DEV Community: Muteki Group

The AI Talent Crisis: Why Companies Are Looking in the Wrong Place

Muteki Group — Thu, 19 Mar 2026 13:17:43 +0000

Today, many companies are writing strategies about how they will save millions of dollars with AI, but most of them never move beyond the pilot phase due to a lack of talent.

The problem is that the hiring model itself is outdated. The average hiring cycle for an AI specialist is about 4 months, while AI evolves in weeks, and the requirements for AI engineers change constantly. Trying to build a team this way is like hiring horses while the Model T is already speeding down the highway.

This is why the conversation about ai development companies vs in-house teams is becoming more relevant. One practical solution is the T-shaped team model.

Imagine the letter T:
• The vertical line is your core in-house engineering team — people who deeply understand your product, architecture, and internal systems.
• The horizontal line is a network of specialized external experts who bring targeted expertise exactly where it’s needed.

Your in-house engineers already carry the responsibility of maintaining core platforms while also trying to learn new frameworks, models, and agent architectures. Keeping up with daily innovation cycles can be nearly impossible.

Meanwhile, independent AI engineers or specialized agencies often gain experience from dozens of deployments across multiple industries.
That’s one of the biggest benefits of hiring an AI agent company or external AI specialists faster scaling of expertise, access to real-world implementation experience, reduced hiring risk, shorter time-to-market

As a result, the AI talent crisis may look intimidating, but it’s far from unsolvable.
You don’t have to compete with companies like Netflix to hire a million-dollar AI engineer. Instead, consider building more flexible team structures on time!

AI_development #Outsourcing #AI_agent #Muteki_Group

Cyber Resilience Act Compliance Services for EU Market | CRA Guide

Muteki Group — Wed, 04 Feb 2026 11:58:01 +0000

Now The EU Cyber Resilience Act (CRA) is a law, with mandatory compliance starting in 2027. From that date on, any product with digital components entering the EU market must meet CRA standards.

Certification practices are already familiar to compliance professionals: preparing for regulatory requirements, adapting products technically, and undergoing conformity assessment procedures is a lengthy and resource-intensive process.

Delaying preparation significantly increases financial costs, regulatory risks, and the risk of losing access to the EU market. So, as IT providers with CRA consulting service, we created this article to help companies assess their level of readiness for the CRA, identify their target compliance state in time, and begin implementing the necessary security and compliance measures.

Non-compliance with the standard can result in:

removal products from the EU market,
loss of contracts with EU clients and partners,
fines of up to €15 million or 2.5% of global annual turnover

These risks can be reduced to zero with a reliable partner. With over 10 years of experience in cybersecurity and regulatory compliance, we offer a comprehensive range of services to support CRA certification — from audits and assessments to technical implementation and staff training.

We’ve also developed our own compliance checklist for manufacturers, developers, importers, and distributors, so you can evaluate your readiness for the CRA

👉 Grab your compliance checklist by writing to our e-mail management@mutekigroup.com

What is the EU Cyber Resilience Act (CRA)

The CRA sets enforceable cybersecurity rules for any product entering the EU market ensuring digital goods are secure from design to delivery. It covers obligations related to:

1) secure product design,

2) vulnerability management,

3) incident reporting,

4) technical documentation,

5) security updates after the product is released.

Who does the CRA apply to? The CRA applies to both EU-based and non-EU companies if they:

1) sell products directly to customers in the EU

2) supply products through distributors, resellers, or OEM partners in Europe.

Products Covered by the EU CRA The CRA applies to nearly all products with digital components, including

1) software (both commercial and embedded),

2) hardware devices with software components,

3) Internet of Things (IoT) products,

4) networking equipment,

5) connected industrial and consumer products.

In practice, if a product processes, stores, or transmits data, it is very likely to fall under the CRA. A key requirement is integrating security from the earliest stages of product design.

CRA Requirements for Manufacturers

Before placing a product with digital components on the EU market, manufacturers must comply with the full set of requirements under the Cyber Resilience Act to ensure cybersecurity throughout the product’s entire lifecycle.

Under Article 13 of the CRA, manufacturers must conduct a cybersecurity risk assessment that takes into account the product’s intended use, expected operating conditions, and life cycle. The findings of this assessment should inform every stage—from planning and design to development, production, supply and ongoing maintenance—to reduce risks and prevent incidents.

Manufacturers are also responsible for securely integrating all components, including open-source software and third-party modules, applying proper due diligence during acquisition and use. Additionally, they must establish clear policies and procedures for managing and mitigating vulnerabilities, including coordinated disclosure when necessary.

Maintaining technical documentation is mandatory. This documentation should cover cybersecurity measures, risk assessments, known vulnerabilities, updates and the preparation of EU Declaration of Conformity. CE marking must be applied after completing the relevant conformity assessment procedure.

Additionally, manufacturers must ensure proper product identification (type, batch or serial number) and provide their name, contact details, and website on the product packaging or accompanying documentation. They must also guarantee product support for at least five years, or for the product’s expected lifespan if shorter.

Security updates released during the support period must remain available for at least ten years or until the end of the support period, whichever is longer.

The CRA also places particular emphasis on reporting obligations. Manufacturers must notify the relevant CSIRTs within 24 hours of discovering security vulnerabilities or incidents. They must notify market authorities and end users and provide vulnerability details to the developers of any integrated components. These reporting duties only become mandatory in 2027, once the transition period ends.

CRA and Software Developers

The Cyber Resilience Act (CRA) primarily targets companies that develop and commercialize software products for the European Union market. This mainly concerns non-embedded software sold or distributed alongside products with digital components.

