How to evaluate code generated by AI agents: from uncertainty to a method
AI accelerates software delivery, but it does not replace engineering accountability. Here is how Inero combines agents with risk-based quality controls.
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AI
43 articles
How to test a voicebot before it handles a business process
Natural conversation is not enough. Learn how to test a voicebot's context handling, corrections, integration failures, and compliance with process rules.
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Monitoring what your chatbot says is not enough. You should also know what users say to it
Anthropic's new policy shows that end-user behavior can create risk for AI solution providers. We explain how to monitor it and when to respond.
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A Rapid PoC in a Few Hours. What Did We Actually Build, and What Comes Next?
We used the same requirements to build an application with Claude, OpenAI Codex, and Cursor with Grok. The experiment showed where rapid prototyping ends and responsible engineering begins.
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Your AI Demo Works. Now What? From Rapid PoC to Production
A Rapid PoC proves feasibility and builds shared understanding. Learn how evaluations, regression testing and monitoring support reliable production AI.
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Cloud or self-hosting? The Gitea vulnerability shows where the real risk lies
CVE-2026-60004 in Gitea shows why choosing between cloud and self-hosting does not replace patching, segmentation, monitoring and independent security audits.
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Jev vs. LLMs: Choosing the Right Model for AI Automation
Learn where Jev, local AI models, personal data (PII) detectors and generative LLMs fit in secure, cost-aware AI automation workflows.
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How much autonomy should an AI agent have? From automation to self-optimization
AI agent autonomy is not an ON/OFF switch. We explain how to match decisions, tools and controls to the risk of a specific business process.
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From prompt to preview: how an AI agent updates a website
How an AI agent can safely deliver a website change, from project analysis and content preparation to a pull request, Cloudflare preview and human approval.
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Giving Your Team Claude Is Easy. Building a System That Survives 5 Years Is Not.
Artificial intelligence has changed the economics of software development. For the first time in decades, the marginal cost of producing code is collapsing. A small team equipped with modern LLM tools can generate APIs, user interfaces, integration layers, and test suites in days instead of weeks.
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Why Not Every AI Automation Project Makes Sense
Over the past few years, as a team building AI solutions in enterprise environments, we have been repeatedly asked to consult on new AI automation initiatives. These conversations rarely start with theory.They start with very concrete ambitions.
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Voicebot Deployment in Call Centers: Key Considerations Beyond the Demo
Voicebot solutions are increasingly being adopted in call center operations as a way to reduce waiting times, lower operational costs, and improve service availability.
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Implementing an AI-Powered Telephony Service Center with ElevenLabs & LiveAPI
Implementing an AI-Powered Telephony Service Center with ElevenLabs & LiveAPI Over the past year, advancements in real-time AI models and high‑fidelity speech synthesis have accelerated the development of AI-driven telephony systems.
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Is Your Company Ready for New Technology? How to Evaluate Technological Readiness
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.
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LLM Implementation and Maintenance Costs for Businesses: A Detailed Breakdown
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.
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Chatbot, Agent or AI Assistant? Find Out Which Solution Is Best for Your Business
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.
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AI User Privacy: An Analysis of Platform Policies
In this article, we’ll break down the data privacy policies of top AI platforms. You will also learn what to do to ensure your data is not used for training Large Language Models (LLM).
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Top Lightweight LLMs for Local Deployment
In this post, we’ll explore several top open-source lightweight LLMs and how to run them on a local Windows PC—whether CPU-only or with a limited GPU—for document processing tasks.
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How to Prepare Your Company for AI Agent Implementation
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.
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Deploying LLMs Locally: A Guide to Ollama and LM Studio
Whether you’re building a custom chatbot, agent, an AI-powered code assistant, or using AI to analyse documents offline, local deployment empowers you to experiment and innovate without relying on external services.
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Optimization of Back-Office Processes with AI Agent Implementation: A Practical Example
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.
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OpenAI vs. DeepSeek: A Technical Comparison Using Unified APIs
In this post, we conduct a comparative analysis of three popular LLMs—OpenAI’s GPT based models: 4o-mini and o3-mini, and open-source DeepSeek R1—to evaluate their effectiveness in reading and analyzing statistical data from large PDFs.
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What are AI Agents and how can they help your company
In this article, we will take a closer look at AI Agents, which can provide valuable support, particularly in back-office processes.
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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.
