Tips & How-To's 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.

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.

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

Posted on August 28, 2024November 25, 2024 by mkoftielieva

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 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:

How to Use AI in Small Business: Ideas and Practical Applications

Posted on July 3, 2024July 3, 2024 by mkoftielieva

At this point, using AI in small business has become a mandatory requirement. There is just no other way to gain a competitive advantage. The level of competition in every industry is skyrocketing, so you must cut your costs and optimize every possible process. This is precisely what AI can do for small businesses, and we’ll tell you how today.

How many companies consider using AI in small business

Using AI in Small Business: Practical Tips from Professionals

Leveraging AI tools can be troublesome for many SMBs because they need help figuring out where to start. Devtorium offers a range of AI software development services, and our developers have expertise in implementing AI into various systems. In this post, they will demystify AI for small businesses, providing insights and examples of successful AI usage.

Bear in mind, that the majority of businesses are either already using or consider implementing AI solutions already. Take a look at stats in the graphs to see what position your company matches currently.

Let’s start with a few general tips to consider when using AI in small business:

Start small
First, you should begin with only targeted AI applications that align with your business goals and resources. For example, if you have an e-commerce site, add a chatbot to enhance customer experience.
Data quality matters
For AI to work accurately, you must be sure that training data is clean and relevant. It’s crucial for minimizing the risk of bias in AI outcomes.
Monitor your ROI
Your BA must analyze all AI projects’ return on investment (ROI) to ensure they deliver value to your business.
Collaborate with experts
Partner with AI specialists or consultants to navigate complex implementation challenges.

Real-World Implementations of AI in Small Business

Improving Customer Experience

Adding an AI chatbot to your website or service is the best AI innovation to start with. This virtual assistant can solve many tasks, from answering users’ frequently asked questions to assisting with product recommendations based on their behavior. Moreover, chatbots can learn and adapt over time. Therefore, their accuracy and efficiency in handling customer queries will improve.

Virtual assistants can cut the workload of your human support staff. In turn, they will have time to focus on more complex tasks. Besides, these AI solutions can gather valuable data on customer interactions. Use them as an analytics tool to learn about your audience and make better-targeted business decisions. For example, you can identify trends, understand customer needs, and improve your services overall.

To start using such a chatbot, small businesses can try services like Dialogflow by that uses Google Al. You can also try to make your own chatbot with LangChain. If you want to go to the next level, check out our case of what a voice bot can do.

Supply Chain Management

Some fantastic ways of using AI in small business that deals with logistics include:

Optimizing delivery routes optimization
Reducing transportation costs
Cutting down on delivery times
Increasing logistics efficiency

Also, you can use AI to evaluate supplier performance and manage relationships, ensuring the best terms and reliability. AI-driven supply chain management systems leverage advanced algorithms to analyze vast amounts of data and make real-time decisions that enhance operational efficiency.

AI can also enhance predictive maintenance in logistics. This is done by using data from IoT sensors to anticipate equipment failures and schedule proactive repairs. With this tech, you can minimize downtime and improve your business’s overall reliability.

Moreover, AI-powered demand forecasting models help anticipate customer needs more accurately. Therefore, businesses can adjust production schedules and inventory levels accordingly. By optimizing these processes, AI contributes to cost reduction and enhances the agility of supply chain operations.

Predictive Analytics

One of the best ways to use AI in small business is implementing predictive analytics to analyze your past data and identify patterns. These tools enable you to forecast sales trends, which can make a crucial difference in achieving success.

Efficient use of predictive analytics can help you come up with effective strategies and prevent rash decisions. Furthermore, predictive analytics can optimize inventory management by forecasting demand and caution about overstock or stockouts.

In addition, these AI systems can analyze sales data to identify seasonal trends and customer behavior patterns. You can utilize tools like Salesforce Einstein Analytics to anticipate market shifts.

Visual content

You should consider using AI in small business marketing, especially if you don’t have a dedicated marketing team. Creating and optimizing visual content can be easy using tools like Midjourney or Photoshop’s integrated AI, creating and optimizing visual content can be easy.

For example, some tools today can personalize marketing materials based on user data, creating tailored experiences that appeal more deeply to the target audience. In addition, using generative AI can save time, money, and effort.

