{"id":7988,"date":"2025-08-24T15:22:11","date_gmt":"2025-08-24T13:22:11","guid":{"rendered":"https:\/\/kaleidoscope.at\/ai-in-business\/"},"modified":"2026-06-02T15:18:59","modified_gmt":"2026-06-02T13:18:59","slug":"ai-in-business","status":"publish","type":"page","link":"https:\/\/kaleidoscope.at\/en\/ai-in-business\/","title":{"rendered":"AI in Business"},"content":{"rendered":"\n
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Successfully Deploy AI in Your Company with Terminology<\/h1><\/div>\n<\/div><\/div>\n\n\n\n
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As terminology has been our domain for decades, we see enormous opportunities for AI to finally give terminology the importance it deserves. Terminology can thus serve as a \"guard rail\" to steer the weaknesses and risks of the various AI models in an orderly fashion. <\/p><\/div>\n\n\n\n

But why do we even need these guard rails? LLMs, AI, GenAI, ChatGPT, Gemini, et al are all based on a vast amount of data harvested primarily from the Internet. The problem here is that the models all provide answers that are, at best, factually correct but terminologically rather unpredictable\u2014and at worst, are completely fictitious and demonstrably false (hallucinations). But thanks to our \"terminology-augmented generation (TAG)\" approach, you can use AI efficiently in your company. <\/p>\n\n\n\n

Retrieval-augmented generation with terminology<\/strong><\/h2><\/div>\n\n\n\n

Approaches such as retrieval-augmented generation (RAG) work relatively well to enrich LLMs with up-to-date knowledge based on documents, but they fall short when it comes to structured knowledge such as terminology. RAG systems try to find relevant text passages based on semantic similarity, leading to unpredictable retrievals and inefficient token usage. <\/p><\/div>\n\n\n\n

To access structured data sources such as termbases more efficiently, our AI team has created a specialized form of RAG: TAG \u2013 Terminology-augmented Generation<\/a>.<\/strong>
However, TAG is not just some abstract concept, but a real solution that has already been realized as a retrieval endpoint in Quickterm, much to the delight of IT departments everywhere. Once a termbase has been set up by your terminology team, TAG makes it easy and convenient to use and opens up a wide range of applications. <\/p><\/div>\n\n\n\n

Terminology is a foundational layer that helps AI deliver meaningful, trustworthy results, and to unlock this value, we need human oversight, structured data, and system integration.<\/div>