CUBIG prepares and improves enterprise data so the AI you adopt can actually use it.
We validate that data against your use case with the model or agent you choose,
and revalidate it when conditions change in operation.
An AI-Ready Data Operating Layer prepares data for a specific AI use case, validates whether it meets the task criteria with the chosen model or agent, and keeps the evidence needed to revalidate when conditions change.
Most enterprises already have data, and much of it has been cleaned. Clean data is still not automatically AI-Ready. Whether data is ready depends on the task, the model or agent, the evaluation criteria, and the operating conditions. Data that works in a pilot can fall short in production, and results can drift once data, models, or policies change.
Customers start with the product that fits their problem: Syntitan, DTS, or LLM Capsule. Syntitan, the AI-Ready Data Platform, runs three paths: Ready for Raw Data, Ready for Model, and Ready for Agent. DTS and LLM Capsule can be adopted on their own and also run as core capabilities inside Syntitan.
Enterprises already have systems that manage data and AI models.
But neither is built to check whether data is ready for the use case,
fill what falls short, and revalidate when conditions change.
Diagnose the data on six axes and apply the suggested fixes, even before a use case is set. Each successful run saves a new version.
Set the data and evaluation conditions for a model task, compare before and after, and check the criteria you agreed on.
Turn work materials into tasks and cases to check your agent against. Linking real agent runs is planned for December.
Enterprise AI stalls between pilot and production
when no one can show the data is ready for the task,
and still ready after something changes.
Most teams can make AI work in a PoC. Production asks harder questions: which data version ran, under which conditions, and whether it still meets the criteria after data, models, or policies change. Answering them means checking the model and the data state together, under the same conditions.
We believe production AI needs a layer that prepares data for each use case,
validates it under fixed conditions,
and keeps that evidence as things change.
CUBIG builds that layer.
CUBIG was founded in 2021 as the first startup from the Ewha Womans University commercialization competition. Working with finance, public-sector, and defense data, we kept hitting the same walls. Data we could not use because of compliance. Data too damaged for training. AI that worked in a PoC but degraded after deployment.
Data governance managed access but did not make data usable. MLOps tracked models but not the data state behind each run. So we built the missing layer: Syntitan, with DTS and LLM Capsule as core capabilities that can also be adopted on their own.
Our team comes from enterprise AI, data engineering, and privacy technology.
We work with the restricted, changing data that production AI depends on.
We build the layer between your data systems and the AI you run. Features solve single problems. A layer handles a whole class of them for the AI systems that depend on it. Each decision starts with which problem it solves and what it makes possible next.
A PoC is not proof. We build for production: restricted data, compliance constraints, schema changes, multi-team pipelines. Every decision is tested against one question: does it hold when conditions change after deployment?
Every claim is backed by operational evidence: before and after outcomes, state comparisons, reproducible runs. We do not claim an accuracy gain without showing what changed and how it can be verified. If we cannot prove it, we do not say it.
Syntitan is our flagship platform.
DTS and LLM Capsule can be adopted on their own,
and they also run as core capabilities inside Syntitan.
The AI-Ready Data Platform. It prepares and validates enterprise data for AI work, then keeps track of changes and result evidence after real use. Ready for Raw Data diagnoses data on six axes. Ready for Model compares data before and after under the same model and evaluation conditions. Ready for Agent turns work materials into tasks and cases for checking an agent.
More →
Data Enablement for AI. Fills missing cases and rebalances skewed distributions so scarce, imbalanced, or restricted data fits the AI task, without moving the original. Adopted on its own, used inside Syntitan, and embedded in LLM Capsule for data that substitution alone cannot keep usable.
More →
Context-Preserving Data Layer for AI. Lets AI work on operational data that cannot move raw. Sensitive terms are consistently substituted while structure, context, and relationships stay usable, and the original values are restored through an internal mapping kept by the customer.
More →From global cloud marketplaces and analyst recognition
to Korean banks, insurers, public agencies, and national defense,
CUBIG works where data needs the most care.
Map your production constraints (data that is locked, damaged, or drifting) to the right path across Syntitan, DTS, and LLM Capsule.
Book architecture review →Research collaboration, press, partnership discussions, or anything not covered above.
[email protected]4F, NAVER 1784, 95 Jeongjail-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea.
21 Arthur Street, Belfast, Antrim, BT1 4GA, United Kingdom.
Start with a free data readiness assessment,
or decide in an architecture review which product to start with.
The assessment is free. Data preparation, fit validation, and operations are paid.
Ready to go? Try it on your data