Volodymyr Marynenko, Author at Devtorium

Why Your Media Business Needs an Enhanced Ad Tech Platform

Posted on March 12, 2025March 17, 2025 by Volodymyr Marynenko

Advertising technology is one of the core parts of the modern media business. However, many media businesses overlook the importance of high-quality ad tech. This happens for various reasons, from cost-cutting measures to a lack of technical expertise.

Unfortunately, a flawed ad tech platform can damage the media business more than its absence. Yet, you reverse the situation when using an enhanced advertising technology platform.

Today, digital ads are in high demand among both businesses and customers as the number of online users grows. In 2024, Netflix announced its ad tech platform, targeting a worldwide launch by late 2025. This move will allow Netflix to manage its ad operations, optimize targeting, and increase engagement.

Devtorium specializes in developing advanced ad tech solutions tailored for media entrepreneurs. In this blog, you’ll discover current ad tech limitations, advice on enhancing the platform, and a real success case.

What are the Primary Limitations of Current Ad Tech Platforms?

Digital transformation has significantly reshaped the media industry, including how businesses approach advertising.

If you own an online media platform, you should have heard of demand-side platforms (DSPs), data management platforms (DMPs), and supply-side platforms (SSPs). These three platforms are key components of advertising technology. DSPs handle ad buying, SSPs manage ad selling, and DMPs operate data.

Media businesses often have trouble estimating these components equally. Such an issue usually results in poorly optimized platforms and causes monetization cuts.

Disconnected blocks representing DSP, SSP, and DMP, illustrating poor integration in outdated ad tech platforms.

The main challenges for ad tech platforms include:

Inability to manage advertising inventory streams according to target groups, engagement metrics, content context, and advertiser requirements
Lack of real-time data processing and automation, causing delays in decision-making
Poor cross-platform data integration, creating siloed information
Weak audience segmentation tools, decreasing targeting accuracy and ROI
Inefficient bid optimization, affecting yield performance
Overdependence on third-party ad networks, limiting direct monetization opportunities
Inefficient ad targeting and delivery lead to wasted impressions and lower engagement rates
Outdated attribution models, failing to capture the complete customer journey
Lack of predictive analytics capabilities
Poor integration between demand and supply platforms, creating technical bottlenecks

How Can You Enhance the Ad Tech Platform For Revenue?

Enhanced ad tech platforms improve monetization by implementing advanced technologies. However, this is easier said than done. These techs mean utilizing AI, predictive analytics, and cross-platform solutions.

AI and Machine Learning

Today, AI and machine learning are becoming essential for empowering ad tech. They can predict user preferences and deliver personalized ad placement based on contextual signals. While AI constantly tests and learns, it optimizes ad performance by adjusting bids, creatives, and targeting.

In the coming years, contextual and AI-driven advertising will replace cookie-based tracking. Privacy regulations restrict traditional monitoring. AI technologies that understand content will enable relevant targeting without personal identifiers.

An AI-powered system analyzing user data to optimize ad targeting across multiple platforms.

Advanced Metrics and Predictive Analytics

Media businesses should track many measurements to guarantee the efficiency of published ads. Advanced metrics include:

customer lifetime value (CLV)
attribution across multiple touchpoints
view-through conversions
engagement time
content affinity patterns
incremental lift metrics
ad fatigue indicators
conversion rates

Your ad tech platform should have at least half of them to be considered advanced. Moreover, combining these metrics with AI analysis can provide you with predictive analytics.

Predictive analytics plays a crucial role in advertising strategy. It forecasts customer behavior and campaign performance. Additionally, it is beneficial for optimizing budget allocation across channels and predicting customer churn.

Cross-Platform and Multi-Channel Integration

Integrating cross-platform practices is essential for achieving a balanced and comprehensive ad tech management solution. Instead of disjointed ads, businesses can achieve consistent audience engagement. Unification happens through centralized management interfaces, synchronized audience definitions, and API connections.

Multi-platform advertising deploys campaigns simultaneously across diverse channels, including websites, social media, apps, and TV. This approach maximizes reach by leveraging each channel’s unique strengths and audience coverage.

SAFe Framework Integration

An improved advertising platform powered by the SAFe framework has a huge advantage for the media business. SAFe allows large companies to coordinate work effectively between numerous teams working on joint projects. This approach facilitates better communication and cooperation between different groups and provides an opportunity to gain invaluable experience working with complex business processes.

