BusinessProcessesOptimization - Inero Software - Software Consulting https://inero-software.com/tag/businessprocessesoptimization/ We unleash innovations using cutting-edge technologies, modern design and AI Mon, 26 May 2025 09:01:34 +0000 en-GB hourly 1 https://wordpress.org/?v=6.9.1 https://inero-software.com/wp-content/uploads/2018/11/inero-logo-favicon.png BusinessProcessesOptimization - Inero Software - Software Consulting https://inero-software.com/tag/businessprocessesoptimization/ 32 32 153509928 Is Your Company Ready for New Technology? How to Evaluate Technological Readiness https://inero-software.com/is-your-company-ready-for-new-technology-how-to-evaluate-technological-readiness/ Mon, 26 May 2025 09:01:34 +0000 https://inero-software.com/?p=8029 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…

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

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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.

]]> 8029 LLM Implementation and Maintenance Costs for Businesses: A Detailed Breakdown https://inero-software.com/llm-implementation-and-maintenance-costs-for-businesses-a-detailed-breakdown/ Wed, 14 May 2025 06:44:35 +0000 https://inero-software.com/?p=7981 In this post we discuss the types of costs associated with using dedicated LLMs and present example calculations for popular models (such as GPT-4, Claude, Mistral, LLaMA, etc.), including business use case scenarios.

Artykuł LLM Implementation and Maintenance Costs for Businesses: A Detailed Breakdown pochodzi z serwisu Inero Software - Software Consulting.

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When considering the introduction of artificial intelligence into your company, it’s important to understand the costs involved in implementing and maintaining your own LLM. Expenses go beyond just paying for model usage (e.g., token-based API fees) and include a range of factors — from infrastructure to security. Below, we discuss the types of costs associated with using dedicated LLMs and present example calculations for popular models (such as GPT-4, Claude, Mistral, LLaMA, etc.), including business use case scenarios.

More and more companies are considering the use of large language models (LLMs) in their own products and processes. These “dedicated” models can act as intelligent assistants—answering customer questions, analyzing documents, generating reports, and much more. You can read more about it here.

 

Types of Costs When Using LLMs

Before starting the implementation, it’s important to understand all the components that contribute to the total cost of using a dedicated model.

Infrastructure:

If you’re using models via a cloud API (OpenAI, Anthropic, Google), you only pay for the tokens used. The infrastructure cost is “hidden” on the provider’s side.

If you choose to self-host a model such as Mistral or LLaMA, you’ll need to maintain a GPU server—either locally or in the cloud. For example, renting an instance with an A100 GPU typically costs $1–2 per hour, which amounts to $750–1,500 per month if the server runs continuously. While such an investment can handle a high volume of queries, it may be underutilized at a smaller scale.

Licensing and Model Fees

Commercial models come with licensing or subscription fees. For example, when using the GPT-4 API from OpenAI or Claude from Anthropic, you pay per token used according to the provider’s pricing (we outline token costs in detail later on). On the other hand, open-source models like LLaMA or Mistral are available for free—there are no licensing or token fees. Meta, for instance, released LLaMA 2 under a license that allows businesses to use it freely. However, “free” doesn’t mean zero cost—you’ll still pay for the infrastructure and electricity needed to run the model (as mentioned earlier). It’s also important to check license restrictions: some open models may have specific usage conditions (e.g., restrictions on certain industries).

Model Adaptation and Customization

For an LLM to perform well in a specific company setting, it often requires customization—such as additional training (fine-tuning) on company-specific data or at least the preparation of tailored prompts (known as prompt engineering). This adaptation process can generate significant costs:

    • Model Fine-Tuning: Training a model on your own dataset requires computing power (typically GPUs running for many hours) and expert knowledge. For larger models, this can cost anywhere from several thousand to tens of thousands of dollars—factoring in both infrastructure expenses and specialist time. Even fine-tuning a smaller model (e.g., GPT-3.5) via OpenAI’s API can incur significant costs, as it involves processing hundreds of thousands or even millions of tokens during training—billed according to the provider’s token pricing.