Free open-source software and pure SaaS solutions are generally not the direct focus of the CRA, except when such software is used to remotely process data generated by hardware products sold in the EU. The CRA also takes into account existing sector-specific regulations. If software is already subject to other EU legislation, such as medical devices or civil aviation, the CRA requirements apply only to aspects not covered by the specialized legislation, avoiding duplication.

For software companies falling under the CRA, the regulation sets a clear objective: to improve the cybersecurity resilience of software products throughout their entire lifecycle.

This means implementing a risk-based approach to development, secure default settings, reducing attack surfaces, timely vulnerability remediation, and regular security updates. Special attention is given to access control, protection of privacy and data integrity, and ensuring that products can maintain the availability of key functions even after incidents. Beyond technical measures, the CRA requires developers to maintain proper documentation, including cybersecurity technical documentation, risk assessments, and, where applicable, a Software Bill of Materials (SBOM). Manufacturers must also make available an EU Declaration of Conformity explaining how the product complies with the CRA requirements and what security support users will receive. All relevant information must be retained for an extended period after the product is placed on the market.

Finally, the Cyber Resilience Act imposes duties to interact with market surveillance authorities and report incidents, but these obligations won’t be mandatory until the transition period ends in 2027. This provides software companies with time to prepare processes, adapt development, and integrate CRA requirements into their product and security strategies.

Importers, Distributors, and Third Parties

Importers play a critical role in ensuring that Internet of Things (IoT) devices entering the EU market comply with the Cyber Resilience Act (CRA). Before placing a product on the market, they must verify that the manufacturer has met all the baseline cybersecurity requirements set out in Annex I of the CRA.

Specifically, importers need to check whether the manufacturer has completed the conformity assessment, prepared technical documentation, and applied CE marking. It is also important to ensure that the product is accompanied by contact information, clear instructions, and safety-related information. The CRA places particular emphasis on documentation. For importing IoT devices into the EU, importers must have access to technical documentation, the EU Declaration of Conformity, CE marking, and complete information for both users and authorities. All of this documentation must be retained for at least 10 years.

If an importer discovers that a product does not meet essential CRA requirements or poses cybersecurity risks, they are obliged to refrain from placing it on the market, inform the manufacturer, and notify market surveillance authorities. In the event of vulnerabilities, the importer must immediately notify the manufacturer, and if the manufacturer cannot fulfill their obligations, the importer must also inform the competent authorities and end users.

Distributors: Compliance Control at the Point of Sale

Distributors are tasked with making sure that products with digital components entering the EU meet CRA requirements. They must act with due diligence, verify CE marking, and confirm that both the manufacturer and importer have met their regulatory duties.

To achieve this, distributors need to keep essential documentation on hand—such as proof of conformity, records of non-compliance, vulnerability reports, and details of corrective actions and communications with market authorities.

If a distributor spots cybersecurity risks or any CRA non-compliance, they must immediately halt distribution, notify the manufacturer and relevant authorities, and help resolve the issues. Should the manufacturer go out of business, distributors are also required to inform authorities and, when possible, end users.

Third Parties: When Modifications Trigger Manufacturer Responsibility

The CRA also applies to third parties who are not manufacturers, importers or distributors but make significant modifications to products with digital components and place them on the market. In such cases, these economic operators are considered manufacturers and must comply with all applicable CRA requirements.

However, this responsibility does not extend to routine security patches that do not change the product’s intended purpose, nor to products developed or modified exclusively for use by government administrations.

Achieving CRA Compliance

Compliance with the CRA is a structured, ongoing process rather than a one-time audit. A typical compliance path includes:

1) analyzing the product and assessing CRA applicability;

2) evaluating gaps in security and compliance;

3) developing a compliance roadmap;

4) implementing technical measures and remediations;

5) training teams;

6) conducting a final assessment and preparing documentation

Download the CRA Compliance Checklist by writing to our e-mail management@mutekigroup.com

Common Cyber Resilience Act Compliance Mistakes

For companies outside the EU, CRA certification is often underestimated.

The most common mistake is misunderstanding CRA applicability. Even if a company is not based in or does not have a physical office in Europe, it is still subject to the CRA and bears all associated risks if compliance is not achieved.

Incomplete technical documentation is another frequent issue. Security controls may exist, but if they are not properly documented, demonstrating compliance becomes impossible.

Lack of a structured vulnerability disclosure process is also a common pitfall. The CRA requires formal procedures for handling and reporting vulnerabilities—ad hoc fixes are not sufficient.

👉 Avoid CRA compliance risks — consult with our expert by using the contact form or us at management@mutekigroup.com

*Muteki Group — CRA Consulting Service *

We help companies achieve CRA compliance without disrupting business or losing access to the EU market.

Our services include:

1) auditing and assessing CRA applicability;

2) analyzing gaps in security and documentation;

3) developing a practical compliance roadmap;

4) supporting secure development and technical implementations;

5) training engineering, product, and management teams.

We offer both CRA certification and standalone services.

Our strengths lie in a track record of successful projects, a diverse team of experts, and constant tracking of EU regulatory updates. We fluently speak both the language of regulators and engineers — and know how to connect these perspectives smoothly, without extra bureaucracy or delays.

Don’t wait until the last minute to prepare for CRA.

Start taking action today—confidently with Muteki Group. Contact us for a CRA consultation!

AI trends to watch in 2026: From Hype to Partnership

Muteki Group — Wed, 17 Dec 2025 23:00:18 +0000

At the end of the year, we usually look back and try to guess what's next. Businesses wrap up their finances, experts share their views, and the market sets its hopes. We examine trends not out of curiosity, but out of pragmatic necessity: to understand what should be added to the strategy, where efforts are worth investing, and where focus should, conversely, be reduced.

2026 is not about “starting a new life on Monday.” It is about inflection points that will define the next decade of technological development.

