minchan chung

I lead CUBIG, a company focused on solving the data problems that prevent enterprises from successfully adopting AI. Today, powerful AI models are widely available. Yet many enterprises still struggle to deploy AI in real operations. The problem is not the model. It is data. Enterprise AI faces three major data challenges. First, sensitive data. Enterprise data often contains customer information or internal secrets, making it difficult to use with AI systems. Second, unusable data. Many organizations have large amounts of data, but much of it is biased, incomplete, or not usable for AI. Third, unstable data states. AI may work in PoC environments, but once deployed, production data constantly changes. As data evolves, AI results become inconsistent and difficult to reproduce. At CUBIG, we build infrastructure that solves these three problems. LLM Capsule enables enterprises to safely use sensitive data with AI. DTS transforms unusable data into AI-ready data. SynTitan stabilizes data states so AI execution can be traced, compared, and reproduced in production environments. Our goal is simple. Turn enterprise data into AI-ready data and make AI execution reliable in real operations.