    • Prompt Engineering: As an alternative or complement to training, you can craft tailored prompts and instructions for the model. While writing prompts itself doesn’t require paid resources, iteratively testing and refining multiple versions consumes tokens (which adds cost when using a cloud-based model) and takes up team time. This can be viewed as either an operational cost or a competence-related expense—specialist time is needed to optimize the model’s behavior for your specific use case.

Operational Costs

After deploying the model, ongoing operational costs come into play. These include monitoring the model’s performance, maintaining efficiency, logging results, applying updates, and fixing potential issues. If you’re using an API, the main operational cost will be the monthly bill for consumed tokens, along with any premium subscription fees (some providers offer subscription plans with usage limits or preferred pricing). If the model is hosted locally, operational costs typically include:

    • Electricity consumption – GPU-based models can consume significant amounts of power, leading to substantial monthly energy costs.

    • System administration – Time spent by administrators on server maintenance, backups, and updating software components (e.g., AI libraries).

    • Infrastructure scaling – As demand grows, additional machines or cloud instances may be needed, resulting in further expenses.

    • High availability – If the LLM assistant needs to operate 24/7 without downtime, you may need to invest in redundant resources (e.g., backup servers) or enter into an SLA agreement with your cloud provider.

Team Expertise

Implementing an LLM requires the right expertise within the IT/Data team. If your company lacks AI experience, it may be necessary to train existing employees or hire new specialists—such as an ML engineer or MLOps expert—which adds recruitment or training costs. Alternatively, some companies choose to work with external consultants or service providers to deploy the model. This also incurs costs, usually one-time project fees, which can be significant. It’s also important to account for the time your team spends integrating the model with existing systems (e.g., connecting it to a database or user-facing application). This is a labor cost that’s often overlooked in smaller projects but can have a major impact in practice.

The categories above show that the total cost of owning a dedicated LLM-based solution goes far beyond just the fee for accessing the model. It’s important to consider all these factors before making a decision. In the next section, we’ll look at specific numbers: how much a single prompt costs for various popular models, and what it would take to maintain a simple LLM assistant in two example business scenarios.

Cost of a Single Prompt in Popular LLM Models

Language models are typically billed based on the number of tokens processed. A token is a small piece of text—it may represent a single word or part of a word (for example, 1,000 tokens roughly equals 750 words of continuous text). API providers list prices per 1,000 or 1 million tokens.

Below is a comparison of the approximate cost to process 1,000 tokens using selected popular LLM models:

LLM Model Comparison
LLM Model Access / License Cost per 1000 tokens Notes
GPT-3.5 Turbo (OpenAI) Cloud API (chat model available, e.g., in ChatGPT) $0.0015 (input)
$0.0020 (output)
  • Very low cost – 16k tokens + paid upgrade to 128k
  • Good response quality
GPT-4 (8k) Cloud API (OpenAI) $0.08 (input)
$0.16 (output)		 High quality; high cost
GPT-4 Turbo (128k) Cloud API (OpenAI) $0.01 (input)
$0.03 (output)
  • Reliable large context (up to 128k tokens)
  • Cheaper (only slightly more than GPT-3.5)
Claude Instant v1.2 Cloud API (Anthropic) $0.0008 (input)
$0.0024 (output)
  • Fast, lower-cost Claude model (equivalent to GPT-3.5)
Claude 2 (100k) Cloud API (Anthropic) $0.008 (input)
$0.024 (output)
  • High-quality model by Anthropic; context up to 100k tokens
Mistral 7B Open source (free model) Token cost: $0
  • Requires self-hosting
  • Alternative to GPT-3.5 – low hardware requirements (can run with <1M tokens)
LLaMA 2 13B Open source (free model) Token cost: $0
  • Self-hosting required
  • Needs stronger hardware (e.g., 2× 24GB GPU) than 7B, but still accessible for many companies
LLaMA 2 70B Open source (free model) Token cost: $0
  • Requires self-hosting
  • Requires expensive infrastructure (e.g., 8× 80GB GPUs)
  • At this scale, costs may match or even exceed GPT-4

Legend: How Token Costs Are Calculated

    • Input tokens – words contained in the user’s prompt.