AI_trends_to_watch_2026_Muteki_Group

AI: From a Tool to a Partner

AI is still the trend everyone's waiting for, and sometimes its race can be scary, but also the one most people get wrong. Microsoft articulates the core idea clearly: AI does not replace humans — it amplifies what people can achieve together.

It is 2026 that is expected to mark the transformation of AI from a tool into a full-fledged partner — one that not only executes tasks, but also helps shape them and find solutions.

The first waves of AI focused on automating routine processes and personalization (marketing, support, coding). The next level is context and domain understanding. For example, in healthcare, AI becomes a lab assistant; in marketing, a junior marketer; in development, a programmer.

This is shaping a new labor market reality: entry at the junior level becomes more challenging, while value shifts toward mid-level expertise, creativity, and strategic thinking.

This is not a future to be feared. It is a future in which the key skill is the ability to work with artificial intelligence, combining the strengths of humans and machines. Technology exists for people — and this principle remains unchanged.

The Computational Reality: More Capacity, Not Less

Despite popular narratives around edge AI, the reality looks different.

By 2026, the deployment and operation of AI models will consume up to two-thirds of total AI infrastructure computing capacity. The core logic of inference will remain concentrated in data centers and on enterprise servers.

This includes:

— new data centers with a combined value approaching half a trillion dollars;

— energy-intensive AI chips with a total value exceeding $200 billion;

— specialized inference chips, which, while optimized, are not necessarily less energy-consuming.

2026 is not the year of “cheap AI.” It is the year of infrastructure concentration.

AI Agents: From Automation to Trust

AI agents are a logical continuation of the broader trend, yet significant enough to warrant separate focus. They are evolving from tools into workplace colleagues to whom increasingly complex functions can be delegated.

However, the real challenge is not scale, but trust and security. Delegation is only possible when:

— the agent has a clear identity;

— access to systems and data is strictly limited;

— there is control over the data it generates;

— built-in protection against malicious actors is in place.

In other words, trust is the currency of innovation, and 2026 will only confirm this sentence.

Security becomes embedded, autonomous, and ambient — not “an add-on at the end.” Ironically, but inevitably, malicious actors will use AI as well — and AI agents themselves will become the primary instruments of defense.

Confidential Computing: The Foundation of the Agent Economy

The growth of AI is impossible without the expansion of confidential computing. This refers to secure processors and trusted execution environments that enable encrypted data to be processed without being exposed.

This is a security trend that scales in direct proportion to AI adoption. The technology relies on secure processors — hardware-based trusted execution environments — to isolate sensitive data while it is being processed in encrypted form, effectively creating fully encrypted environments for both storage and computation. Cloud providers such as Microsoft, Google and Amazon are adopting confidential computing, with trust emerging as a new currency of innovation.

Without these changes, progress in agent-based systems is not possible. As AI continues to take on more functions, it introduces risks that organizations must be able to manage in real time. This transition will be gradual, because change takes time — and trust takes time as well. Not only between people, but between technologies.

These shifts will happen incrementally. Trust requires time — both between humans and between systems.

AI in Science: An MVP Approach to Discovery

AI's been helping out in science for a while, but things are really gonna speed up around 2026. AI will come up with ideas, test them out with simulations, manage experiments, and basically work side-by-side with scientists like another member of the team. This mirrors a “wipe coding” approach, but applied to scientific research:

— the researcher defines the hypothesis;

— AI conducts initial experiments;

— the team becomes involved only once the hypothesis proves viable.

The result is faster selection of promising research directions and a significant reduction in time spent at the early stages of discovery.

Robotics, Drones, and Humanoids: Awaiting the Inflection Point

By 2026, there could be 5.5 million industrial robots worldwide, according to Statista, but still, robot sales haven't grown since 2021. Things might really change around 2030. By then, we might see a million new robots shipped each year, which is double what it is now.

This growth would be driven by two key catalysts: labor shortages in specialized industrial applications across developed economies and the exponential growth of computing power alongside the emergence of specialized AI foundation models.

Robots may expand across industries and use cases, including autonomous drones. However, if the broader ecosystem of technology, artificial intelligence, and robotics fails to address bottlenecks related to data quality, system integration, and cybersecurity, the industrial robotics market may continue to experience relatively modest annual growth.

The Fragility of Semiconductor Supply Chains

As trade restrictions on next-generation AI chip technologies continue to expand, leaders must adapt quickly to make supply chains more resilient.

In the past, manufacturing cutting-edge chips already required navigating fragile supply chains. Today, the stakes are significantly higher.

By 2026, bottlenecks may extend beyond EUV lithography to include:

— software tools;

— materials;

— highly specialized manufacturing processes.

Dependence on a limited number of suppliers is pushing governments to impose trade barriers, further complicating the global AI ecosystem.

Standardizing AI “IQ”

The AI industry still lacks a unified standard for evaluation. While there are individual benchmarks, they only cover limited aspects of AI performance, so comparisons are far from comprehensive. The absence of standardization across enterprise solutions remains a challenge.

MIQ (Machine Intelligence Quotient), developed in 2024, could become the first holistic approach, combining:

— reasoning;

— accuracy;

— efficiency;

— explainability;

— adaptability;

— speed;

— ethical compliance.

AI is no longer merely a “transformational technology.” In recent years, it has continuously amazed us, and this trend will persist. However, it is increasingly important to assess AI using comprehensive metrics — reasoning ability, accuracy, efficiency, explainability, adaptability, speed, and adherence to ethical standards — unified into a single evaluation framework.

Standardization will enable organizations to evaluate their own solutions and compare competing systems. Odd as it may sound, standardization does more than create order — it drives further improvement.

Standardized benchmarks will also help AI providers optimize their solutions, knowing that companies will monitor these benchmarks and use them to make informed decisions about AI procurement and deployment.

Standardization is not a brake; it is a catalyst for development, allowing solutions to be compared and informed business decisions to be made.

