Natural Language Generation (NLG) is a technology that automatically produces human-readable text or speech from structured and unstructured data. While its counterpart, Natural Language Understanding (NLU), focuses on comprehension, NLG transforms data into meaningful narratives. It is widely applied in chatbots, voice assistants, automated reports, and other business contexts.

Natural Language Understanding (NLU) is a critical area of Natural Language Processing (NLP) that enables computers to accurately interpret human language in context. Beyond recognizing individual words, NLU can grasp user intent and emotions, making it applicable to chatbots, voice assistants, sentiment analysis, and more. This enhances conversational AI and data-driven decision-making.

With the advancement of DX, generative AI is now widely used in text creation, customer support, and data analysis, contributing to improved efficiency and competitiveness. However, at the same time, a serious issue known as “hallucination” has emerged—when AI generates information that appears plausible but is in fact incorrect.

In today’s digital society, high-quality images are indispensable—from Instagram posts to web banners, game characters, and advertising posters. However, traditional production required specialized skills and significant costs, creating major barriers. This is where “Image Generation AI” comes into the spotlight. By simply entering text or a simple prompt, users can instantly generate professional-level visuals. For example, marketers can create advertising materials in seconds, and artists can easily experiment with new styles.

AI agents are gaining attention as powerful tools for automating and streamlining operations. For technology-driven companies in particular, reducing repetitive tasks, accelerating data analysis, and enhancing customer support are critical to boosting competitiveness. However, many organizations face challenges such as “where to start” and “how best to deploy them” when considering adoption.