    • Output tokens – words generated by the model in response (completion).

For most commercial providers, the cost is charged separately for input and output tokens. For example:

GPT-4 Turbo:

    • 1,000 input tokens: $0.03

    • 1,000 output tokens: $0.06

If a dialogue contains a total of 1,000 tokens (e.g., 500 input + 500 output), the cost is approximately $0.045.

For simplicity, you can assume that a full interaction of 1,000 tokens costs about $0.09.

By comparison:

    • GPT-3.5 Turbo – a similar 1,000-token dialogue costs only about $0.0035 (i.e., 0.35 cents).

    • Open-source models (e.g., Mistral, LLaMA) – token costs are $0, since the models run locally. You only pay for infrastructure-related costs (power consumption, server uptime, etc.).

Open-source models (such as Mistral, LLaMA, etc.) are attractive because they come with no fees for the model itself—you can generate any number of tokens without paying the model provider a cent. However, to run these models, you need to maintain your own infrastructure. At a small scale, the cost of renting a machine for a single query may actually exceed the cost of an individual API call to a model like GPT. On the other hand, at a large scale—with many queries per day—open-source solutions can become significantly more cost-effective. In summary, cost-effectiveness depends on the use case, which we’ll explore in the next section.

Example Costs of Implementing an LLM Assistant (100 Queries per Day)

Let’s now consider a practical scenario: your company wants to implement a simple LLM-based virtual assistant that performs one of the following tasks:

    • Document analysis – e.g., the assistant reads offers or contracts and extracts key information such as clauses, deadlines, and amounts.

    • Customer inquiry handling – e.g., the assistant replies to customer emails with questions about pricing, product availability, technical support, etc.

Let’s assume that:

    • The assistant will handle approximately 100 interactions per day.

    • Each interaction consists of a prompt and a response, totaling around 2,000 tokens (e.g., 1,000 tokens in the prompt—roughly 750 words or several paragraphs—and 1,000 tokens in the response, or about 750 generated words). This token size covers fairly complex queries and detailed replies.

    • On a monthly basis, the assistant will process around 6 million tokens (3,000 interactions × 2,000 tokens = 6,000,000 tokens).

We want to compare the monthly operating costs of such an assistant depending on the choice of model and deployment approach. We’ll present two variants:

    • API Variant (Closed Model): We use a commercial model via an API (e.g., OpenAI GPT or Anthropic Claude). We don’t maintain our own servers—costs are limited to token usage, billed according to the provider’s pricing.

    • Self-Hosted Variant (Open-Source Model): We use an open-source model (e.g., Mistral or LLaMA) deployed on our own servers. Costs include infrastructure needed to support approximately 100 queries per day—such as cloud GPU instance rental or hardware amortization, plus electricity.

Below is a table comparing estimated monthly costs for several example models under both deployment variants, assuming 6 million tokens per month:

