Marta Kuprasz, Autor w serwisie Inero Software - Software Consulting

Is Your Company Ready for New Technology? How to Evaluate Technological Readiness

Marta Kuprasz — Mon, 26 May 2025 09:01:34 +0000

In the publication “Intelligent Agents in AI Really Can Work Alone. Here’s How.” by Gartner, the authors predict that by 2028, 33% of business applications will use agentic artificial intelligence, compared to less than 1% in 2024. This development will enable autonomous decision-making for up to 15% of daily operational tasks. Is your company prepared to take advantage of these changes?

Technological readiness is a broad concept that encompasses IT infrastructure, team capabilities, the maturity of business processes, organizational readiness for change, and compliance with legal and security policies. Companies planning to implement ERP systems, identity and access management (IAM) tools, or AI-based solutions need a strong technical and operational foundation to succeed.

How to Assess an Enterprise’s Technological Readiness?

To accurately determine an enterprise’s technological readiness, it’s worth conducting an assessment across several key areas. This process is similar to an audit, helping to answer a crucial question: is the company prepared to successfully implement and make use of a new technology?

IT Infrastructure

The assessment of IT infrastructure should begin with a clear definition of the requirements of the new technology. These requirements determine which resources will be necessary — in terms of performance, architecture, security, and availability.

Only once you have a clear technological specification can you reliably assess whether your current IT environment is capable of meeting those requirements. If you’re considering introducing AI-based tools into your organization and want to understand the exact costs associated with deploying and maintaining a large language model, be sure to check out our latest analysis.

LLM Implementation and Maintenance Costs for Businesses: A Detailed Breakdown

Managing and Accessing Data

The assessment of data management should begin by identifying what data will be used by the new technology, in what format, how frequently, and from which sources. It’s the system’s requirements that define what data is needed and in what form it must be available.

Only based on this can you determine whether the data within the organization is ready for use. It’s essential to verify whether there are technical means for retrieving data from sources (e.g., APIs, data exchange files), whether the data has a consistent structure, and whether it meets minimum quality standards. Inconsistencies, duplicates, incomplete records, or unstructured data may require an additional processing stage before the data can be effectively used.

Organizational accessibility is equally important — data must be available not only technically, but also in accordance with internal policies and regulations. For sensitive data, it’s crucial to ensure that access complies with security policies and legal frameworks such as the GDPR.

If the planned technology involves integration of multiple sources, real-time analysis, or processing of large volumes of data, it may be necessary to prepare a dedicated integration layer or reorganize the company’s data management approach.

Team Preparation and Training

Assessing team readiness should start with an analysis of the competencies required to operate the new technology. Depending on the solution, this may involve both technical skills (e.g., system configuration, data analysis) and operational knowledge (e.g., understanding business processes, interpreting system outputs).

Only once roles and responsibilities in the new environment are clearly defined can you assess whether the team has the necessary qualifications or if additional training is needed. This might involve upskilling through training sessions, involving external experts, or securing temporary support from the technology provider.

Special attention should be given to those responsible for maintaining and developing the system — they need early access to information about the architecture, data model, failure scenarios, and access controls. Without this, the new technology risks becoming a “black box,” increasing the likelihood of operational errors and making future improvements more difficult.

Team preparation should not be a one-time effort. It’s important to plan for post-implementation activities such as mentoring, internal documentation, and continuous development of skills in areas supported by the new technology.

Measuring Business Readiness

Assessing technological readiness should be treated as a process. The most effective approach is to create a roadmap of preparatory actions and then measure progress based on clearly defined stages and evaluation criteria.

The roadmap should include key areas such as IT infrastructure, data availability, team readiness, system integration, change management, and regulatory compliance. For each of these areas, it’s important to define the target requirements as well as assess the current state. This approach not only helps estimate the overall level of readiness but also identifies specific obstacles and weak points that may hinder the implementation process.

Step-by-step evaluation — based on the schedule and tasks assigned to specific teams — allows for ongoing verification of whether the company is moving toward operational readiness. This approach helps minimize the risk of unexpected delays and costs, as potential issues can be detected early, before entering the actual implementation phase.

Technological readiness is not a single end result. It’s the sum of many elements — technical, organizational, and competency-related — all of which should be assessed in the context of the specific implementation and its requirements.

How We Work

When working with clients on the implementation of new applications and systems, we always begin by discussing the business context and the organization’s actual needs. We don’t recommend off-the-shelf solutions without prior analysis — instead, we help identify which technologies have the potential to truly improve processes, and which may only lead to unnecessary costs and complications.

Drawing on our experience from IT projects across various industries, we provide step-by-step guidance — from the planning stage, through readiness assessment, to proper implementation and stabilization. We make sure the technology fits the organization’s capabilities and genuinely supports its operational development, rather than becoming an additional burden.

A well-planned implementation doesn’t end with launching an application — it ends with achieving the intended business outcomes.

Artykuł Is Your Company Ready for New Technology? How to Evaluate Technological Readiness pochodzi z serwisu Inero Software - Software Consulting.

Chatbot, Agent or AI Assistant? Find Out Which Solution Is Best for Your Business

Marta Kuprasz — Thu, 08 May 2025 08:57:21 +0000

Artificial intelligence and Large Language Models are buzzwords heard in nearly every industry. Many companies are wondering how to use them safely and which solution will be the most effective. There are plenty of options—and they’re often hard to tell apart. In this article, we break them down in a clear and easy-to-understand way.

AI can take on many roles in a company—as a chatbot, assistant, agent, data analysis tool, content generator, or knowledge search engine. So how can you choose the solution that best fits your employees’ needs? It helps to understand what each option has to offer.

Chatbot – answers questions, provides explanations, and handles requests

This is the most common use of AI in areas such as customer service and sales. An AI chatbot based on a large language model, such as ChatGPT, can hold natural conversations, understand the context of inquiries, and deliver accurate answers—24/7, in multiple languages, and without human involvement.

These solutions are typically implemented on websites, in messaging platforms (like Messenger or WhatsApp), or within helpdesk systems, where they assist with answering questions, tracking orders, or providing product information. As a result, they significantly automate customer service, reduce operational costs, and improve customer satisfaction ratings.

