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Our latest blog posts about LLM application quality management
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The 4-Layers Architecture of GenAI Applications

Developing production-grade generative AI applications requires more than just powerful models. Our analysis identifies four distinct layers—Input, Model, Orchestration, and Output—that form the foundation of reliable GenAI systems. This architectural perspective provides development teams with clear quality checkpoints at each layer, enabling precise testing strategies that evolve alongside emerging patterns like RAG and agentic architectures. Discover how this framework helps engineering teams systematically address quality challenges unique to LLM-based applications

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Zenetics Team

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Our latest blog posts about LLM application quality management
General

The 4-Layers Architecture of GenAI Applications

Developing production-grade generative AI applications requires more than just powerful models. Our analysis identifies four distinct layers—Input, Model, Orchestration, and Output—that form the foundation of reliable GenAI systems. This architectural perspective provides development teams with clear quality checkpoints at each layer, enabling precise testing strategies that evolve alongside emerging patterns like RAG and agentic architectures. Discover how this framework helps engineering teams systematically address quality challenges unique to LLM-based applications

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Zenetics Team
General

ISO 42001: Effective AI Governance for in Modern Organizations

ISO 42001 provides the first standardized framework for AI management systems, helping organizations implement responsible governance practices for their AI applications. The standard addresses risk mitigation, quality management, and regulatory alignment, making it valuable for companies of all sizes developing or using AI technologies—especially as regulations like the EU AI Act shape the evolving AI landscape.

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Zenetics Team
General

Building Reliable GenAI Solutions for Media and Publishing Companies

Media, Publishing and Advertising are one of the key industries where GenAI already has made a big impact. The resulting disruption presents both threats and opportunities to media business models. But how can media companies build reliable applications that meet the specific needs of their business? This article outlines the potential and challenges for GenAI in Media and introduces a process that enables companies to build reliable applications fast and with confidence.

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Zenetics Team
General

The Hidden Cost of Unreliable GenAI Applications

In the race to harness Generative AI's transformative potential, companies are discovering an uncomfortable truth: the path from promising prototype to reliable production system is filled with hidden obstacles and unexpected costs. While GenAI offers unprecedented opportunities for building competitive advantage, organizations consistently underestimate what it takes to deliver dependable AI-powered solutions. This article outlines major challenges and negative effects, and outlines a solution with an AI-ready quality management process.

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Zenetics Team
General

Taming the AI Complexity: Why We Built ZENETICS

As organizations race to implement generative AI, they face unique challenges: complex inputs and outputs, inherent randomness, and rapidly evolving models. Current solutions can't bridge the gap between AI's potential and reliable implementation. At ZENETICS, we believe that teams that work on challenging LLM applications deserve a new generation of tools that help them ship complex applications fast and with confidence.

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Zenetics Team