Integrating AI into visual content strategies helps businesses stay ahead of the competition by consistently delivering visually compelling content. These tools can streamline the design process by providing instant enhancements and creative ideas, allowing teams to focus on more strategic tasks.

Cybersecurity

One area where small businesses aren’t using enough AI is cybersecurity. You should definitely make this your priority, as data breaches are a major threat today. Implement AI-powered cybersecurity solutions to detect and mitigate potential threats in real-time. For instance, predictive threat intelligence enables AI to analyze patterns and trends in cyber attack data, forecasting where and how future attacks might occur. Using AI for small business in this particular sphere enables them to strengthen their defenses preemptively.

On other levels, AI enhances email security by detecting phishing attempts and malware-laden messages. This will significantly reduce the risk of successful social engineering attacks. Additionally, AI assists in post-incident analysis, providing valuable insights to understand the nature and scope of breaches, and informing future prevention strategies. This cycle of learning and making changes based on new data ensures that AI-powered cybersecurity solutions remain effective against an ever-changing threat landscape.

Conclusion: how to best start using AI in small business.

Bottom Line: How to Use AI in Small Business to Get Top Value for Money

To sum it up, small businesses can and should use AI systems to automate processes, make data-driven decisions, and achieve desirable growth. As AI continues to evolve and become more accessible, embracing this technology will be essential for staying ahead of the curve. However, it is challenging to adopt AI for your needs without technical expertise and business analysis. If you want to get maximum benefit from any AI tools or even customize some of them to fit your business needs, set up a free consultation with our experts today.

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:

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!

Devtorium UI/UX Design Services: What We Can Do for You

Posted on February 6, 2024February 6, 2024 by mkoftielieva

Surprisingly, the majority of people have very little idea of what exactly is included in the UI/UX design services they purchase from specialized providers like Devtorium. Of course, in the end, you get the design of the app or other materials you’ve requested. However, this type of service is much more expansive and covers many processes, from market research to branding.

Today, we’d like to explain what’s included in a comprehensive design package using Devtorium UI/UX design services as an example. From the post below, you’ll learn what UI/UX and graphic designers can do for you and how you can use their services to help your business at any stage of development.

Devtorium UI/UX Design Services: How We Create Web Designs

Our designers start with research to create a stunning, visually appealing, user-friendly design. In fact, the stage during which the UI/UX designer creates the visuals usually takes less time than preparation. The quality of work you put in during the research and planning stages determines whether your product can deliver a superb customer experience.

No one can argue that design affects user retention. Therefore, it’s one of the crucial success factors in our world of information overload. In order to deliver outstanding UI/UX design services, the Devtorium team takes the following steps.

How Devtorium UI/UX design services work.

Digital and website discovery (General User needs research)

Our team starts the design process by conducting discovery sessions. At this time, we gather requirements and brainstorm ideas to understand the underlying purpose behind your digital goals. These sessions allow us to clearly outline the client’s needs and help align our strategies with your objectives.

During this stage, we perform:

Competitor research
Field research
User personas and user stories study

Digital and website strategy (Information Architecture)

We start designing solutions once we thoroughly understand the client’s goals and challenges. At this stage of Devtorium’s UI/UX design services, our team will create wireframes and customer journey maps. This way, we can define the high-level structure of screen layouts, flow, behavior, and organization. These outputs visually represent our proposed solutions and help us envision how the identified problems can be addressed effectively.

Our primary goal is to lay out content and functionality on each page strategically. We aim to maximize usability and user experience. To achieve this, we first build informational architecture:

Organise, structure, and label content, functions, and features
Choose between different design patterns for content layout
Use design patterns for content
Map out website and content
Plan site navigation

After the architecture is complete, we create wireframes that the client can effectively use when attracting investors.

Digital and website design development (User interface prototyping)

With the website architecture complete, we can provide the rest of the innovative UI/UX design services and branding package. Depending on the client’s requirements, we can build a visual brand style from scratch or use their existing one. During this stage, we use tools like style tiles, mood boards, and prototyping to facilitate the process.

Devtorium’s design services also include graphic design, which covers the creation of:

Brand and identity
Logo
Marketing collateral

When working within the premise of our UI/UX design services, we can also leverage video and photos to enhance the website’s reach and engagement.