For the purposes of this article, we define a chatbot as an AI interface primarily intended for external users—in other words, it operates “outside the company.” This definition distinguishes it from AI agents, which perform more complex tasks within internal processes by integrating with systems, databases, or APIs.

https://www.incone60.eu/seastat

AI Agent – a tool designed to carry out specific tasks

Unlike a chatbot, which interacts with external users, an AI agent operates within the organization and supports employees by automating specific business processes. It’s not a one-size-fits-all tool—it’s built with a clearly defined purpose in mind, such as document processing, data analysis, or integration with ERP systems.

Thanks to large language models like Gemini or Claude, an AI agent can understand context, make decisions, and trigger specific actions—without human input. It can run in the background, process data from multiple sources, manage files, or handle email inboxes. Each AI agent is tailored to the company’s individual needs and specific tasks. Only then can it offer real value instead of becoming just another generic tool.

Want to see how this works in practice?

Check out our case study: Meet your personal AI agent-a case study for a freight forwarding company – where we describe how we built an agent integrated with an email inbox.

AI Assistant – supports users in daily work by operating contextually and “in the background”

Unlike a chatbot that answers questions or an agent that automates a specific process, an AI assistant is a tool that works alongside employees in real time—it understands context, suggests next steps, and makes tasks easier within familiar applications.

It’s typically integrated into a specific work environment, such as a word processor, spreadsheet, CRM, or project management tool. The assistant doesn’t replace the user—it actively supports them in making decisions, writing, analyzing data, or planning.

AI assistants like GitHub Copilot, Notion AI, or Google’s Workspace assistant show how this technology can genuinely boost team productivity and reduce time spent on routine tasks. From a business perspective, a well-designed assistant can improve work quality, reduce errors, and make onboarding new employees easier.

Other Business Applications of Large Language Models

The possibilities go far beyond chatbots, assistants, or agents. These models can take on specialized roles, supporting tasks such as document processing, data analysis, or content creation. They’re increasingly used to automatically summarize reports, extract information from unstructured sources (like emails, PDFs, or scanned forms), or answer natural-language questions based on internal documentation.

LLMs can also assist marketing teams by generating suggestions for ad copy, product descriptions, or sales messages tailored to the company’s style. In analytics departments, they provide faster access to data—generating database queries, interpreting results, and presenting insights in a way that’s easy for non-technical users to understand. These applications often don’t require building a new tool from scratch, but rather integrating the AI model into existing company systems. This way, the technology supports specific tasks—right where it’s needed.

AI Models and Data Security

Business owners and managers still approach AI tools with caution, mainly because they’re unsure how to ensure the security and confidentiality of processed data. We’ve explored these topics in previous publications that are worth reviewing.

In the article “AI User Privacy: An Analysis of Platform Policies”, we outlined the data privacy and model training policies followed by major AI providers such as OpenAI, Google Gemini, Microsoft’s Azure OpenAI, and Anthropic’s Claude.

For those considering an on-premise solution, we recommend the blog post “Top Lightweight LLMs for Local Deployment” There, we reviewed several top open-source lightweight LLMs and explained how to run them on a local Windows machine—even with limited GPU resources.

Choosing the right AI tool for your company depends primarily on the goal it’s meant to achieve. A chatbot works best where quick and accessible customer service is key. An AI agent can automate repetitive internal processes and improve information flow between systems. An AI assistant provides day-to-day support for employees—offering suggestions, summaries, or preparing data for further use.

Large language models also allow integration with existing processes—without the need to build a dedicated tool from scratch. However, implementing AI-based technology requires a well-thought-out decision, taking into account both efficiency and data security. If you’re looking to adopt AI in your company and need an experienced partner to guide you through the process, get in touch with us.

Bring AI into Your Business

We provide professional consulting and end-to-end implementation of tools based on large language models.

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How to Prepare Your Company for AI Agent Implementation

Marta Kuprasz — Tue, 08 Apr 2025 08:45:46 +0000

Implementing an AI agent in a company is not only a technological challenge but also a strategic one. As more businesses consider using artificial intelligence in their daily operations—from customer service to document analysis—successful implementation requires careful planning. This article explains what to focus on before deploying an AI agent, which areas of the business need to be well-prepared, and how to avoid common mistakes.

There are many areas where AI can be helpful. From automating routine tasks, supporting customer service and data analysis, to streamlining decision-making processes and creating intelligent assistants that support team workflows. The potential is enormous—but the key lies in properly preparing the organization for this change.

Stages of AI Assistant Implementation

The process of implementing an AI assistant in an organization can be divided into several stages, each requiring specific actions. From analyzing business needs, selecting the right language model, and preparing the infrastructure, to integrating with existing systems and testing—each step impacts the overall effectiveness of the solution.

The key stages are:

Needs analysis and readiness assessment
Data and content preparation
Solution design
Assistant development and configuration
Testing and pilot phase
Deployment and maintenance

Needs analysis and readiness assessment

To ensure the best results from implementing an AI agent, start by asking yourself: which tasks and areas have the most potential for optimization through the use of artificial intelligence?

When looking for an answer to this question, it’s worth carefully analyzing your company’s current structure, processes, and employee responsibilities. This will help identify so-called “bottlenecks” that may affect the quality of services provided. These might include, for example:

- long response times to quote requests
- teams overloaded with routine tasks
- lack of consistency in customer communication
- manual processing of documents and data
- difficulties in quickly accessing internal company knowledge

Based on this analysis, you’ll be able to identify areas for improvement as well as the people who will directly benefit from the support of AI assistants.

The second area that should be reviewed is the existing infrastructure. Implementing an AI assistant doesn’t require a large amount of hardware. If the company doesn’t want to invest in new machines, it can opt to use cloud services such as Azure, AWS, or Google Cloud.

Data is a crucial part of the preparation process. To fully leverage the potential of dedicated AI solutions, it’s important to understand that training the model behind the assistant requires datasets stored in digital form. These should be well-organized and kept in a central repository or database. The less structured the data, the higher the cost of implementing the assistant—and the greater the risk that the solution won’t meet expectations.