Monthly LLM Cost Comparison
Model (variant) Estimated Monthly Cost Comment
GPT-3.5 Turbo (API) approx. $18 (USD)
  • Very low cost for this quality level.
  • Estimate: approx. $0.0027/1k tokens → $12 for generating 4M tokens + $6 for prompts → ~$18/month total.
GPT-4 (8k) (API) approx. $270
  • Much higher cost for better quality.
  • Example: 8M tokens → cost: 8M × $0.08/1k (input) + $0.16/1k (output) → $270–$540 monthly.
GPT-4 Turbo (128k) (API) approx. $18
  • Slightly more expensive than GPT-3.5 due to cheaper input/output token pricing.
  • May even deliver better quality than GPT-4 (8k).
Claude Instant (API) approx. $20–25
  • Comparable to GPT-3.5 in cost.
  • Estimate: approx. $0.0021/1k tokens (input+output) → ~$18–25 for 8M tokens (plus potential flat fees).
Claude 2 (API) approx. $150–200
  • Cheaper than GPT-4, but still several times more expensive than GPT-3.5.
  • Estimate: $0.032/1k tokens → ~$192 for 8M tokens.
Mistral 7B (open source, self-hosted, 1x GPU) approx. $300
  • Cost mainly for maintaining server/GPU.
  • Assumption: 1x 24GB GPU instance – model generates ~30–60 tokens/sec, power usage 100–150W.
  • Actual cost depends on location and usage (electricity + server = ~$300–400/month).
LLaMA 2 70B (open source, self-hosted, multi-GPU) approx. $1,000+
  • High cost due to powerful GPU requirements.
  • Typically requires at least 8×80GB GPUs (~$10k–12k hardware + high power consumption).
  • Costs vary based on setup model (on-prem / cloud / GPU provider).
Local model (e.g., LLaMA 13B, GPTQ, Mistral 7B – CPU) approx. $300–500
  • Cost includes operation of local server.
  • May be slower than GPT-3.5, but offers more privacy and control.
  • For CPU instance (e.g., 12 cores, 64 GB RAM), monthly cost is mainly for electricity and maintenance.

From the above comparison, several key takeaways can be drawn:

Small-scale usage (100 queries/day) favors API solutions

With relatively low query volume, using a commercial API (OpenAI, Anthropic) is highly cost-effective—especially with lower-priced models like GPT-3.5 or Claude Instant, where monthly costs can be as low as a few dozen dollars. For higher-end models, monthly costs may rise to several hundred dollars. Still, at this scale, running your own GPU server at $300+ per month would be less economical than relying on cloud-based APIs.

Large-scale usage (thousands of queries) changes the equation

If your assistant becomes successful and the number of queries increases by 10x or even 100x, the monthly API bill could grow to thousands or even tens of thousands of dollars. In such cases, investing in an open-source, self-hosted model starts to make financial sense.  With a high enough query volume, the per-request cost of running the model locally becomes lower than the API cost—since the purchased or rented hardware is being used more efficiently. In extreme cases of massive scale, some organizations may even consider training their own model from scratch—but this is typically reserved for the largest players with very substantial budgets.

Use Case Matters (Quality vs. Cost Efficiency)

Choosing the right model shouldn’t be based solely on cost—it also depends on the quality of output required for your use case. In a document analysis scenario, precision in extracting information is the top priority. A lower-cost or open-source model may be sufficient here, especially if fine-tuned to the task. A model with 7B–13B parameters can offer adequate performance at a much lower cost. Moreover, when processing sensitive documents (e.g., contracts), running the model locally ensures that the content never leaves your organization—an invaluable benefit from a legal and data privacy standpoint. On the other hand, in customer inquiry handling, where natural language quality, politeness, and contextual understanding are critical, GPT-4 can significantly outperform smaller models. In this case, a company may find it worthwhile to pay more for superior customer experience.

Hidden Costs Around the Project

It’s important to note that the above calculations cover only the technical costs—such as token usage or infrastructure. In practice, there are also “soft” costs to consider, including staff time for preparing the implementation, integrating the model with systems like a CRM or knowledge base, testing, and ongoing iterations and improvements. For example, if the assistant needs to retrieve data from a company’s internal document repository, those documents often need to be organized or cleaned before they can be effectively used by the model.

Cost Example: AI Assistant for Analyzing Emails and PDF Documents

Here we also present the cost breakdown of our assistant based on Google’s Gemini model, which we described [here]. Its task is to automatically analyze incoming emails to identify insurance policies and extract key data from attached PDF documents—such as policy number, insured party address, or payment confirmation.