Multimodal AI

Human communication is multidimensional. To be our best assistant, AI must be multimodal, integrating text, voice, images, video, and sound to understand context, not just language.

It is only a matter of time before we all use AI as a full-fledged assistant. The challenge for 2026 is ensuring that people can interact effectively with AI systems. Human–machine interaction can take many forms, including text, voice, images, video and sound. Typical single-modal AI systems are limited to one type of input. The problem with single-modal input is context: human interaction is often subtler and more complex than written words, encompassing body language, vocal intonation and facial expressions.

The outcome:

— more natural interaction;

— improved UX;

— more ethical and balanced decisions.

The multimodal AI market is expected to grow from $1.6 billion in 2024 to $27 billion by 2034. Multimodal AI represents an opportunity to enhance AI capabilities — it cannot be ignored and will remain a key trend.

Regulation, Sovereign Clouds, and “Invisible AI”

AI regulation is increasingly a reflection of geopolitics. The EU, Canada, and other countries are moving toward sovereign control over AI infrastructure. Sovereign clouds are emerging, with the IaaS market projected to reach $169 billion by 2028.

At the same time, businesses are adopting the concept of “invisible AI,” where GenAI is deeply integrated into products and services and ceases to be a standalone object of attention.

Analyzing these trends, it becomes clear that AI in 2026 will not be about hype, but about value: standardization, full project execution, and measurable outcomes. Progress will be defined not by flashy models, but by fundamental, systemic work.

2026 is not another wave of AI excitement

It is going to be the year when promises align with reality and technology becomes not a hype, but the infrastructure of growth, efficiency, and innovation.

P.S. If you want to start 2026 with a true business transformation powered by AI and optimized processes, partner with Muteki Group as your IT experts. We help you develop AI solutions from scratch or implement the right existing tools if they are already available on the market. From strategy to execution, we guide you every step of the way.

Together, we can achieve everything — and even more.

Discover the transformative power of AI agent development service

Muteki Group — Tue, 09 Dec 2025 23:14:13 +0000

After the hot news about Manus, China's first autonomous AI agent, this topic has become even more trending. But don't worry; science fiction is still far away (in case you imagine one of the Black Mirror episodes). Now, AI agents only used in areas that people can control like understanding goals, developing and performing tasks. You use our AI agent development service to optimize your business and delegate functions because, unlike chatbots, they can learn and improve daily job performance.

Thanks to new AI models, this industry is developing rapidly, so we suggest not postponing changes for later but instead figuring out how AI agents work and how they can be integrated into your business. After all, companies that do this now will gain an advantage for years to come!

What is an AI agent?

AI agents can act autonomously in the environment and can receive information and make decisions based on data. It is one of their primary difference from traditional сhatbots. AI agents Custom AI solutions can learn and update their behavior over time, allowing them to improve until they achieve their goal. Some, such as robots, can be built in, while others are purely software-based.

Another difference from AI chatbots is that AI agents do not require prompts and instructions; they need only a goal to trigger their behavior. This means the system will use processors to consider the problem, determine the best solution, and take the necessary actions.

AI agents have the flexibility that chatbots lack. This means they can understand and interact with the environment, making them an excellent help for unpredictable tasks.

The market for AI is growing quickly: it is expected to grow from 3.36 billion US dollars in 2023 to 98.09 billion dollars by 2032, with an average growth rate of 42.3%. The new generation of AI agents is already being used in digital work environments like Slack, Microsoft Teams, and Notion, developing mixed teams of humans and machines. E-learning platforms use AI agents for personalized learning, and companies use them to support employees and students. At the same time, there is a rising need to protect personal data, adapt globally, promote ethical practices, and ensure better nutrition. The market is also shifting toward specialized solutions— AI agents focusing on specific areas like finance, healthcare, human resources, and law.

How AI agents work

AI agents typically use sensors to gather data, control systems to develop hypotheses, actuators to make decisions, and a learning system to monitor the process and address errors. But what does this entire process look like step by step? Let's explore it in more detail:

Determination of goal. In turn, the AI agent initializes it. That is, it returns the hint to the main AI model and returns the first result of the internal monologue. Simply put, an AI agent should understand the goal correctly so it repeats what it was told to do.

Task list development. After initiating the goal, the AI agent generates a list of tasks and the order in which they should be performed to achieve it. When the agent checks the hypotheses and understands that this plan will lead to the goal, the execution stage will begin.

Next, you must understand that the AI agent can collect information on the Internet or outsource some tasks depending on the model (for example, image or video generation).

While performing tasks, the agent stores all the information and manages the data in its knowledge system, enabling it to refine its strategy.

Tasks are crossed off the list as they are completed. In the meantime, the agent evaluates progress toward the goal and collects feedback on its work, both internal and external.

These five points describe a cyclical process that will be repeated until the goal is achieved. That is, the agent will continue to repeat, generate tasks, collect new information, or outsource tasks on a constant basis. This will be a constant movement forward without a pause, but with a result at the end.

One key aspect of an AI agent is its ability to learn and adapt. AI agents continually learn and improve by updating their knowledge base. This ongoing process allows them to stay practical and relevant in a constantly changing environment. As a result, their capacity to learn and enhance their performance provides a significant competitive advantage.

AI Agents development services: benefits for business

Chatbots and their presence in support revolutionized the way businesses communicate with customers. Is it really possible to connect artificial intelligence and not answer constantly repeated questions? However, using AI agents in customer support and service provides even more advantages because AI agents continuously learn and improve, allowing them to offer greater customer personalization. And let's be honest—we are all frankly annoyed by ready-made answers and want a little more interaction, even from a robot. So, say goodbye to the chatbot and welcome AI agents as a new step in customer support!