Data and content preparation

At this stage, it’s essential to gather all materials that contain important company knowledge—this may include PDF, Word, and Excel documents, website content, FAQ sections, emails, or data from databases.

Next, the collected information needs to be properly prepared—organized, cleaned of unnecessary content (e.g., unreadable PDFs), standardized where possible, and exported to CSV or JSON files (e.g., emails).

In some cases, such as when planning further model customization (fine-tuning), it will also be necessary to label the data or prepare a dedicated training set in the form of instructions and expected responses, for example:

{"prompt": "What documents are required to sign an OCS agreement?", "response": "The following documents are required to sign an OCS agreement: ..."}

Solution design

At this stage, decisions are made about the technical design of the solution. It’s important to define what type of assistant will best meet the company’s needs—whether it’s a simple chatbot answering questions, a more advanced assistant with access to company knowledge (so-called RAG – Retrieval-Augmented Generation), or an agent capable of independently performing specific tasks such as making bookings, generating reports, or sending emails.

The next step is selecting the appropriate technologies, including the large language model (LLM) that will power the assistant—such as GPT-4, Claude, Mistral, LLaMA, or Gemini—depending on specific needs and requirements related to privacy, cost, and integration capabilities.

Finally, it’s worth preparing a list of functions the assistant should perform and planning integration with other systems used in the company—such as the CRM, knowledge base, or email.

Assistant development and configuration

At this stage, both the technical backend and the user-facing part of the assistant (frontend) are developed. This could be, for example, a chat interface on the website, a button that launches the assistant in an application, or a widget integrated with tools like Slack. You can read more about how AI agent integration with the Slack communication platform can look here >>LINK

In parallel, the selected language model is deployed—via services such as Azure OpenAI, OpenAI API, Anthropic (Claude), Google Vertex AI (Gemini), or locally using open-source models like LLaMA, Mistral, or Mixtral.

If the assistant is meant to use internal company knowledge, a RAG (Retrieval-Augmented Generation) mechanism needs to be configured—enabling it to search and match relevant documents to user queries.

Finally, integrations with other systems—such as CRM, ticketing systems, or email—are implemented, allowing the assistant to meaningfully support the team’s day-to-day work.

Testing and pilot phase

After implementation, thorough testing of the solution is essential. The first step is functional testing—checking whether the assistant correctly understands user intent, responds in line with company documentation, and handles different types of queries appropriately.

The next phase is testing with end users (UAT – User Acceptance Testing), which helps assess how well the assistant performs in real-world scenarios and whether it meets employees’ expectations.

Based on feedback and observations, iterative improvements are made—such as adjusting responses, adding new documents to the knowledge base, or refining prompts and the agent’s logic. This phase is often repeated several times until a satisfactory level of quality is achieved.

Deployment and maintenance

After completing the testing phase, the assistant is deployed to the target infrastructure—this may be a public cloud (e.g., Azure, AWS, GCP), on-premise servers, or a hybrid solution, depending on security and availability requirements. More about this is covered later in the article.

It’s also necessary to set up monitoring, which allows you to track things like token usage, query frequency, error rates, and the quality of generated responses. This enables quick issue resolution and cost optimization.

In daily use, it’s important to keep the data up to date—adding new documents, removing outdated information, and updating the knowledge base the assistant relies on.

Over time, as business needs evolve, it may be worth considering retraining or fine-tuning the model—e.g., every few months—to better align it with the organization’s specific context.

Finally, it’s important to provide technical support and user assistance to ensure the solution is not only technically reliable but also convenient and intuitive for everyday use.

Data privacy

In the “Deployment and maintenance” section, we discussed the available options for choosing the infrastructure on which the AI agent will be deployed.

Each solution has its pros and cons. Choosing an on-premise setup gives you full control over the data, but it requires a dedicated machine with specific parameters.

Another option is using a public cloud service, such as Azure. Microsoft clearly states that data submitted to the Azure OpenAI service is not used to train or improve OpenAI or Microsoft models (source).

According to Microsoft, prompts and responses are not shared with other customers or OpenAI. Azure operates in full isolation mode: when using GPT-4 on Azure, no information from your conversations is shared with OpenAI LLC. Microsoft has confirmed this in a Data Processing Addendum (DPA).

AI decision accountability

It’s important to remember that formal and legal responsibility for the outcomes of an AI agent’s actions and the data it processes lies with the entity that implemented and oversees the solution—most often.

the organization (e.g., the company that deployed the assistant),
the system administrator,
the individual making decisions based on AI suggestions (e.g., a customer service representative, recruiter, or doctor).

How to reduce risk?

Human-in-the-loop (HITL) – A human must approve important decisions, while AI only supports the process (e.g., the assistant drafts a response, but a person approves it).
Clear disclaimers and warnings – The AI should inform users: “I am an AI assistant – please verify my responses before making a decision.”
Source verification – The AI assistant should, where possible, cite sources for its answers or indicate when it doesn’t know rather than guessing. Using RAG enables precise control over the knowledge base.

Summary

The process of implementing an AI agent must be well-planned and carefully considered. It may seem challenging at first, but with proper preparation, it can deliver long-term benefits. If you need support, feel free to contact us.

AI Agent in Your Company?

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Artykuł How to Prepare Your Company for AI Agent Implementation pochodzi z serwisu Inero Software - Software Consulting.

Optimization of Back-Office Processes with AI Agent Implementation: A Practical Example

Marta Kuprasz — Wed, 19 Mar 2025 12:38:32 +0000

Are you looking for practical examples of AI agent applications in a business setting? In our previous blog, we discussed integrating an AI agent with email in a freight forwarding company. You can find the full text here >>. This time, we will explore how an AI-powered agent can support legal, administrative, and human resources departments. Check out our latest case study.

AI agents are intelligent systems based on machine learning algorithms. You can learn more about them in this text >>. They can be integrated with various tools such as email, ERP, or CRM systems. In this example, we will demonstrate an integration with the Slack communication platform.

Slack is used as a team collaboration tool that enables messaging, organizing discussions in thematic channels, file sharing, and integration with other business applications.