Average Token Count per Email:

    • Input: 3,500 tokens

    • Output: 220 tokens

Analyzing 100 emails with attachments using the Gemini 2.0 Flash model costs approximately $1.50.

Summary

Can We Afford Our Own “ChatGPT” in the Company? As we’ve seen, the answer is: it depends—primarily on the scale of usage and quality requirements. The key lies in selecting a model and deployment method that aligns with your specific needs. An iterative approach is often the most practical: start with a lower-cost model or API, evaluate the results, and scale up to a more powerful model or self-hosted solution as the project matures. Regardless of the path you choose, careful planning and cost monitoring across all categories is essential. We hope this comparison helps you make informed decisions and prepare a realistic budget for implementing a dedicated LLM in your organization.

If you’re considering implementing an assistant in your company, it’s worth finding answers to the following questions:

    • Do I need high-quality responses (e.g., GPT-4), or is an approximate answer sufficient (e.g., Claude Haiku, Gemini Flash)?

    • Am I processing sensitive data (e.g., customer documents)?

    • Do I have an IT team capable of hosting a model in-house?

    • What is the expected number of queries per day/month?

    • Is it more cost-effective to maintain my own infrastructure, or should I pay for API access?

For small to medium-scale applications, the cost of using a dedicated LLM can be quite reasonable. Thanks to cloud-based services, it’s possible to get started for just a few dozen dollars per month with models like GPT-3.5 or Claude Instant—an excellent option for experimentation and early prototypes. If you need top-tier performance, such as what GPT-4 offers, you’ll need to account for higher costs. However, even a few hundred dollars per month can be justified if the business value is significant—for example, by automating tasks that would otherwise require many hours of manual work.

On the other hand, for large companies planning intensive AI use, costs can grow exponentially—making it worth considering open-source options and greater investment in in-house infrastructure. Open models like LLaMA or Mistral offer freedom from per-token fees, but shift the cost burden to hardware and staffing. They become cost-effective when operating at scale or when full control over data is a top priority.

Looking to Bring AI Tools into Your Company?

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Artykuł LLM Implementation and Maintenance Costs for Businesses: A Detailed Breakdown pochodzi z serwisu Inero Software - Software Consulting.

]]> 7981 Chatbot, Agent or AI Assistant? Find Out Which Solution Is Best for Your Business https://inero-software.com/chatbot-agent-or-ai-assistant-find-out-which-solution-is-best-for-your-business/ Thu, 08 May 2025 08:57:21 +0000 https://inero-software.com/?p=7947 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.

Artykuł Chatbot, Agent or AI Assistant? Find Out Which Solution Is Best for Your Business pochodzi z serwisu Inero Software - Software Consulting.

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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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Artykuł Chatbot, Agent or AI Assistant? Find Out Which Solution Is Best for Your Business pochodzi z serwisu Inero Software - Software Consulting.

]]> 7947 How to Prepare Your Company for AI Agent Implementation https://inero-software.com/how-to-prepare-your-company-for-ai-agent-implementation/ Tue, 08 Apr 2025 08:45:46 +0000 https://inero-software.com/?p=7741 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.

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

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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:

  1. Needs analysis and readiness assessment
  2. Data and content preparation
  3. Solution design
  4. Assistant development and configuration
  5. Testing and pilot phase
  6. 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.

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

How to reduce risk?

  1. 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).
  2. Clear disclaimers and warnings – The AI should inform users: “I am an AI assistant – please verify my responses before making a decision.”
  3. 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.

 

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]]> 7741 Optimization of Back-Office Processes with AI Agent Implementation: A Practical Example https://inero-software.com/optimization-of-back-office-processes-with-ai-agent-implementation-a-practical-example/ Wed, 19 Mar 2025 12:38:32 +0000 https://inero-software.com/?p=7613 In this blog we will explore how an AI-powered agent can support legal, administrative, and human resources departments. Check out our latest case study.