What do AI agents provide:

Efficiency. AI agents can simultaneously perform several operations with customers, reducing response time and processing a larger number of requests without reputational damage. Let's say more: Customers will be interested in talking to an AI agent.

A new level of service. AI agents provide fast and accurate answers, which users like. They are also capable of personalizing answers; thus, customers prefer to communicate with them rather than chatbots.

24/7 support. AI agents guarantee that your customers will receive a high level of service and a solution to their problems at any time of the day. This availability helps companies increase customer satisfaction.

Scalability. AI agents can perform many operations simultaneously, which makes them indispensable for processing increased volumes of customer interactions. Therefore, AI agents can be the optimal solution if you want to develop without sacrificing service quality.

Analytics. An additional advantage of working with AI agents is that they generate valuable data about customer interactions. This data can include reactions, likes, and behavior. Knowing this, companies can understand customer needs and trends, which can be used to improve services.

Accuracy. AI agents provide consistent and accurate responses to requests, reducing the risk of errors and enhancing the company's level of service.

AI agents are universal workers who work 24/7, do not make mistakes, and, in addition, collect information about customers for further training, processing, and analytics.

It sounds more than interesting, especially against the background of the fact that more and more businesses are showing interest in generative artificial intelligence. It's simple - the world is changing, and how we do business and support customers is no exception. The old methods will soon stop working altogether, but it's good that we have an alternative: AI agents.

AI agents case studies: Industries

Truly, AI agent use cases are not limited to any function or industry. And that's great. After all, every business can figure out how to realize the potential of generative AI. Some can create a universal assistant for a department, others to meet the needs of a business line. You can use agents to interact with the user or when an event occurs. The potential options are endless, so we recommend using Muteki Group's AI consulting or our ai agent development services to create an AI agent best suited for your business.

Let's look at how enterprises can use AI agents to meet their needs:

Production. AI agents can automate real-time product quality control and thus increase production rates. Their analytical capabilities can also optimize resource consumption, leading to savings and increased profits. Agents can detect temperature deviations during production and launch the necessary security systems as insurance.

Logistics companies have long used AI and chatbots to gain an advantage over competitors. With AI agents, logistics companies can forecast demand, optimize delivery routes in real-time, and provide timely changes to customers. AI agents can also perform routine tasks, such as automatically recalculating delivery costs.

Healthcare. Considering AI's analytical capabilities, clinics and hospitals can automate the moment of diagnosis (for example, MRI or tests). AI agents can also be universal support for patients and remind them about taking medications or visiting a doctor. AI assistants and their personalization ability will help doctors identify patterns and personalize treatment plans based on medical history. AI agents can also remove the routine work of creating document workflow from doctors and give them time for more critical tasks.

Investing is one of the most promising areas for AI agents because AI's analytical capabilities can predict financial risks and provide investment strategies. AI can also detect suspicious transactions and help automate financial reporting.

Cybersecurity. AI agents can become the best guards for your system because they can constantly monitor the network for suspicious actions. They also detect new types of attacks, initiate protective actions, and block activity before human intervention.

Learning. Learning and personalization should go hand in hand. AI agents can help by developing a path for each student based on their preferences, making the learning process more effective. Also, an AI agent as universal support will help provide timely feedback and quickly respond to student requests. AI agent analytics will help analyze the student's process, identify gaps, and quickly make changes.

Retail. Personalization in commerce is the way to the hearts of buyers. AI agents will recommend your products in real time based on previous purchase history; they will also take the seller through the sales funnel and analyze buyer behavior. If you need universal support around the clock, your spy in the world of trends and preferences, then an AI agent is exactly what you need.

PropTech. AI agents can take over the selection of apartments or other real estate at the client's request and significantly reduce the time spent processing options. Also, with the help of AI agents, you can eliminate one risk in real estate management, namely determining a fair price for the object, because AI agents will analyze and consider all factors and do it for you.

AI agents also help manage buildings' lighting, heating, security, and maintenance and predict whether engineering systems need repair.

AI agents can be used in many ways, and these examples only scratch the surface without expert analysis. AI technology helps you save time by handling tasks that are often time-consuming. For instance, in the PropTech industry, a client submits a request, and you need to search through an extensive database and multiple platforms to find suitable options. This process usually takes a realtor 3-4 hours each day. Additionally, the PropTech industry is highly competitive, and clients often work with several agencies at the beginning. Using an AI agent to find properties quickly can give you an advantage over your competitors.

If you question yourself how to create an AI agents is seems like you are ready to embrace the future. We at Muteki Group can provide custom ai agent development solution for your business. Whether you need to start fresh or improve your current solutions, our team can design and deliver AI-powered agents that achieve real results.

AI agents VS AI copilots

AI agents and AI copilots may seem similar, but they have different roles. Copilots are virtual assistants that help users with business tasks. While AI agents and copilots perform different functions, they can work together to enhance efficiency and collaboration.

AI agent and AI copilot collaboration examples:

Collaboration. AI copilots can manage AI agents by deciding which tasks to work on. They can also connect agents so they can collaborate instead of working alone.

Interaction and customization. With built-in copilots, users can control agents using natural facial expressions. Copilots also provide a simple platform for creating and scaling custom intelligent agents without requiring coding skills.

Dynamics. Combining AI agents and copilots enables two types of interaction: complete automation and automation with human input.

AI agents in the workspace: the best practices

As Microsoft wrote in its report, it is necessary to prepare yourself and your employees that the future is already here and that their future colleagues are AI agents. In addition, the best integration is prepared, and AI agents are no exception.

Here are some pieces of advice from our experts:

Ethical principles. As part of their learning process, humans are responsible for the work of all AI agents. So, data used to study AI agents may also be subject to verification to include aspects of discrimination.