One of the available options is API integration with a selected LLM (Large Language Model) such as Gemini, ChatGPT, or DeepSeek. This time, our AI agent will be based on ChatGPT from OpenAI and Google Search.

You are surely familiar with situations where an offer or contract needs to be urgently prepared by filling in a template. Every day, employees have to draft, analyze, and negotiate various documents, including commercial agreements, supplier contracts, non-disclosure agreements (NDAs), and collaboration agreements.

Many managers wonder how artificial intelligence can automate and accelerate some of these tasks without compromising the high quality of work. The solution once again lies in implementing a dedicated AI agent that supports employees by searching for and filling in information based on data from the National Court Register.

In our example, we designed the AI agent to receive commands via the Slack platform and automatically fill in contractor details in a pre-existing NDA template. Thanks to its integration with Google Search, the agent retrieves specific data from the National Court Register (KRS), including the company’s address, tax identification number, business registry number, KRS number, and the authorized representative of the company.

Next, the assistant asks for verification of the retrieved information. Once confirmed, the employee receives the NDA with the partner company’s details filled in. The completed fields are highlighted in yellow to ensure visibility of the changes made.

This is just one of many examples of how an AI agent can support documentation and front- and back-office processes. Using our developed method, we can create a tool that prepares contracts for companies dealing with, for example, seasonal employment or those with high employee turnover.

AI agents are a relatively new topic but are rapidly gaining popularity. Many companies recognize benefits such as time savings on repetitive tasks, reduced operational costs, and increased business process efficiency. By automating routine tasks, employees can focus on more strategic and creative aspects of their work.

Moreover, AI agents can operate 24/7, eliminating delays caused by staff availability. Their applications span various fields—from customer service and legal document analysis to advanced decision-support systems in business.

Companies are increasingly adopting open language models, allowing for greater control over data and the customization of AI agents to meet specific organizational needs. As technology evolves, we can expect even deeper AI integration into daily processes, along with a growing awareness of ethical considerations and security in their use.

Build an AI agent with us!

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Trusted devices in Keycloak

Marta Kuprasz — Thu, 06 Mar 2025 10:47:08 +0000

User authentication in IT systems requires finding a balance between convenience and security. On one hand, users expect the login process to involve the fewest steps possible; on the other, it is essential to protect system access from unauthorized use. One way to manage security levels flexibly is through the trusted devices mechanism, which allows users to reduce the number of required login steps for recognized and secure devices.

The stricter the security policy, the more inconvenient it becomes for users—this is the eternal dilemma for system administrators. Long and complex passwords, frequent password changes, and additional authentication factors enhance security but also lead users to find ways to bypass procedures—such as writing passwords down in notebooks or saving them in browsers.

Trusted Devices – How Does It Work?

By default, Keycloak does not recognize or remember devices, meaning each session is treated independently. However, with extensions, support for trusted devices can be added, allowing users to skip certain steps during subsequent logins.

CTO of Inero Software, Waldemar Korłub, emphasizes:

"Hence the concept of trusted devices – the first time, we need to go through all the steps, but afterward, the application can, for example, skip asking for the two-factor authentication code."

Once a device is marked as “trusted,” the system can retain its status for a specified period, allowing for a simplified login process. However, the user may still be periodically asked to reauthenticate to ensure security.

Is remembering devices secure?

While the trusted devices mechanism improves user convenience, it also introduces additional risks. The biggest threat is the theft or loss of a device that has been previously marked as trusted.

As Waldemar Korłub points out:

"If the system does not require an additional authentication factor, an attacker could gain access to all stored applications. That’s why it is crucial for users to have control over their trusted devices—ideally through a panel where they can remove them at any time."

Introducing a device management panel and the option to revoke a device’s trusted status in case of loss are essential elements for ensuring security.

How can administrators control access in Keycloak?

Administrators can restrict the device remembering mechanism, for example, to specific networks or corporate devices.

Waldemar Korłub explains:

"We can limit this mechanism, for example, to computers within the local network—if users connect via the corporate VPN, we can recognize company-owned devices and enable the trusted devices option for them."

Thanks to such solutions, organizations can prevent users from assigning trusted status to personal devices that are beyond their control.

Trusted Devices in Keycloak – Key Takeaways

- Trusted Devices Help Simplify Login but Require Proper Security Measures
  The trusted devices mechanism in Keycloak allows users to skip certain authentication steps, such as entering a 2FA code. While this is a convenient solution that streamlines daily operations, it also requires the implementation of appropriate security measures. It is essential to define the validity period of a trusted device and monitor changes in login behavior to prevent misuse.

- Administrators Can Control the Trusted Device Access Policy
  Not every device should be marked as trusted, which is why administrators can restrict this feature to corporate computers or require a VPN connection. This helps prevent situations where a user assigns trusted status to a personal computer that the organization cannot control.

- Device Management Panel Enhances Security
  To give users greater control over their sessions, it is beneficial to implement a panel that allows them to review and remove trusted devices. This way, in case of device loss or suspected unauthorized access, users can quickly revoke granted permissions.

- The Ability to Remove a Device Protects Against Account Takeover
  If a device is lost or stolen and the system does not require additional authentication, an attacker could gain access to the user’s account. That’s why it is crucial to allow the removal of trusted devices at any time and enforce reauthentication. This approach provides greater flexibility while reducing the risk of unauthorized account access.

When Should You Use the Trusted Devices Feature in Keycloak?

The trusted devices feature in Keycloak enhances login convenience while maintaining a high level of security. This functionality is particularly useful in corporate environments and BYOD (Bring Your Own Device) models, where users regularly log in from the same devices.

Marking a device as trusted reduces the number of two-factor authentication (2FA) prompts, extends session duration, and allows for dynamic security policy adjustments, such as requiring reauthentication for suspicious logins. This approach helps mitigate the risk of account takeover—even if an attacker obtains a password and 2FA code—since logging in from a new device may trigger additional verification.

Implementing trusted devices in Keycloak enables organizations to strike a balance between security and user convenience.

The trusted devices mechanism in Keycloak enhances login convenience without significantly compromising cybersecurity. Proper implementation of the device remembering policy in Keycloak helps balance system security and user experience.