As each participant understands their responsibilities and interdependencies, companies can respond quickly to changes and achieve higher productivity. SAFe also promotes discipline and organization, making processes more predictable and controllable.

Bonus: All-in-One Solution

It may sound unlikely, but it’s true—an ad tech platform that integrates DSP, SSP, and DMP functions eliminates the abovementioned challenges. If you are excited, read the next paragraph or discover our case study on this topic.

Media Companies’ Success with Advanced Ad Tech

The right ad tech platform isn’t just theory—it delivers measurable results. At Devtorium, we develop cutting-edge software solutions that align with your business strategy. Our global clients, from startups to enterprise leaders, have leveraged our expertise to drive profit growth. But don’t just take our word for it—here are real case studies to prove it.

TV Making Software

The client’s main product is a really complex TV Making Platform that helps businesses to generate ideas for quality video…

Why an Innovative Ad Tech Matters for Media Business

As you can see, an advanced ad tech platform is necessary to gain a competitive advantage among media providers today. Also, without it, inefficiencies in targeting will continue to limit revenue potential. Programmatic solutions and AI-driven analytics can quickly boost any legacy ad tech platform, so your media business will be among the top.

Transitioning from a flawed platform may seem challenging, but the long-term benefits outweigh the costs. Choosing the right ad tech provider requires careful evaluation of many factors: budget, reputation, experience, offered functionality, and more. Devtorium can meet all these needs. Contact us for expert guidance if you’re ready to enhance your platform!

What Are the Must-Have Features of an Insurance Tech Company?

Posted on February 27, 2025February 27, 2025 by Volodymyr Marynenko

Any insurance company must innovate and rely on an online presence to maximize its efficacy. Without digitalizing services, businesses will struggle to attract customers: every year, the number of people who prefer online shopping to offline increases significantly.

According to Eurostat, insurance policies are at the top of online purchases by Internet users. Additionally, as per Statista, the U.S. insurtech market is expected to continue growing until 2026, with the market size predicted to reach 261.6 billion U.S. dollars.

These statistics indicate that insurtech is needed today. People are excited to buy insurance services online. Therefore, to define your insurance company as modern and profitable, you must provide customers with technologies: an e-platform, a mobile app, a chatbot, and a cloud database.

So, how can you make your insurance company more modern and tech? In this blog, our Head of Business Operations, Nataliia Shapran, will explore the challenges of digital transformation in insurance, highlight successful insurtech startups, and outline key features of a competitive insurance platform.

Value of insurance distribution technology market in the United States from 2017 to 2021 with forecasts from 2022 to 2026

What Are the Challenges of Boosting Insurtech?

Applying our experience in insurtech, we have listed the top challenges while innovating the insurance business. Moreover, our developers have proposed solutions to address each of these challenges.

Complex Insurance Buying: The complicated process for insurance buyers requires extensive steps to find and secure a policy.

Solution: The insurance system must automate the entire process to ensure that customers can purchase the policy with just a few clicks and receive the documents via email.

Data Deficiency in Personalized Quotes: The inability to create personalized proposals due to the absence of data about the insurees.

Solution: Establish connections to various databases to assess the insuree in real-time, calculate premiums, and create a personalized proposal immediately. End-users only need to enter minimal information on the platform, while the system will pull additional data from specialized databases using third-party APIs.

Embedding Insurance Products into Workflow: This issue often requires long development cycles and specialized technical expertise. A good example is adding the ability to get travel insurance during the final step of booking a flight or offering rental coverage during car rental checkouts. These processes are typically embedded within the primary business flow.

Solution: the addressing of this challenge is possible by delivering the ability to configure customizable embedded insurance widgets as part of insurance product configuration. This approach enables digital quote-to-bind capabilities across any channel. Moreover, businesses can now offer a seamless customer experience, making integration significantly easier.

Subscription Management Hurdles: Preventing clients face unexpected costs or gaps in coverage while managing monthly subscriptions.

Solution: Delivering an efficient monthly insurance plan subscription system ensures no client overpays for their policy.

Risk Assessment: Ensuring accurate risk assessment across diverse regions while maintaining data integrity and confidentiality

Solution: Integrating a postal code database enables location-based risk scoring, while regional settings ensure personalized insurance options for global clients.
You should contact us if you have more specific questions about project ideas. Additionally, you can read more about insurance and other projects in our case study.

Which Insurtech Startups are Successful?