Artykuł Optimization of Back-Office Processes with AI Agent Implementation: A Practical Example pochodzi z serwisu Inero Software - Software Consulting.

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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.

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]]> 7613 Meet Your Personal AI Agent: A Case Study for a Freight Forwarding Company https://inero-software.com/meet-your-personal-ai-agent-a-case-study-for-a-freight-forwarding-company/ Fri, 21 Feb 2025 11:27:19 +0000 https://inero-software.com/?p=7341 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…

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

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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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]]> 7341 Cybersecurity: More Than Just Antivirus Software https://inero-software.com/cybersecurity-more-than-just-antivirus-software/ Tue, 18 Feb 2025 12:54:19 +0000 https://inero-software.com/?p=7231 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.

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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.

]]> 7231 Security Information and Event Management Systems: Why Is It Worth Adding Keycloak? https://inero-software.com/security-information-and-event-management-systems-why-is-it-worth-adding-keycloak/ Thu, 06 Feb 2025 10:15:42 +0000 https://inero-software.com/?p=6930 In this blog, you’ll learn how Keycloak can support your SIEM system.

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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.

Do You Want to Implement Keycloak?

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

]]> 6930 Best Practices in Keycloak: Secure Your System in 5 Steps https://inero-software.com/best-keycloak-practices/ Mon, 13 May 2024 13:55:51 +0000 https://inero-software.com/?p=5732 Keycloak is a tool for managing identity and access that ensures the security of applications and web services. To maximally secure your environment using it, it’s important to implement best practices. Here are 5 key steps that will help you in this process. Enable HTTPS and Use Strong Certificates The…

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Keycloak is a tool for managing identity and access that ensures the security of applications and web services. To maximally secure your environment using it, it’s important to implement best practices. Here are 5 key steps that will help you in this process.

Enable HTTPS and Use Strong Certificates

The first and most crucial step is to ensure all communication with the Keycloak server is done through the secure HTTPS protocol. Using SSL/TLS certificates from trusted providers protects against data interception and manipulation.

In this step:

    • Configure the Keycloak Server: Set the server to use only HTTPS, rejecting all unencrypted HTTP requests.
    • Update Certificates: Regularly renew and update SSL/TLS certificates to avoid the risk of exploiting outdated keys.

Implement Multi-Factor Authentication (MFA)

This feature adds a layer of security by simultaneously using multiple methods to verify a user’s identity. 2FA (Two-Factor Authentication) is a popular form of MFA that often requires users to enter a password and confirm their identity with a second factor, such as a code from an authentication app.

In this step:

    • Activate Multi-Factor Authentication in Keycloak: Enable MFA for all users, especially those with administrative access and access to sensitive data.
    • Choose Authentication Methods: Keycloak supports various MFA methods; commonly used ones include authentication apps (e.g., Microsoft Authenticator).

Read also:

Implement strong password policies and session management

Password and session management are key to protecting user identities and preventing unauthorized access. They are the first line of defense against attacks such as brute force or phishing. Keycloak provides a wide range of configurable password policy settings from the administrative console.

In this step:

    • Configure the password policy: Set precise rules for password selection to require specific lengths, complexity (e.g., the presence of special characters, uppercase and lowercase letters), and define the password’s lifespan and history.
    • Limit session lifespan: Set short but practical session and token lifespan to minimize the window for potential attacks. Automatically logging out users after a specified period of inactivity is important for preventing accidentally leaving sessions open on shared or public devices.

Read also:

Secure API endpoints and use Role-Based Access Control (RBAC)

Role-Based Access Control (RBAC) allows for defining roles, assigning them to users, and managing permissions, enabling control over API operations depending on the role.

