devtorium has.

Oleksandr Kostylenko: Strong Middle .NET Engineer

Oleksandr is a very experienced and talented developer in his professional life and a highly artistic person outside of work. When we talked to him about his reasons for joining the Research and Development Team, he showed that both parts of his character played into it.

We can already feel the presence of AI everywhere, and personally, I use ChatGPT and DALL-E in my daily life. The future of the IT industry is inseparable from AI technology, and I’m sure studying it will help me in my work. Moreover, this outstandingly fascinating technology allows you to realize your artistic potential in development, which is crucial for me.

Oleksandr states that he enjoys being a part of the R&D Team and how Oleksii teaches such a complex subject. He especially commends the fact that Oleksii, as a teacher, encourages the team to write their own code and experiment. It’s a very productive and efficient method of teaching such complicated topics.

According to Oleksandr, knowledge is the main benefit he gets from joining Research and Development. Despite being a team member for only a few months, he already understands the core principles of Deep Learning and looks for ways to apply this in practice. He is looking forward to learning more about AI and reaching new heights with the help of this technology.

Devtorium Research and Development Department: Progress Report 2023

Despite being so new, the Devtorium R&D Department has grown fast and attracted many talented people who wish to expand their knowledge of innovative technologies. According to Oleksii, who heads the Department, the main achievements so far were:

Building a core team of people who are interested, driven, and have an aptitude for this type of work.
Covering the basics of Machine Learning, Deep Learning, Python, and Algebra needed for understanding and further work with AI technology.
Bringing the team to approximately the same level of knowledge.
Starting to work on simple model training tasks.

Currently, the Research and Development Team will continue to expand their knowledge and learn more about AI programming. We hope the team will soon have new exciting projects to work on.