However, administrators should ensure that mechanisms are in place for managing the list of trusted devices and enforcing periodic verification of their status to mitigate potential security risks.

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Want to implement Keycloak or add new functionalities? Schedule a meeting to explore the possibilities.

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What are AI Agents and how can they help your company

Marta Kuprasz — Fri, 28 Feb 2025 09:51:15 +0000

The term artificial intelligence has been prominently featured in numerous publications as a solution to challenges related to efficiency, organization, and creativity. Many companies are following this trend, striving to incorporate AI-driven solutions into their offerings. These efforts take various forms. In this article, we will take a closer look at AI Agents, which can provide valuable support, particularly in back-office processes.

For some time now, we have been observing a significant rise in the popularity of terms related to the use of artificial intelligence. So, let’s start from the beginning.

What is "Artificial Intelligence"?

The term “artificial intelligence” encompasses Large Language Models (LLMs), natural language processing (NLP) systems, machine learning algorithms, neural networks, and generative AI models.

LLMs, such as ChatGPT from OpenAI or Gemini from Google, are models trained on vast datasets that can analyze, process, and generate text in a way that mimics human reasoning. They are used in various applications, ranging from chatbots and voice assistants to advanced systems supporting business analysis and process automation in companies.

Artificial intelligence is not limited to text processing. Modern models can also analyze images, audio, video, and numerical data, making them highly versatile tools in business. AI enables not only the automation of repetitive tasks but also the detection of patterns in large datasets, trend forecasting, and support for strategic decision-making in companies.

Who are AI agents?

“AI agents” are intelligent systems based on machine learning algorithms, natural language processing (NLP) models, and Large Language Models (LLMs). Their purpose is to automate processes, support decision-making, and interact with users in a natural and context-aware manner.

This means that virtual assistants are based on well-known and widely used LLMs such as ChatGPT, Gemini, Claude, Mistral, or DeepSeek, which can generate coherent responses, analyze texts, and adapt to the context of a conversation.

However, AI agents differ from language models in that they are designed to perform specific tasks autonomously. In practice, this means they are equipped with additional modules that enable them to gather information, process data in real-time, and make decisions based on business rules.

Unlike traditional chatbots, AI agents not only answer questions but can also handle complex processes, integrate with enterprise systems, and learn from user interactions. As a result, they are used in various areas, from administrative support and document analysis to the automation of operational processes in enterprises.

Also read: Meet Your Personal AI Agent – A Case Study for a Freight Company

The operation of AI agents is based on several key components:

- Communication interface – allows the agent to interact with users through text, speech, or other data formats.
- Decision engine – based on AI models and business rules, it enables situation analysis and the selection of optimal actions.
- Integration with external systems – AI agents often operate in conjunction with databases, business applications (ERP, CRM), or cloud services, allowing them to access up-to-date information.
- Process automation – they can perform specific tasks, such as generating reports, processing requests, sending notifications, or initiating predefined processes in IT systems.

What are the types of AI agents?

AI agents may take various forms depending on their application and level of autonomy. Leveraging advanced artificial intelligence models, they can assist users in a wide range of activities, from customer support to data analysis and business process management.

We can distinguish several main types of AI agents:

- Conversational agents – include chatbots and voicebots that interact with users through text or speech. They can answer questions, handle customer inquiries, and support sales processes.
- Analytical agents – specialize in processing and interpreting data. They use machine learning algorithms to analyze trends, detect anomalies, and generate reports.
- Operational agents – automate business tasks by integrating with enterprise systems. They can manage documentation, process documents, or coordinate activities within corporate processes.
- Autonomous agents – operate independently, making decisions based on collected data and predefined business rules. They are used in areas such as logistics, resource management, and dynamic operational planning.
- Decision-support agents – provide recommendations based on advanced data analysis, helping managers and specialists make strategic decisions.

Each of these types can operate independently or collaborate with other systems, creating a complex AI-driven environment. In the following sections, we will explore specific applications of AI agents and their impact on the operational efficiency of businesses.

Cloud or on-premise solution – how can an AI agent be implemented in a corporate environment?

Implementing an AI agent in an organization requires selecting the appropriate deployment model that best meets business, technical, and regulatory requirements. Companies can choose between a cloud-based solution (SaaS) or an on-premise deployment, depending on their needs for flexibility, security, and integration with existing systems.

The choice of the appropriate model depends on various factors, which are presented in the table below.

Comparison: SaaS vs On-Premise

Criterion	SaaS (Cloud)	On-Premise (Local)
Deployment model	Cloud-based (AWS, Azure, Google Cloud)	Operates on the company’s own infrastructure
Infrastructure	Cloud service provider	Local servers
Initial costs	Low	High
Operational costs	Subscription-based	Fixed maintenance and energy costs
Scalability	Very high	Limited (dependent on hardware)
Data security	Limited (processed outside the company)	High (full control over data)
Regulatory compliance	May require additional agreements and certifications	Easier to meet regulatory requirements
Ease of implementation	Easy and fast	Requires hardware purchase and setup
Updates and maintenance	Automatic, provided by the vendor	Self-managed updates and maintenance
Integration with enterprise systems	Strong API support and pre-built integrations	Full control but may require additional integration

The choice of the appropriate deployment model—cloud-based or on-premise—depends on the company’s specific requirements regarding security, costs, and integration with existing systems. Regardless of the chosen strategy, AI agents can significantly enhance operational efficiency and allow employees to focus on tasks that require creativity and strategic thinking.

The development of AI technology is undoubtedly one of the strongest technological trends in recent years. Therefore, it is worth considering now how AI agents can support your company’s growth and become a key element of its digital transformation.

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Artykuł What are AI Agents and how can they help your company pochodzi z serwisu Inero Software - Software Consulting.

Meet Your Personal AI Agent: A Case Study for a Freight Forwarding Company

Marta Kuprasz — Fri, 21 Feb 2025 11:27:19 +0000

AI-driven tools are becoming increasingly prevalent across various industries, streamlining processes from simple graphic design and translations to advanced document, email, and database analysis. In this article, we will present a practical business application of an AI assistant in action.