In this step:

    • Securing API endpoints: To secure API endpoints, it is crucial to apply appropriate authorization and authentication mechanisms:
    • Authentication: Implement authentication protocols such as OAuth 2.0 and OpenID Connect, so users and applications must prove their identity before gaining access to the API. Access tokens: Use access tokens, which contain information about user permissions, to verify access rights to various API resources. HTTPS: Ensure that all requests to the API are sent over HTTPS, protecting data from interception and modification. Role-Based Access Control (RBAC): Role-Based Access Control allows for managing user permissions based on their roles in the organization:
    • Defining roles: Establish roles that reflect different access levels in the application, e.g., administrator, user, guest, etc. Assigning roles: Assign roles to users that specify which resources and operations they can access. Managing permissions: Configure access policies in Keycloak to control which operations can be performed by users with a given role at specific API endpoints.

Read also: 

Regularly update and monitor the environment

Updating and continuously monitoring the Keycloak environment is essential to maintain high protection against new threats and security vulnerabilities. Keycloak updates appear every few months, and information about them can be found on the official project website or in the Keycloak documentation.

In this step:

    • Updates: Regularly update Keycloak to the latest stable versions.
    • Monitoring and logging: Use monitoring tools to track any unusual behavior and respond quickly to potential security incidents. Set up logging systems to collect key information about system operation. For example, using Kubernetes, you can efficiently manage and scale monitoring and logging tools such as Prometheus and ELK Stack. Kubernetes facilitates the deployment and management of containers with these tools, automating their deployment, scaling, and repair, which is crucial for maintaining continuity of operation and security in distributed systems.
    • Choose a proven partner: If implementing Keycloak best practices seems like a labor-intensive process that will heavily burden your team at this stage, seek help from specialists in this field.

Read also:

Inero Software has extensive experience in implementing advanced cybersecurity solutions. We create comprehensive systems for managing users and their roles, tailored to complex IT infrastructures and meeting high corporate standards. Our team, consisting of cybersecurity experts, implements advanced authorization schemes in accordance with renowned security standards. Thanks to our knowledge and experience, we provide effective protection against threats and compliance with corporate security policies.

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]]> 5732 Andrzej Chybicki: Models will be able to instantly review an entire repository and then provide the employee with ready-to-use data https://inero-software.com/andrzej-chybicki-models-will-be-able-to-instantly-review-an-entire-repository-and-then-provide-the-employee-with-ready-to-use-data/ Mon, 15 Apr 2024 06:56:52 +0000 https://inero-software.com/?p=5522 Research on artificial intelligence (AI) began with the desire to understand whether and to what extent computers can solve problems in a way similar to the human mind. Many of the early works related to AI focused on creating mathematical models and algorithms capable of performing tasks requiring intelligence, such…

Artykuł Andrzej Chybicki: Models will be able to instantly review an entire repository and then provide the employee with ready-to-use data pochodzi z serwisu Inero Software - Software Consulting.

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Research on artificial intelligence (AI) began with the desire to understand whether and to what extent computers can solve problems in a way similar to the human mind. Many of the early works related to AI focused on creating mathematical models and algorithms capable of performing tasks requiring intelligence, such as reasoning, learning, perception, or using language.

 

The development of large language models is currently one of the key topics in the field of new technologies, which is why we encourage you to read further parts of this article, which include comments from the CEO of Inero Software, Andrzej Chybicki, on the evolution of this issue over the past years.

 

Natural Language Processing. A New Approach to Data Analysis

Techniques for processing speech, text, and images have been used in industry and science for many years. They formed the basis of products based on so-called NLP (Natural Language Processing) models. These are algorithms that could understand individual words, later map relationships between words, and combine them into sequences. They began to understand and distinguish the meaning of the same words in different sequences (in other words, they distinguished context) in order to ultimately be able to interpret the meaning of entire sentences. To say that NLP models “understand” language is somewhat of an exaggeration; nevertheless, current NLP language models are able to assign individual sentences to so-called intents (intentions), i.e., distinguish their meaning. Then, by creating appropriate software, we are able to handle simple conversations, dialogues, or respond to simple questions – this is how chatbots have worked until now.

