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).
Blog Inero Software
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).
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.
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.
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.
In this blog, we will first take a look at the built-in Keycloak mechanisms for password policy management. Then, we will explore the possibilities for customizing these mechanisms to better fit specific requirements.
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.
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.
This blog focuses on configuring Passkeys specifically for mobile devices, ensuring a seamless and secure passwordless experience.
The trusted devices mechanism in Keycloak is a way to enhance login convenience without significantly compromising cybersecurity.
In this article, we will take a closer look at AI Agents, which can provide valuable support, particularly in back-office processes.