Companies needing data sharing
When customer info, finance, or confidential data restricts external AI use
Regulation and privacy block its use in AI training
Missing values and bias cap AI performance
Shifting data state makes AI results unreliable
CUBIG converts enterprise data to AI-ready state and provides the data layer where real AI projects can run.
Up to KRW 200M government voucher — bring your enterprise data to AI-ready state
AI Voucher inquiry (Demand companies)The AI Voucher program is a government scheme that funds SMEs and mid-sized companies adopting AI solutions, covering up to KRW 200M per project. Demand companies form a consortium with an AI solution supplier and run real AI projects — data analysis, AI model development, AI service deployment.
When customer info, finance, or confidential data restricts external AI use
Plenty of data, but unstructured, scattered, and low-quality — hard to train or analyze with
AI model in place, but unstable execution makes results keep changing
Pre-review of AI goals and data environment
Define AI Voucher project structure and scope
Write the project plan and apply
Agreement among demand company, CUBIG, NIA
Build and operate the AI system
Up to KRW 200M government voucher — convert enterprise data to AI-ready state and build the execution environment
SynTitan is the data operations platform that manages AI execution so enterprise data stays usable in production. DTS and LLM Capsule secure data while expanding usable scope, so internal data can be used safely with AI.
Data operations platform that manages AI execution
Synthetic Data Generation
Sensitive-data detection and protection
Customer transaction data contains PII and sensitive info, so we could not use it directly to train models. Regulation also made external environments hard.
LLM Capsule detected and de-identified sensitive info, and DTS produced experiment-ready data without exposing the source.
We had service data but not enough for training. Specific event and pattern data was missing, so model performance was unstable.
We expanded training data with synthetic data that preserved the statistics of the original. Class imbalance also improved.
The model itself was fine, but shifting data state changed the results. We could not even track which data state a run used.
We could pin the data state at run time and compare results across runs. Reproducing and analyzing AI outcomes got much easier.
Use the AI Voucher to bring enterprise data into AI-ready state and build an AI environment that actually works.
Voucher inquiry