Why invest in UI/UX design services from Devtorium.

Why It Pays to Invest in Creative UI/UX Design Services

We live in an era where everything is lightning fast, and you only have one chance to make an impression. Your product can be extraordinary and offer hundreds of solid benefits. However, if you can’t capture the client’s attention enough to slow down and learn about it, you’ll lose them.

According to research published in the Behaviour and Information Technology Journal, you have only 0.05 seconds to make a favorable first impression. 50 milliseconds is all it takes to form an impression in the brain of a modern information-overloaded person. If that impression isn’t enough to capture their interest to pursue further study of your product, your chances of conversion are slim.

That’s how important great design is, and if you want people to keep using your products, you must provide a stellar user experience. It really is that simple today. Therefore, above all else, you must never skimp on making your product as user-friendly, intuitive, and attractive as possible. If you want to make your product that way, contact us for a free consultation!

If you are interested in more design topics, check out some of our other posts:

Introduction to AI Part 2: Next-Level AI Terms Glossary

Posted on January 22, 2024January 22, 2024 by mkoftielieva

2023 was a breakout year for Generative AI, and it has proven beyond all doubt that expanding your vocabulary with common AI terms is necessary now. Artificial intelligence technology is fast becoming an indispensable part of our lives on every level. Therefore, you must understand at least the basics of how it works and what it can do.

Devtorium will continue our series of articles about AI, its capabilities, and developments that are revolutionizing multiple industries. Today, we will expand our AI terms glossary with some more fundamental terminology. Understanding these concepts will help you get a better idea of how artificial intelligence models work with data. You can find the first post on basic AI terminology here.

AI Terms Glossary (Still in Alphabetical Order)

Bias

Bias is a phenomenon in machine learning (ML) that occurs when the outputs of ML algorithms are skewed. This happens due to the prejudiced assumptions made during the algorithm development. Simply put, it’s AI’s reaction to an error in the initial algorithm. This is often a reaction to human error or prejudice that occurs during programming the ML algorithm.

For example, it can happen if the data AI is given to learn from isn’t comprehensive enough or is programmed with cognitive human biases. In this case, the bias starts leading AI toward specific outcomes, affecting the results’ clarity and accuracy. If you are looking for a more technical explanation of how bias works in ML models, check out the relevant page on GeeksforGeeks.

Embedding

Embedding is a technique used to represent data (text, images, and audio) as a mathematical vector. Machine Learning models use data converted through embedding to capture semantic relationships and patterns. That’s the only data format they can directly process at this point of technological development, so embedding is one of the most important AI terms you need to understand right now.

Embeddings allow NLP models to process data, find contextual meanings, and perform tasks like querying, classification, comparison, and recognition. For example, Word2Vec and GloVe are popular embedding methods that are used for word embeddings. In simpler words, they help AI understand texts.

Reinforcement Learning (RL)

Unsurprisingly, Reinforcement Learning is a type of Machine Learning that improves decision-making algorithms over time. It enables an AI-driven system to learn through interacting with its environment using the trial-and-error method. In very basic AI terms, this can be explained as the AI’s ability to learn from all its interactions with the user, both successful and not.

Also, RL uses the exploration-exploitation trade-off. It means that a computer balances the need to discover new, better strategies while exploiting the ones it already knows. The machine does this in order to achieve maximized rewards. This ML model is commonly used in robotics, gaming, and various autonomous systems.

Vector Database

Vector databases are designed specifically for handling embeddings. The main difference between traditional and vector databases is in their data optimization and querying methods. Basically, instead of querying a row with a perfect value match, vector databases use a similarity metric, searching for a vector most similar to the query.

Devtorium developers used vector databases while working on AI-based recommendation systems, searching for images and text, NLP, and fraud detection software. We’ve also written an extensive article on the topic of vector database applications in AI and their pros and cons.

What’s After AI Terms Glossary?

The topic of AI is getting hotter and more relevant in our fast-developing world. Therefore, Devtorium will continue with our effort to explain exactly how this technology works and what it can do. The potential AI tech has is unlimited, and it has already changed human lives in significant ways. We hope that our developers and solution architects will be able to leave their mark on the world as well by creating new AI-powered products.

If you want to read more about AI, check out some of our previous articles: