Practical notes on AI, identity and access management, Keycloak, and cybersecurity.
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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KeyCloak is one of the most widely adopted enterprise identity and access management (IAM) platforms today. At Inero Software, we work with Keycloak on a daily basis — delivering Keycloak deployment, integration, and consulting services for organizations that rely on it as a core security component.
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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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Keycloak Deployment Auditing – General Scope and Guidelines Practical lessons from auditing multi-realm, multi-client Keycloak environments in medium and large organizations 1.
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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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Secure Email Delivery in Keycloak 26.2 Using XOAUTH2 Email has been one of the oldest and most fundamental services on the internet, used for notifications, password resets, verifications, and more.
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Introduction Shipping single sign‑on quickly is tempting. Stakeholders push for a smooth login experience, developers want to move on to core features, and security teams are eager to tick the “MFA enabled” box. The trouble is that identity and access management (IAM) decisions outlive launch days.
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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.
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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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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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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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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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