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Assessing Retrieval-Augmented Generation (RAG) Large Language Models (LLMs) with DeepEval for Complex Tabular Data
This post explores how DeepEval helps systematically assess the effectiveness of both retrieval and generation components, ensuring more reliable machine-generated insights.
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AI Assistants: Opportunities and Challenges for Business Growth
In this article, we discuss the limitations (especially in the context of the EU) and focus on the challenges of implementing AI Agents.
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Andrzej Chybicki: The groundbreaking change brought by MCP and technologies based on LLM is the reduction of the communication barrier between humans and IT systems
We invite you to read an interview with Andrzej Chybicki, CEO of Inero Software, where we delve deeper into the practical applications of MCP.
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Running AI in client-side: Real-Time Face Detection in the Browser Using YOLO and TensorFlow.js – use case study
In this post, we’ll explore how to implement object detection directly in the browser using…
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Andrzej Chybicki: Models will be able to instantly review an entire repository and then provide the employee with ready-to-use data
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.
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The possibilities brought by the development and popularization of AI in business management
The development of broadly understood artificial intelligence is gaining momentum. This is evidenced by the widespread interest in this topic and an increasing number of media articles and publications.
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GitHub Copilot – review of pros and cons in a programmer’s work
GitHub anticipates that in the coming years, 80% of the world’s code will be generated by AI [1]. Although it is difficult to find exact information on the number of sold GitHub Copilot licenses, it is undeniable that we are observing a change in the way organizations and programmers themselves …
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A year under the sign of artificial intelligence development
The end of the year is a time for summaries. In the world of IT, many interesting things have happened, so in this article, we decided to focus on AI. The development of artificial intelligence and its media presence accelerated to an unprecedented scale. Tools based on Large Language Models …
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Andrzej Chybicki: The use of Artificial Intelligence already constitutes a significant part of the projects carried out by our team
Experts predict that the market for software utilizing artificial intelligence (AI) to automate tasks and generate content in response to user commands will nearly quadruple by the year 2027. How can you benefit from this growth and prepare your team for new challenges and projects?
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Case study: Deploying an AI-based solution at Farm Frites Poland
At Interizon Day 2023 Waldemar Kołub co- founder of Inero Software presented a success story about using Neuronal Networks to optimize the production process.
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Optimization of Data Collection Process Thanks to AI Algorithms
Optimization of Data Collection Process Thanks to AI Algorithms In an era of progress and numerous achievements in natural language processing, we see increasing possibilities in the analysis and inference based on data collected in unstructured textual archives and documents.
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Document Data Processing using GPT model
GPT – A DIFFERENT POINT OF VIEW In the previous post, we looked at the possibilities of the GPT model in terms of supporting the work of a programmer. But that’s not the only thing this technology can do. GPT is a language model that utilizes a deep learning technique …
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ChatGPT – A developer’s point of view
INTRODUCTION ChatGPT was introduced a few weeks ago and since then it has been one of the main topics in the IT industry. In this blog post, we’ll take a closer look at what GPT is and the potential applications of this powerful technology in the hands of software engineer. …
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Managing data with machine learning
With the revolution of Industry 4.0, production became automated. We have more automatic devices and measurements, so we can handle more data. They are becoming the driving force of industrial production and cover all its aspects.
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Digital Cloud Document Repositories – how to identify signatures in scanned PDF documents
Computer vision and object detection are increasingly used in the automation of business processes. Along with the dynamic development of technology, especially artificial intelligence, there are many new innovative business applications for this type of algorithms.
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Redistribution supply chain network
Redistribution supply chain network (RSCN) – modelling and optimization One of our teams field of specialization is the use of artificial intelligence for modeling and optimizing complex transport models.
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Few tips on how to create custom class detection system using R-CNN
Region based Convolutional Neural Network – R- CNN Thanks to the development of computing technology and artificial intelligence in recent years, applications using machine learning and systems based on neural networks have more and more business uses.
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Investing in AI – do you have what it takes
Good Software Equals Good Business Artificial Intelligence is exploding at the moment, largely due to a sharp spike both in data availability and parallel computing power, as well as improved AI algorithms. More and more companies are treating Machine Learning development services as a necessity.
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Data – the Playground of Machine Learning
Why is Data so Important? As digitization in many companies has rapidly developed, we are now faced by ever growing sets of data contained in various databases, which are unstructured, unmapped, unsynchronized and non-harmonized.
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