One of the best ways to strengthen your insurtech company is to analyze the groundbreaking insurtech startups that have achieved in providing advanced technologies and learn from their experience. Here are four standout companies leading the way:

Next Insurance: Launched in 2016, Next Insurance caters to small businesses by offering tailored insurance policies across various industries. Next Insurance harnesses ML and big data to provide ultra-personalized policies. Moreover, using AI, it processes applications in just 10 minutes, significantly reducing the time businesses take to obtain coverage.
Oscar Health: Founded in 2012, Oscar Health offers health insurance plans for individuals, families, and employers. The company’s insurers can easily access plan details, in-network care, 24/7 virtual consultations, messaging support, and bill payment features. Oscar Health utilizes technology to offer a seamless digital experience, including telemedicine services and AI-driven care navigation.
Lemonade: Launched in 2015, Lemonade offers renters’ insurance, homeowners’ insurance, car insurance, pet insurance, and term life insurance through a user-friendly digital platform. A key feature of this company is a unique policy management experience, which includes AI-powered quoting and claims tools that service directly in the customer’s mobile app.
Root Insurance: Established in 2015, Root Insurance provides personalized car insurance rates based on individual driving behavior. By leveraging telematics and data from the smartphone’s sensors, Root offers a car insurance quote based on how you drive.

How to Enhance Your Insurtech Company?

As noted above, every profitable insurtech business has integration with cutting-edge technology, mostly even several. Therefore, we prepared a list of must-have technological features for insurtech:

1. Digital Platform

Primarily, in insurtech, your company must own an e-platform that provides services and policies on a user-friendly interface. The benefits of creating an insurance platform are countless: increase in customer outreach, automation of service signup, optimization of insurance agents’ performance, analytics and personalization of quotes, a convenient way for the insuree to manage their portfolios from the devices, purchase policies, monitor policy performance, and more.

2. Mobile and Web App

The next step is to develop mobile and web applications. These applications enable insurers to easily manage their claims, access insurance information, update policies, and receive real-time assistance from any device anywhere. By integrating real-time data tracking tools, these apps can also offer dynamic pricing models and proactive risk prevention tips based on user behavior.

3. Cloud Technologies

In addition to developing software solutions for your insurance business, it is essential to consider cloud technologies. Mainly because they enable secure decentralized data storage and high-speed processing while ensuring scalability and cost efficiency. Also, you can integrate parametric insurance models and facilitate seamless API connections with third-party data sources using cloud services.

4. Data analytics and machine learning

Data analysis is an integral part of insurance practices. However, processing the vast amounts of insurance information demands much effort. Fortunately, we live in a time when machine learning and AI can perform analytics for you. Whether you want to automate repetitive tasks, predict behavior and risks, or optimize claims processing and underwriting, machine learning algorithms can do that for you.

5. AI-powered chatbots

Suppose you want to enhance customer experience, reduce the workload of the support insurers, gather insights from insurers, and implement new technology that is fully discovered and won’t cost much. In that case, an AI-powered virtual assistant is your best choice. It can handle policy inquiries, assist with claims processing, and provide personalized recommendations by analyzing real-time policyholders’ data.

Bottom Line: Why Does an Insurance Agency Need to Utilize Advanced Technologies Today?

To remain competitive, insurance agencies must integrate advanced technologies. As the preference for digital insurance solutions grows, adopting innovations such as AI-driven platforms, cloud computing, and mobile apps is crucial. These technologies will definitely align with modern consumer expectations, driving profitability, reducing costs, and attracting customers.

If you are interested in this topic, check out more of our articles about product development click here.

Set up a free consultation to see how you can digitally transform your business! Our experts will conduct a detailed analysis of your business and ideas, then provide a proposal on how to achieve the best results with your project.

AI in Business Intelligence: Reveal the Best Practices of Data-Driven Decisions

Posted on January 14, 2025January 14, 2025 by Volodymyr Marynenko

In today’s fast-paced world, companies receive vast amounts of data every minute: clicks, views, transactions, interactions, and more. However, most businesses lack the resources or time to process all this information manually. That’s where Artificial Intelligence (AI) comes in.

According to Exploding Topics, 48% of businesses already use some form of AI to manage big volumes of data effectively. Moreover, when combined with Business Intelligence (BI), AI fundamentally enhances companies’ data analysis and decision-making processes.

At Devtorium, we have outstanding Data Science and Business Analytics experts who specialize in enhancing companies’ data processing. With extensive experience in providing AI-driven BI tools, our leading specialist, Olena Medvedieva – Head of the Data Science Department, has prepared a blog on the efficient application of AI in BI.