AI Agents have a wide range of applications, and their full potential is still being discovered. The main advantages of AI-powered assistants include:

1. Automating Routine Processes

AI agents can handle repetitive tasks such as customer inquiries, document analysis, and data management. By automating these processes, businesses can reduce operational costs and improve efficiency.

2. Personalized Customer Interactions

By analyzing data, AI agents can provide personalized recommendations and tailored offers, enhancing customer engagement and improving overall user experience.

3. Speed and Availability

AI operates 24/7, delivering instant responses and real-time support. This is particularly valuable in industries that require quick reaction times, such as e-commerce, finance, and logistics.

4. Advanced Data Analysis

AI-powered agents can process vast amounts of data in a short time, identifying patterns and correlations that support better business decision-making.

5. Optimizing Decision-Making Processes

With predictive modeling, AI assists in demand forecasting, risk management, and supply chain optimization, helping organizations make more informed decisions.

6. Seamless Integration with Existing Systems

Modern AI solutions can be easily integrated into existing ERP, CRM, and analytics platforms, enhancing their capabilities and improving overall system efficiency.

A Practical Example of AI Agent Use in the Transport Industry

AI agents can be applied across various industries, including banking, sales, and human resource management. In this text, we will focus on a freight forwarding company that handles anywhere from a few to dozens of shipments daily.

Freight forwarders deal with constant communication and the verification of numerous documents. Each of these tasks takes time—a resource that is often in short supply—making errors more likely when the workload is high.

How can time management be improved? By automating repetitive and predictable tasks. This is where an AI Agent comes in. Here’s an example of an AI assistant we developed, powered by Google’s Large Language Model, Gemini.

One possible application is the following scenario:

A freight forwarder receives an email that should include an insurance policy along with proof of payment. The AI Agent automatically, without needing to be prompted, checks whether the email contains the required attachments. If they are included, it proceeds to verify the following details:

In the Insurance Policy:

- - Policy number
  - Insurance period and whether it is currently valid
  - Insured party details, including tax identification number and address
  - Bank account number for premium payment

In the Payment Confirmation:

- - Payment reference
  - Amount
  - Bank account number
  - Payment date
  - Whether the transfer corresponds to the submitted policy (e.g., based on the reference, account number)

The AI Agent then transfers the extracted data into a designated Excel file, which is continuously updated. The data file can be formatted accordingly, for example, by highlighting entries in red where the insurance policy is invalid or the payment has not been verified.

In this simple way, instead of searching through their inbox for the right emails, the freight forwarder can check the Excel file to see if the documents have been received from a specific sender and whether they are correct. This saves a significant amount of time and ensures data accuracy.

There are many ways to further develop our AI Assistant. It can be integrated with other tools, such as Slack or other communication platforms, to send notifications about missing documents or generate automated email responses. An AI-powered agent can be tailored to the specific needs of a company, a department, or even an individual role.

Do you want to explore the possibilities of AI Agents?

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Artykuł Meet Your Personal AI Agent: A Case Study for a Freight Forwarding Company pochodzi z serwisu Inero Software - Software Consulting.

Cybersecurity: More Than Just Antivirus Software

Marta Kuprasz — Tue, 18 Feb 2025 12:54:19 +0000

For many people, the first thing that comes to mind when talking about cybersecurity is antivirus software. However, modern cyber threats go far beyond traditional computer viruses. While antivirus software remains an important element of protection, an effective strategy for preventing attacks and minimizing the risk of data or financial loss must encompass a much broader range of tools and processes.

Cybercriminals have a vast arsenal of attack techniques and methods. In public discourse, terms such as phishing, ransomware, DDoS (Distributed Denial of Service) attacks, and social engineering are frequently mentioned.

On which threats should we focus in 2025?

Cybersecurity experts point out that these techniques continue to evolve and adapt to better target potential victims. In 2025, it is worth paying attention to three key areas:

Passkeys are replacing passwords

Passwords remain a weak link. With the rise of phishing and authentication attacks, companies are shifting to passkeys—biometric or cryptographic authentication methods that enhance security and user convenience. Tech giants like Google and Apple are leading this transition.

Phishing is becoming hyper-personalized

Generic phishing emails are a thing of the past. Attackers now use in-depth research and social media data to create highly targeted campaigns that appear legitimate. AI-generated messages mimic real conversations, making social engineering more effective. Organizations must invest in advanced detection and employee awareness to stay protected.

Deepfakes are a growing threat

AI-powered deepfake technology is becoming an increasing cybersecurity threat. Criminals use AI-generated profiles impersonating managers or other trusted individuals to bypass security measures and manipulate employees into revealing confidential information. One well-known method involves using filters during video calls to impersonate someone in real time. Companies must strengthen verification protocols and educate employees on detecting deepfakes.

When antivirus software is not enough

Antivirus software is designed to detect, block, and remove malicious programs such as viruses, trojans, and spyware. Its functionality relies on databases of known threats. However, cybercriminal techniques are evolving beyond the capabilities of traditional antivirus solutions. Attacks like phishing, ransomware, social engineering, and deepfakes are not directly linked to classic malware and often require more advanced protection methods.

How to stay secure?

First and foremost, by implementing modern authentication methods, identity management, and incident detection systems that can indicate potential attacks. This includes using multi-factor authentication (MFA), integrating with identity management systems like Keycloak, and monitoring activity through SIEM (Security Information and Event Management) tools. Such an approach enables real-time identification and response to unusual behavior that may signal a system compromise.

We wrote about why integrating Keycloak with a Security Information and Event Management (SIEM) system is worth considering here.

Start by structuring the process

A well-structured cybersecurity process begins with a thorough risk assessment and threat identification. At this stage, an organization inventories all IT assets, including devices, systems, applications, and data. It is crucial to determine which of these assets are the most sensitive and require special protection. Next, a risk analysis should be conducted to evaluate potential threats, their impact on business operations, and the likelihood of occurrence. This helps identify the key areas that require the most protection.

The next step is to develop a comprehensive security policy. This includes a set of rules and procedures governing all aspects of technology use within the organization—from password and access management to guidelines for mobile device usage and remote work. At this stage, it is essential to consider applicable legal regulations, such as GDPR, and industry standards.