Management. Managers can assert the level of agent autonomy and review their work. Once the job is delegated, managers switch to the level of control, which changes the corporate culture.

Data preparation. Agents need internal information, and you should be 100% sure AI agents do not steal confidential information.

Employees are important stakeholders in AI projects. Each team member has valuable knowledge and skills that help AI agents perform better. Without this knowledge, it is hard to achieve full autonomy. Therefore, the entire team plays a stakeholder role when developing the agent.

Evaluate your results regularly. Pay attention to the feedback from your peers and clients. Instead of focusing on the hours you've listened to, set a personal rule to keep track of this feedback. Regular evaluations will help you make quick adjustments and improve your outcomes!

Conclusion
Based on world market trends and news, it is important to recognize that AI agents will play a main role in future corporate culture. Business processes are changing, and using AI will be a must-have for success in the future. This is not just a dream; it's a necessary strategy.

Muteki Group is among the first companies to make AI a key part of its work, and we continue to support others in adapting to this change. We offer a complete service, from consulting on AI agents to developing custom solutions for your business needs.

Our experts are here to find the best solutions to give you an edge in the market — faster, smarter, and more effective. Contact us; Muteki Group as ai agent development company will help you turn your potential into real results.

AI as a New Computing Paradigm: Software 2.0

Muteki Group — Tue, 09 Dec 2025 23:07:14 +0000

Today, we are witnessing programming change its fundamental nature, likely for the first time in decades. We are moving from Software 1.0, where engineers manually write rules and carefully craft code, to Software 2.0, where code is generated automatically, and engineers act as supervisors and managers of this automated process. Essentially, this is a story about classical delegation — only instead of delegating routine tasks to junior specialists, we delegate them to AI, which essentially replaces that person. With this shift, the best engineer is someone with experience, soft skills, and business-oriented thinking for decision-making.

Software 2.0 is not about a person who receives a task and executes it — it is about a person who becomes the stakeholder between the project owner, the team, and the AI. This changes how we build products, organize teams, and create value for clients.

Why Software 2.0 is the most accurate analogy for modern AI

In Software 1.0, engineers write the logic: if → then → else.

In Software 2.0, the logic is learned, and engineers work with data, models, and system behavior.

Key differences:

Code is replaced by models that continuously learn and improve. There is no need to invest in learning new technologies or algorithms.

Functions become probabilistic rather than deterministic. Thanks to training, product code is not rewritten — it evolves automatically.

The center of gravity shifts from programming to data engineering + behavioral engineering of LLMs.

We are at a stage where not just technology is changing — the logic of product creation is changing, development stages are evolving, and even the minimum number of engineers needed in a team is shifting.

Software 2.0: our experience

At Muteki Group, we were among the first to focus the company’s strategy on AI. We know how this technology can transform businesses and routine processes, and we have seen the strong results automation can deliver. Shifting to AI as a new paradigm allowed us to rebuild our development approaches and offer solutions that were impossible in Software 1.0.

Automated AI agents for business processes

We develop AI agents capable of acting as autonomous assistants that continuously learn. They process customer requests, analyze documents and data, make decisions guided by defined policies, and execute multi-step tasks.

For our clients, AI agents reduce operational costs by 40–60%, speed up task processing 5–10×, and reduce dependency on “manual bottlenecks.” For example, in marketing, they can deliver up to 37% cost savings in operations.

Accelerated MVP development (from idea to launch in 4–8 weeks)

We use LLMs and generative tools to rapidly build prototypes, interactive demos, and initial product versions that already operate on real data. In Software 2.0, an MVP does not mean “minimally viable product,” because this is a different paradigm. It is essentially the initial intelligence of the system that can be quickly scaled.

Software 2.0 allows rapid hypothesis testing and informed decisions about full product development — giving us a speed advantage crucial for startups.

Integrating LLMs into existing products and services

We implement AI capabilities that make sense for a specific product — built-in assistants, personalization, and analysis of large datasets. This allows our clients’ businesses to operate in real time and remain technologically competitive. Products become more adaptive and capable of self-learning.

For clients, this means higher retention, increased conversion, and faster decision-making.

Do our engineers work within the Software 2.0 paradigm

Absolutely — when a project requires it. Otherwise, ignoring it would mean rejecting AI-driven technological progress. Standard engineering expertise alone is not enough for Software 2.0. We invest in behavioral engineering, train engineers in LLM-first thinking, and integrate AI tools directly into workflows. Our team uses automated solutions whenever they provide an advantage, because we value time and align with technological evolution.

Benefits for clients using Software 2.0

Software 2.0 transforms the development process, and as AI evolves, companies will no longer operate as they once did.

Key client benefits include:

Faster solution creation and market hypothesis testing.

Lower scaling costs, because development expenses decrease proportionally to time saved.

Flexible and adaptive products.

Systems can learn and evolve without complex improvement cycles or constant competitor monitoring.

A competitive advantage that is difficult to copy. Product logic is defined by data, not code, creating a moat competitors cannot replicate.

Software 2.0 is a future-oriented architecture.

It provides a foundation that enables AI integration without rewriting entire systems or wasting massive amounts of time and money. It’s a new way of thinking, designing, and building digital products.

Choosing an IT provider with a Software 2.0 approach gives your business a strategic advantage: speed, quality, and scalable solutions. At Muteki Group, we help businesses transition to Software 2.0 today — developing AI agents , accelerating MVP development, and integrating LLMs into products while training engineers and teams to work in this new paradigm. Choose us — let’s build the future now!