AI Modernizing BI Tools

At its core, Business Intelligence is about transforming raw data into valuable insights that help businesses make better decisions. BI tools help companies track performance, monitor key metrics, and analyze past data to understand trends. But while BI has been great at showing what happened, AI is now stepping in to show why and what might happen next. So, how exactly does AI fit into the BI picture?

1. Smarter Data Analysis

Traditional BI tools focus on structured data, such as sales numbers or customer demographics. However, AI can analyze all types of data, including unstructured ones like emails, social media posts, or audio recordings. By examining a fuller picture of data, businesses can uncover hidden insights and make more informed decisions.

2. Predicting the Future

AI isn’t just good at analyzing what happened; it’s great at predicting what will happen next. Using machine learning, AI can examine past data to forecast trends, customer behavior, and potential risks. For example, a retailer could predict which products will be in high demand next month, helping avoid stock shortages or overstocking – ultimately saving time and money.

3. Natural Language Processing (NLP)

One of the most impressive aspects of AI in BI is its ability to interact with data in a more natural way. With Natural Language Processing (NLP), you can ask your BI system questions in plain language, just like you would ask a colleague. For example, you could ask, “What were the top-selling products last week?” and get a quick, clear response—no need to be an expert in data or learn complicated commands.

4. Automating Insights

AI can also identify and highlight essential insights. By continuously scanning data for trends or anomalies, it can alert you to unusual occurrences, such as a sudden drop in sales or a spike in customer complaints. These insights help businesses stay on top of critical real-time changes and react faster.

5. Improved Data Visualization

By recommending the best way to visualize data, AI makes it easier for businesses to see trends and patterns. Whether through bar charts, line graphs, or heat maps, AI ensures that presented data is in the most insightful and accessible way possible.

The Strategic Advantages of AI in BI

Adding AI into the BI area drives businesses to an entirely new level. By doing so, companies benefit from various fields, empowering them to stay ahead in today’s data-reliant market. Here’s how:

Faster, Smarter Decisions by processing enormous amounts of data quicker and more accurately than doing that manually.
Cost Savings by automating tasks that traditionally require manual effort, such as data analysis and report generation. AI can free up employees to focus on more strategic tasks, ultimately saving money and boosting efficiency.
Scalability by scaling solutions as the business grows. It helps companies handle more extensive datasets and complex analyses, ensuring that data-driven decision-making remains possible even as the business expands.

Challenges to Consider

Of course, integrating AI with BI isn’t all smooth sailing. There are a few challenges businesses need to consider:

Data Quality: AI is only as good as its fed data. If the data is inaccurate, incomplete, or biased, the insights AI provides may not be reliable. Businesses need to ensure that their data is clean and high-quality.
Cost and Expertise: While AI-powered BI tools are becoming more accessible, they still require a significant investment. Not to mention, finding professionals who can integrate and manage these tools requires specialized skills.
Ethics and Privacy: As with any technology, AI raises ethical concerns, especially regarding data privacy. Businesses must be transparent about collecting, storing, and using data to avoid privacy violations or bias in their AI models.

Bottom line: Why AI is the Perfect Fit for Business Intelligence?

Overthinking the future of AI in BI, it is predictable how manageable and robust working with data will become. Moreover, their combination will help businesses quickly uncover trends, predict future outcomes, and automate repetitive tasks, freeing time for more strategic thinking. With user-friendly AI tools, even those without technical backgrounds can gain valuable insights from data.

To see how AI and BI tools transform your business in practice, book a free consultation with the Devtorium Data Science team!

Devtorium Software Development Company: Progress Report 2024

Posted on January 6, 2025January 6, 2025 by Volodymyr Marynenko

Over the past twelve months, due to the struggle of the global IT market, Devtorium has faced many tough challenges and had to make hard decisions. However, we neither gave up nor scaled back our ambitions. On the contrary, our company has demonstrated a better dynamic compared to the previous year. None of our achievements would have been possible without our invaluable employees, whose dedication and hard work made everything real. At Devtorium, we believe that encouraging our teammates’ initiatives, ideas, and determination drives our company to success.

Despite this year’s economic volatility, we were confident that our reliable employees would show unwavering commitment to our shared goals, and we were right. So, in this blog, we want to share the results from our 2024 company performance report.