Once policies are established, the next step is implementing the necessary security technologies and tools, such as the previously mentioned Keycloak. This stage involves integrating security measures like multi-factor authentication (MFA), data encryption, and IT infrastructure monitoring systems like SIEM (Security Information and Event Management). Simultaneously, mechanisms for regular software updates and data backups should be introduced to enable quick system recovery in the event of an incident.

When designing a cybersecurity process, it is important to recognize that humans are often the weakest link in security. Increasing employee awareness through training programs is critical. These programs educate staff on different attack techniques and real-world examples, helping them identify potential threats.

The final, but equally important, component is monitoring and incident response. Organizations should have clearly defined procedures for responding to security breaches, ensuring rapid detection, analysis, and mitigation of cyberattacks.

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Artykuł Cybersecurity: More Than Just Antivirus Software pochodzi z serwisu Inero Software - Software Consulting.

Security Information and Event Management Systems: Why Is It Worth Adding Keycloak?

Marta Kuprasz — Thu, 06 Feb 2025 10:15:42 +0000

Security Information and Event Management (SIEM) systems enable the collection and analysis of data on user activity, system access, and cybersecurity events to detect threats and respond to incidents in real time. Identity and Access Management (IAM) systems, in turn, provide insights into user activity. In this blog, you’ll learn how Keycloak can support your SIEM system.

In the Report on the State of Cybersecurity in Poland for 2023 prepared by CSIRT GOV, it was indicated that among the threats persisting in the Polish cyberspace in 2023, which had a significant impact on risk assessment, social engineering attacks and brute-force attacks were particularly notable. Social engineering attacks involve manipulating users to gain unauthorized access to systems, while brute-force attacks rely on automatically attempting various password combinations to break security measures.

Proper identity management and log monitoring are key elements in protecting against such attacks. This is why integrating Keycloak with a SIEM system allows organizations to detect threats more effectively and respond to them immediately.

Why Is It Worth Integrating SIEM with Keycloak?

Every organization using a SIEM system aims to detect as many threats as possible and respond to incidents as quickly as possible. Information about who attempted to access systems, from where, and when can be crucial in identifying attacks and unauthorized login attempts. This is where Keycloak—a popular open-source IAM platform—can significantly enhance the SIEM ecosystem by providing valuable data on authentication, authorization, and session management processes.

Keycloak, developed by the Red Hat community, offers comprehensive solutions for authenticating and authorizing users in web applications, mobile apps, and backend services. We’ve covered it in detail https://inero-software.com/keycloak-services/

Keycloak can provide data on:

Login attempts – both successful and failed, along with information about the originating IP address.
Forced password resets and changes in access policies – allowing for monitoring of potential account takeover attempts.
User sessions – including unusual logins from new locations or devices.
Detected threats, such as suspicious multiple login attempts (e.g., brute-force attacks, which involve cracking passwords or cryptographic keys by trying all possible combinations).

The SIEM system, in turn, can analyze this data and correlate it with other events, such as:

Login attempts from unusual locations linked to suspicious network activity.
Multiple failed login attempts from a single IP address – a sign of a brute-force attack.
Sudden changes in user privileges associated with suspicious system access.

An example of effective integration can be seen in a situation where a user repeatedly enters an incorrect password within a short period. Keycloak logs this as suspicious activity. A SIEM system can then correlate this data with login attempts from different locations and take action, such as temporarily blocking the account or enforcing additional authentication.

How Do Keycloak and SIEM Work Together?

Keycloak and Security Information and Event Management (SIEM) systems serve different purposes in identity management and IT security, but they complement each other perfectly.

Feature	SIEM (Security Information and Event Management)	IAM (Identity and Access Management – Keycloak)
Main Function	Monitoring and analyzing security events	Managing user identities and access
Scope of Operation	Log collection, incident analysis, threat detection	Authentication, authorization, access control
Types of Data	System logs, network traffic, security alerts	User sessions, authentication logs, authorization requests
Mode of Operation	Aggregation and correlation of events from multiple sources	Verification of user identities and permissions
Primary Uses	Anomaly detection, incident response, compliance	Single Sign-On (SSO), identity federation, MFA
Examples of Threats	DDoS attacks, malware, privilege escalation	Brute-force attacks, account takeover, privilege misuse
Response to Threats	Alert generation, automatic blocking, reporting	Account blocking, enforcing MFA, session management
Integration with Other Systems	Yes – collects logs from SIEM systems, IDS, firewalls	Yes – integrates with LDAP, AD, databases, SIEM

How to Implement Keycloak?

Integrating Keycloak with a SIEM system enhances IT security by providing additional information about users and their activities. This allows organizations to detect threats more effectively and respond to incidents more quickly.

If you’re wondering how to implement and configure Keycloak for your organization, be sure to check out these articles:

These resources provide practical guidance on configuring and integrating Keycloak with various systems. Importantly, one of Keycloak’s key features is its ability to integrate with Lightweight Directory Access Protocol (LDAP) directories, which we covered in detail here: Exporting accounts to federated realms: A guide to Keycloak and LDAP Integration

There are many SIEM solutions available on the market, so it’s worth conducting a security audit within your organization before making a decision. Identifying potential vulnerabilities will help guide the selection and implementation of an appropriate incident management system, enhanced with Keycloak integration, to better monitor threats and strengthen data protection across your organization.

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Artykuł Security Information and Event Management Systems: Why Is It Worth Adding Keycloak? pochodzi z serwisu Inero Software - Software Consulting.

AI Assistants: Opportunities and Challenges for Business Growth

Marta Kuprasz — Thu, 23 Jan 2025 14:03:57 +0000

Gartner predicts that by 2028, at least 15% of daily professional decisions will be made autonomously by agent-based artificial intelligence. In theory, this technology promises the development of more adaptive software systems capable of performing a wide range of tasks. However, in practice, many uncertainties remain, particularly regarding data security, compliance with internal procedures, and legal regulations. In this article, we discuss the limitations (especially in the context of the EU) and focus on the challenges of implementing AI Agents.