The LLM Bubble, Not the “AI Bubble”

Muteki Group — Fri, 05 Dec 2025 13:30:02 +0000

There has been much discussion about the risk of an AI bubble and the challenges of running a startup in this space. However, I believe that even if the market undergoes a correction, AI itself will not be at risk. We are not on the brink of an “AI bubble,” but rather an LLM bubble. It is this specific bubble, not AI as a whole, that may burst in the near future. I don’t foresee any significant issues with AI as a field because the AI industry is already quite large and diversified. This means that even if a segment of the industry, such as LLMs, is overvalued, it is unlikely to have a massive impact on the overall AI field or its businesses.

Language models powering Gemini, ChatGPT, and many other products receive disproportionately high attention today. In the news, enormous budgets are being poured into these massive systems, and competition is increasing month by month. It cannot remain this way forever. Suppose we are now trying to solve everything with one universal model — in any business, in any industry, with any needs. But is that even possible?

LLMs are not the answer to everything. They are not the ideal tool for all tasks. Even now, living in an LLM bubble, we already know that the future belongs to smaller, more specialized models, because they are cheaper to build, more predictable in operation, and can be easily personalized for particular business cases.

For example, does your bank need a universal model capable of holding philosophical conversations? Of course not. Banks need tools for client care management, fraud detection, safe transaction handling, risk assessment, client verification, and internal processes. Following this logic, in the coming years, we will see what is already being called model differentiation — the emergence of thousands of personalized, narrowly focused systems.

The real threat is not the LLMs themselves but the economics of their scaling. Today, R&D is developing faster than businesses can actually deploy such models into production. This means that investments are growing faster than markets can absorb them, infrastructure is becoming too expensive, and more and more companies are building almost identical models, which leads to oversupply. As a result, the market overheats and needs a correction.

And this correction will be 100% inevitable. It won’t be the “end of AI,” but it will be the end of an overvalued chapter marked by excessive investment in large language models without assessing their real impact on business. The simple picture is this: AI as an industry is too large to be affected, because AI is not only about LLMs. It is a vast ecosystem that includes computer vision, recommendation systems, AI agents, process optimization, robotics models, and specialized domain systems. And this is why, even if the LLM segment experiences a strong correction, the AI industry as a whole will remain stable and continue to grow. What we should fear is not AI, but universal solutions positioned as “one-size-fits-all answers.”

Universality is a myth. Real solutions are personalized. Don’t believe in one corporate solution that fits everyone. Even with AI, this is impossible. When we chose AI as our expertise focus 10+ years ago, we understood that it is impossible to build a model that fits every business equally well, and this understanding helped us build a different philosophy:

Products are built not only by engineers, but also by industry experts who deeply analyze the business structure, study needs, risks, and processes, and provide recommendations on which models make sense for a specific company, and which do not.

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Because the future of AI is not in universal giants, but in personalization, domain expertise, and precise solutions that truly work in real business.

How Muteki Group Approaches AI Strategy
At Muteki Group, we intentionally moved away from the idea of “universal models” and built a strategy based on three core principles:

Diagnosis Before Technology
We start with a full business audit — not with choosing a model. Our experts analyze processes, costs, constraints, risks, and opportunities, and then build a clear AI roadmap with measurable ROI for every step. Result: no generic tools, only economically justified AI solutions.

Specialized Models, Not One-Size-Fits-All Systems
We focus on building highly targeted AI systems tailored to specific business functions:

fraud detection,
anomaly recognition,
document automation,
risk scoring,
logistics optimization,
sales intelligence,
computer vision,
HR automation, and more.
These models are trained on industry-specific and client-specific data — making them more accurate, more stable, and far more cost-efficient.

Full-Cycle Transformation, End-to-End
We cover the complete AI lifecycle: strategy, PoC, MVP, infrastructure, deployment, integration, team onboarding, and long-term model evolution. This ensures that solutions don’t remain “concepts” — they work in real production environments.

Security and Compliance at the Core
Our solutions follow strict data privacy rules, local regulations, transparent model behavior, and continuous monitoring — critical for finance, aviation, healthcare, retail, and other high-responsibility sectors.

Built for the Long Term
Our systems are designed to evolve: models can be retrained, scaled, and transferred to new infrastructures without vendor lock-in. This guarantees long-term value, not short-term hype.

If you’re looking for AI solutions that actually transform your business — not abstract “universal tools” — our team will be glad to help. Contact Muteki Group to discuss your goals and build a tailored AI strategy that delivers real results.

by Katerina Gurba

CEO and Founder at Muteki Group

Bridging the GenAI Gap

Muteki Group — Fri, 05 Dec 2025 13:27:34 +0000

The accelerating interest in Generative AI does not automatically translate into measurable business outcomes. The widening disconnect between GenAI’s potential and its actual impact is what many analysts now refer to as the GenAI Gap. In essence, organizations invest heavily in promise, but execution collapses at the point where real capabilities are required. The result is predictable: investments underperform, expectations are unmet, and only those organizations capable of closing the GenAI Gap will gain meaningful competitive advantage. Recent studies reveal a striking trend: although more than 70% of companies have launched GenAI pilots, only 10–12% have successfully scaled them.

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Skills gap

The most common challenge is the lack of people trained to work with GenAI. Companies need not only ML engineers, but also data architects, prompt-engineering specialists, and compliance and governance consultants. The problem lies both in the shortage of talent on the market and in the inability to quickly develop these competencies internally.

Solution: investing in team training, engaging expert partners, and outsourcing secondary roles.

Legacy architecture and technical barriers

Using GenAI requires centralized and clean data, productive infrastructure, secure access to model services, and integration with legacy IT systems. The issue is that many corporate platforms simply cannot meet GenAI requirements — and companies are forced either to modernize their architecture or build a new one.

Solution: phased GenAI implementation, modernization of data architecture, and secure AI platforms development.

Governance and responsible AI

Implementing GenAI is a responsible decision that is impossible without rules for data use, security policies, controls, certification, and structured processes. Companies that lack this foundation cannot properly manage GenAI, achieve solid results, or scale their solutions.