What We Achieved as a Software Development Company in 2024

In 2024, Devtorium’s teams engaged in 6 new projects. The tech stack and industries of the projects varied significantly. The key project topics of this year were AI and web development.
Information security was one of our company’s primary growth areas the previous year. We are proud that our Information Security Team made a giant leap – we became an official PECB Partner. Additionally, Devtorium’s Deputy General Manager – Natali Kashuba, who has 20+ years of experience in security, is a Certified PECB Trainer and ISO 27001 auditor. Under her leadership, our company has received and successfully maintained the ISO/IEC2007:2013 certificate in the ISMS field.
Last year, Devtorium joined the IT Ukraine Association (ITU). We are thrilled to collaborate with such exceptional professionals, look forward to strengthening this partnership, and contributing to the recognition of Ukrainian developers worldwide.
Devtorium’s Head of Business Operations, Nataliia Shapran, attended a techUK conference in London. It was a fantastic event with excellent networking opportunities. We were delighted to make many new contacts and meet extraordinary people, such as Ukraine Britain Business Council members and representatives from various UK businesses. Our company is deeply grateful to everyone who drives the global tech industry forward and provides Ukrainian companies with the opportunity to demonstrate their growing potential.
In 2024, Devtorium was proud to share that Clutch had recognized our company in two categories: Clutch Global & Clutch Champion. The first award highlights our recognition as a global custom software development leader, while the second reflects our commitment to delivering innovative solutions for clients worldwide. Additionally, Devtorium was named one of the top AI companies by The Manifest based on reviews. We want to express our gratitude to our clients worldwide. Thank you for your honest reviews and appreciation of our work! To see more of Devtorium’s achievements and read verified reviews from our clients, visit our Clutch profile.
At Devtorium, we actively support our employees’ initiatives, particularly those focused on education. Last year wasn’t an exception. We want to mention the top three workshop series dedicated to enhancing our teammate’s knowledge and enabling them to provide tech services of any complexity. Our Senior Full Stack Engineers, Serhii Kovalskyi and Serhii Datsii, made an impactful series of backend workshops designed for Front-Enders eager to expand their knowledge and skills in Backend development. Also, Olena Medvedeva, head of the Data Science department, conducted lessons with practical tasks focused on statistics and probability theory. Finally, Oleksii Makarov, head of the R&D department, concluded a series of classes on computer vision and intricate workings of neural networks. His workshops explored advanced topics such as self-attention mechanisms, multi-head attention, and transformer models.

Devtorium: Plans for the Future

At Devtorium, we remain committed to growth, innovation, and delivering excellence in every project we undertake. Our achievements in 2024 reflect our teams’ dedication and our clients’ trust. Moving forward, we aim to deepen our expertise in trending technologies like AI and Information Security.

Supporting the professional development of our employees will continue to be a key priority, ensuring we maintain a competitive edge in the global tech market. Additionally, we plan to strengthen our partnerships and explore new opportunities across a diverse range of industries. Above all, we remain passionate about innovative technologies and providing seamless customer experience worldwide.

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 Volodymyr Marynenko

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.

How to Improve Client’s Attitude towards You: Tips for Making Them Your Fan

Posted on August 28, 2024November 25, 2024 by Volodymyr Marynenko

Have you ever wondered why some professionals seem to effortlessly win over clients? The way you achieve it is simpler than you think. With the right strategies, you can turn the tide and make a discerning client your biggest supporter. That is why Olha Turok of the Lviv Office gave a Tech Talk about building a positive and productive relationship with your clients.

Olha is a Front-End Engineer with 7+ years of experience. She has a strong background in communicating with clients. This blog will explore actionable tips that Olha shared to make a client a true advocate for your brand.

Building Trust Through Workflow

Quality comes first

Nothing is more convincing in you being a fantastic worker than doing your job well. Your skills and results must meet the client’s expectations. Poor performance will lead to problems that will destroy even the best relationships. Always remember that your work must be reliable, clear, and well-structured.

Predicting possible “surprises”

If you feel that something in the project might go wrong, it’s important to let the client know in advance. Even if you’re not sure, timely warning of possible difficulties allows you to take steps to resolve them and prevent complications. Following this tip will create an atmosphere of trust and demonstrate your responsibility, helping to avoid adversity.

Protecting your own opinion

In client relationships, you should stand by your professional expertise, even when it is challenging. When you protect your opinion, it does not mean that you are stubborn. On the contrary, you ensure the client receives the best possible guidance. If the client makes incorrect assumptions, you must explain the situation clearly, using examples and arguments. Always communicate your perspective distinctly and confidently, backing it up with evidence or experience.