The rapid development of AI-based tools suggests that the practical application of “agentic” artificial intelligence for performing time-consuming tasks will revolutionize many industries, particularly those where automation and data-driven decision-making can significantly enhance efficiency.

While exploring these possibilities, it is equally important to consider the limitations.

Limitations of Using AI Agents

Dependence on Data Quality

AI requires large datasets of high quality to operate effectively. Incorrect, incomplete, or manipulated data can result in flawed decisions, which, in critical areas like medicine or finance, may have severe consequences.

The study “The Effects of Data Quality on Machine Learning Performance” examines the impact of six traditional data quality dimensions on the performance of fifteen popular machine learning algorithms. The results indicate that incomplete, erroneous, or inadequate training data can lead to unreliable models that make incorrect decisions.

For AI applications to be dependable, the training and testing datasets must meet high standards across several quality dimensions, such as accuracy, completeness, and consistency.

While this may be evident to AI/ML professionals, evaluating data quality within an organization is not always straightforward. It is essential to ensure that the information intended for use by an AI agent, such as OpenAI, is comprehensive and free from offensive content. Assessing these aspects early in the process can help prevent potential issues during implementation.

Data Security

The program, which in this case is designed to serve as a helper or assistant, is tasked with delivering reliable and verified information. Its analysis relies on the data we discussed earlier. Naturally, the best learning materials are real documents and data processed within the organization.

However, many doubts and concerns arise when it comes to providing the Assistant with sensitive company data.

The current state of legal regulations, regardless of our opinions, sets the framework for the operation of AI systems, particularly in the context of GDPR compliance. For example, agents based on Azure OpenAI infrastructure ensure full compliance with regulations when properly configured by the user, thanks to their integration with cloud services. Solutions like Azure AI Studio, which enable the creation of agents within a cloud environment, are also worth mentioning.

On the other hand, local solutions—based on models such as Llama, Bielik, or other open language models (e.g., the somewhat controversial DeppSeek)—offer full control over data and its processing but require greater technical investment, such as dedicated servers. It’s important to emphasize, however, that compliance with regulations, including GDPR, depends on the proper configuration and implementation of systems by the user, whether cloud-based or local.
The dynamic development of this field suggests that in a few months, these capabilities will be even more advanced, offering new options for both local and cloud-based solutions, says Andrzej Chybicki, CEO of Inero Software.

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The rapid development of AI-based tools suggests that the practical application of “agentic” artificial intelligence for performing time-consuming tasks will revolutionize many industries, particularly those where automation and decision-making based on large datasets can significantly enhance efficiency.

While considering the possibilities, it is also important to remain mindful of the limitations.

Cybersecurity and Authorization

User authentication poses a challenge for AI systems, requiring special attention in terms of security. While authorization is typically handled in earlier stages, there are methods to bypass this process, which can leave systems vulnerable to manipulation.

Example:

An AI agent that interacts with users can be susceptible to phishing attacks if not adequately secured. For example, an attacker might craft a query that imitates communication from an authorized user. If the agent fails to verify the context or input data, it could respond to such a query by disclosing sensitive information or performing unauthorized actions.

This highlights that prior user authorization, while critical, is not always sufficient to fully secure the system. It is essential to implement additional measures, such as contextual analysis, advanced anomaly detection systems, and robust safeguards for data processed by the AI agent. Such an approach is the only way to minimize the risks of phishing attacks and manipulation of AI agents.

In practice, user authorization should always occur before granting access to the AI assistant. In a cloud environment, such as Azure, additional security measures can be applied, such as using Active Directory or Keycloak, to reduce the risk of attacks. If data cannot be processed outside the organization, it is advisable to use local authentication and data storage solutions.
From a security perspective, however, the development of phishing-resistant mechanisms is crucial, as insufficient safeguards in AI agents can lead to serious threats, adds Andrzej Chybicki.
Z perspektywy bezpieczeństwa kluczowe jest jednak rozwijanie mechanizmów odpornych na phishing, ponieważ brak odpowiednich zabezpieczeń w agentach AI może prowadzić do poważnych zagrożeń- dodaje Andrzej Chybicki

In summary, agentic artificial intelligence has the potential to revolutionize the way professional decisions are made, particularly in tasks based on analyzing large datasets. However, these possibilities come with significant challenges, such as data quality, information security, and compliance with legal regulations.

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Artykuł AI Assistants: Opportunities and Challenges for Business Growth pochodzi z serwisu Inero Software - Software Consulting.

Marta Kuprasz, Autor w serwisie Inero Software - Software Consulting

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What are AI Agents and how can they help your company

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Also read: Meet Your Personal AI Agent – A Case Study for a Freight Company

What are the types of AI agents?

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Meet Your Personal AI Agent: A Case Study for a Freight Forwarding Company

AI-driven tools are becoming increasingly prevalent across various industries, streamlining processes from simple graphic design and translations to advanced document, email, and database analysis. In this article, we will present a practical business application of an AI assistant in action.

1. Automating Routine Processes

2. Personalized Customer Interactions

3. Speed and Availability

4. Advanced Data Analysis

5. Optimizing Decision-Making Processes

6. Seamless Integration with Existing Systems

A Practical Example of AI Agent Use in the Transport Industry

Do you want to explore the possibilities of AI Agents?​

Cybersecurity: More Than Just Antivirus Software

On which threats should we focus in 2025?

Passkeys are replacing passwords

Phishing is becoming hyper-personalized

Deepfakes are a growing threat

When antivirus software is not enough

How to stay secure?

We wrote about why integrating Keycloak with a Security Information and Event Management (SIEM) system is worth considering here.

Start by structuring the process

We will help you implement Keycloak

Security Information and Event Management Systems: Why Is It Worth Adding Keycloak?

Why Is It Worth Integrating SIEM with Keycloak?

How Do Keycloak and SIEM Work Together?

How to Implement Keycloak?

Do You Want to Implement Keycloak?

AI Assistants: Opportunities and Challenges for Business Growth

Limitations of Using AI Agents

Dependence on Data Quality

Data Security

Manage user access and permissions with Keycloak

Cybersecurity and Authorization

We will help you implement AI tools in your organization

Do you want to explore the possibilities of AI Agents?