Solution: establishing AI committees, implementing responsible AI practices, and forming transparent usage principles.

Trust

Even the best model will not succeed if users do not trust it. A model must inspire confidence, be convenient to use, take into account people’s workflows, and deliver accurate results.

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Solution: clear interfaces and educational materials.

FAIGMOE and Enterprise architecture

Recent studies propose new frameworks:

FAIGMOE — a structured approach to implementing Generative AI in medium and large companies.
Enterprise Architecture (EA) as a dynamic capability — a tool for aligning technological solutions with business strategy.
Both frameworks have in common that GenAI implementation is a systemic action requiring preparation and workforce training. Therefore, the universal approach is to develop a long-term AI strategy that considers not only the model but also cultural code, security, and data.

Security, risks, data quality

Information security is another major factor contributing to the GenAI gap. Companies are afraid to transfer confidential data, lack control tools, and are not confident in protecting their models. At the same time, many solutions rely on low-quality data, which leads to errors, biases, and inaccuracies.

Solution: building AI platforms with access control, anonymization, model auditing, and thorough data preparation.

Among the most important reasons for the GenAI gap are the underestimation of why GenAI is needed, the absence of real implementation value (only hype), and low workforce readiness.

There is also the issue that many companies have dozens of PoCs but not a single productive AI system. The reason is simple: pilots are not tied to business processes; they are evaluated based on impressions, not results.

Solution: launching small but highly practical use cases — automation, customer support, document management, expert systems.

Thus, bridging the GenAI gap is a strategic effort that requires considering all factors, investing in skills, and modernizing data architecture. This comprehensive approach underpins the development of all AI solutions in Muteki Group. You can contact our experts, receive a detailed consultation, and understand your next steps. We are proud to be among the first to integrate AI into our company’s expertise and to have 10+ years of experience working with this technology. We will gladly discuss your project and provide a practical roadmap for implementing AI in your business.

In the end, I would like to add that you can see the GenAI gap not as a technological setback or a flaw in your planning, but as a challenge — an organizational transformation that is inevitable if you want to succeed.

Atlas OpenAI: a new era in the confrontation between Google and OpenAI

Muteki Group — Fri, 05 Dec 2025 00:00:10 +0000

On October 21, 2025, something happened that exceeded expectations. OpenAI announced the launch of a new artificial intelligence–based browser called Atlas. This marks a new phase in the ongoing rivalry between Google and OpenAI, a situation in which the outcome remains uncertain for both tech giants.

From ChatGPT to the browser of a new generation
Many people already use ChatGPT as a replacement for the good old way of "googling" something — and now you're getting a browser where you can easily obtain information from the Internet, have your own AI agent, and, overall, the ability to optimize your workflow.

The ChatGPT audience is 800 million users per week, while the LLM model understands their lives, skills, styles, and needs. This can enable more personalized search and optimize advertising (since Google's contextual ads are based on how Google perceives you, not who you actually are).

In this regard, Atlas from OpenAI is more precise.

If a user on Google searches for "luxury lifestyle" and is then perceived as someone who lives luxuriously and needs a premium car, ChatGPT will, based on the query, better understand who the person on the other side of the screen really is.

Isn't that straight out of Black Mirror? Also, add the ability to open a side chat panel and analyze data from any website to this story.

But that's not even the most impressive part. Atlas includes a built-in AI agent (for paid users) capable of doing everything — from booking tickets to ordering children's clothes for an urgent kindergarten event.

Launch and availability

The company claims that Atlas will initially be launched on macOS, with Windows, iOS, and Android support coming soon. OpenAI also states that the product will be available to all free users immediately after launch.

Browsers have quickly become the next battleground in the artificial intelligence industry.

Although Google Chrome has long dominated this space, there is a growing sense that AI-based chatbots and agents are fundamentally changing how people perform tasks online.

Google and Microsoft have also tried to update Chrome and Edge, respectively, by adding AI-based features to make their aging products stand out among competitors.

Personalization is the main advantage

Key features of Atlas to consider:

Built-in assistant on the sidebar.

Agent mode and the browser's ability to automatically perform tasks (for example, booking tickets at an attractive price).

Memory and personalization — the browser will remember your preferences and provide relevant data.

History — you will control it yourself, setting which data to remember and which to delete.

Based on Chromium — compatible with many extensions.

How Atlas differs from ChatGPT

We've come to the main question: why install a browser if we already have ChatGPT?

Think of it this way — this might not be just ChatGPT, but an expansion on the organizational level.

That is:

Available control at the team level.

Corporate-level AI agents.

Shared workspace usage.

Integrated knowledge.

Opportunities for business and marketing

For example, this opens up great marketing opportunities for businesses.

It enables quick responses to media inquiries and reputation management, creates technical assignments for contractors based on negative experiences (not just your company's), trains AI agents in the brand's tone and voice, and optimizes communication overall.

We are talking about global marketing optimization, where a marketer or a company's marketing team can stay creative — yet maintain 24-hour, complete operational focus on their work.

How is that possible?

Simple — AI agents do it for them!

Key points:

AI assistants are moving from personal to professional levels

Atlas signals a change in how GPTs are used — from personal assistants to business assets.

For marketers, this means developing GPTs that can generate text, summarize reports, or handle common campaign-related queries — all within brand standards.

Internal knowledge can finally be used in real time

Muteki Group's Experience

At Muteki Group, we began developing AI agents for our clients back in 2024. This is truly a revolutionary service. It significantly optimizes all processes — and this is the future — by delegating routine functions to an AI agent that learns continuously and improves every day.

This will help elevate your company, team, or organization to new heights and open up new opportunities. Moreover, by choosing a custom AI agent explicitly built for your business, you create a unique solution that doesn't depend on corporate decisions or subscription changes.