Ensuring Success in Task Management and Estimation

Clear requirements

If you want to show your reliability, it is important to know what the client expects from your work. You should never accept a task that is not described: without clear requirements, it is impossible to succeed. When the client’s representatives cannot provide details, you can take the initiative: ask for additional time to describe your vision of the task. You will avoid misunderstandings and save time spent on unnecessary work.

Requirements are in question

The best practice for providing significant changes in requirements during a task is to document them in a separate task. You can add a new task to an existing one, as it allows you to take into account new details without confusion. Or you can stop the current work and start from scratch. Following this advice will ensure clarity in execution and help you complete the task on time.

Write down what is important

During calls with clients, you would instead summarize the conversation to make sure everyone understands each other. After the call, you should write summaries or comments on the task in the chat. If you always do that, you will avoid forgetting details and ensure that all changes and agreements are saved for later review.

Fulfill your estimates

An estimate is a promise to complete a job within a certain period. For the client, the main thing is that there are no delays. It is better to promise 70% and deliver 80% than to promise 100% and deliver “only” 90%. Force majeure is possible, but an estimate is a benchmark that the client relies on.

Mastering Client Communication

Politeness

Politeness is a cornerstone of effective client communication, especially when interacting across cultures and languages. Since most clients usually speak English, you should be able to express yourself clearly and correctly. Misusing phrases or mistranslating can unintentionally come across as rude or commanding, which can strain the relationship. Always take the time to choose your words carefully, ensuring that your tone is respectful and professional.

Turn Competition into Collaboration

When client’s employees feel competitive with you, it can lead to unwarranted comments or criticism. It is worth building time to discuss issues in the timeline of tasks to avoid unnecessary conflicts. Involve supervisors in conflict resolution, discuss clear criteria for interaction, and maintain a professional approach. The best way to avoid problems is to build good personal relationships with the client’s employees.

Think Positive

Even when things don’t go as planned, keep a positive attitude. Clients appreciate employees who can remain optimistic and find solutions in any situation. Remember that your mood can affect the whole team and the client, so try to be a source of positive energy.

Bottom Line: The Importance of Building Trust with Clients

As Olha said: “Today, building a trustworthy and robust relationship with a client is not only about comfort but also about confidence in the future of your workplace. Follow this advice, and you can improve clients’ perceptions of you, your team, or even the whole company. When clients trust you, they are more likely to return for future projects and recommend your services to others.” Our company agrees with this statement. We thank Olha for making such a remarkable Tech Talk.

Will Small Business Be Affected by the AI Bubble Burst?

Posted on August 14, 2024August 13, 2024 by Volodymyr Marynenko

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.

The Manifest Crowns Devtorium as One of the Most-Reviewed AI Companies

Posted on August 8, 2024August 8, 2024 by Volodymyr Marynenko

We are proud to share that The Manifest named Devtorium one of the most-reviewed AI companies in Lviv, Ukraine. At Devtorium, we strive to build a customer-centric business. Therefore, it’s an important milestone for us to see our efforts recognized through honest and verified reviews from our customers.

For those of you who aren’t familiar with The Manifest, they are a business blog website that aims to gather and verify the hard data, expert insights, and actionable advice that you need to build your brand and grow your business – to provide the practical business wisdom that manifests in your success.

We would like to thank the team at The Manifest and share the words they had to say about our company:

If you want your business to stay competitive in this digital age, then it is essential that you know about the latest trends, technologies, and products in the market. You’ll have to have a firm grasp and understanding of the best AI tools and solutions and how you can utilize them for your growth. Let the team at Devtorium help you as you navigate through the fast-paced AI space.

More than 200 skilled software engineers, business analysts, data scientists, project managers, and UI/UX designers make up Devtorium. Each of these experts has specialized credentials in their field and has worked on challenging projects for a range of businesses. The Devtorium team is continually expanding and mastering new technologies to expand their tech stack.

In order to keep their clients ahead of the competition, Devtorium concentrates on AI development and the application of cutting-edge technology. Most of their clientele have been with the company for five years or longer, and you can see how satisfied they are by looking at Devtorium’s reviews.

It is huge for us to be recognized as one of the top AI companies by The Manifest, but this wouldn’t be possible without our partners. Your support helps us achieve all of this. We are grateful to each and every one of you for choosing us as your development partner.

Do you have a great idea that you want to bring to life? Let’s make it happen together! At Devtorium, we’re always looking for new opportunities to collaborate and help businesses grow. Get in touch with us today and let’s start a conversation about how we can work together.