 

To illustrate this number, we might compare it to reading a several-hundred-page book in a few seconds.

Words Grow in Power: Large Language Models

LLMs, or Large Language Models, which have been developed over the past few years, represent a technological breakthrough because they no longer function merely as classifiers of user intent, but by using trained sets of input and output similarities, they are able to find connections between longer sequences of words (tokens). Large language models are now capable not only of analyzing individual sentences but also significantly longer texts. This is referred to as the length of context (or context window), which currently can be as much as 128,000 tokens. To illustrate this number, we might compare it to reading a several-hundred-page book in a few seconds.

Level of Understanding: Logic in Many Ways Similar to Human Logic

Students sometimes ask, “do computers understand text?” The answer to this question is difficult, if we do not define what we mean by “understanding”. Creators of language models and researchers deal with creating various measures, criteria to be able to evaluate whether a model “understands” a given problem. Nevertheless, in the simplest approach, we can say that a model understands a given issue if the responses (sequences of words), which it generates, are consistent with how a human expert in the field would respond.

I believe that in the context of AI technology development, the difference between how humans communicate (speech, writing) and computers (protocols, databases) is gradually blurring. It seems that in the near future, AI models will support humans in an increasing number of everyday tasks. They will be able to automatically retrieve information to send a response to a customer, suggest to employees the answers to questions, or handle communication through messengers.

 

Models will be able to browse a whole range of information, including documents and instructions covering, for example, orders, emails, contracts, or invoices, then infer based on them, and subsequently provide the employee with ready data needed for a report, an offer, or other operational actions.

Virtual Assistants: Will Employees Gain Real Help?

Much indicates that AI assistance in work is the future for many areas, yet I still see a big problem. Despite the fact that digitization, mainly during the pandemic, has accelerated rapidly, many companies and institutions still process a large part of information in an unstructured form, i.e., in the form of documents, contracts, instructions, and email messages. The amount of information that employees deal with daily is enormous.

Ultimately, models will be able to browse a whole range of information, including documents and instructions covering, for example, orders, emails, contracts, or invoices, then infer based on them, and subsequently provide the employee with ready data needed for a report, an offer, or other operational actions. Furthermore, a potential area of application will also include validation and checking of documents, analysis of dependencies between two different documents, or several sources of knowledge.

Virtual Assistant Speeds Up Daily Work

An example might be a situation where a bank employee receives a credit application along with attachments such as account status confirmation, employment confirmation, tax statement, and others. Currently, employees often have to manually check whether the data contained in such documents are consistent, e.g., whether the number given in the credit application matches the one on the employment certificate. Whether the bank account number written in the application is correct, and whether the dates of the issued income certificate are current, among many other things. Similarly, in the insurance sector, where issuing a policy requires manually checking VIN numbers on documents, registration certificates, and comparing them with data about inspections, policies, etc.

Implementing tools based on the LLM model into organizations can bring measurable benefits in the form of automation and increasing work efficiency. It is a mistake to claim that software can replace an employee. It can help them. Analyzing the European market, we see demographic trends resulting from an aging society and the decreasing availability of skilled workers.

 

In summary. Natural Language Processing (NLP/LLM) models increasingly support people in everyday tasks through automating responses to customer inquiries, assisting employees, and managing communication through platforms. These technologies not only increase work efficiency through the automation of routine tasks but also enable advanced operations like document analysis or data integration, which translates into broad applications across various industries and professional levels.

 

 

 

Andrzej Chybicki, PhD Eng.
CEO, Inero Software Sp. z o. o.
andy@inero-software.com

Artykuł Andrzej Chybicki: Models will be able to instantly review an entire repository and then provide the employee with ready-to-use data pochodzi z serwisu Inero Software - Software Consulting.

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