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Alinia RAG Guardrails: Advancing Hallucination Detection in highly-regulated and multilingual settings

Retrieval-Augmented Generation (RAG) presents a powerful approach for integrating external knowledge into language models, however, its multi-step approach can complicate evaluation and monitoring by introducing challenges at various stages. Alinia RAG Guardrails, built on the RAG Triad framework, establish a new standard for detection accuracy and efficiency. The framework focuses on robust context retrieval, mitigating

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Hallucination Detection Redefined

Our Alinia RAG Guard is a leading model for detecting hallucinations and irrelevance in RAG-based applications. Tested across six languages and benchmarked against industry leaders like GPT-4o-mini, IBM Granite, and AWS Bedrock, our multilingual guardrails deliver up to 40% better performance in reducing errors—especially in high-stakes domains like finance and healthcare. Built for trust, speed, and precision, Alinia RAG Guard empowers enterprises to deploy reliable, regulation-ready AI assistants at scale.

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