Sensitive or regulated data cannot reach AI safely. Compliance constraints keep it out of training, validation, and inference.
About CUBIG
CUBIG builds the AI-ready data operating layer, turning restricted, unusable, and unstable enterprise data into states AI can actually run on.
What is AI-ready execution?
AI-ready execution is the operating layer that makes enterprise data usable, reliable, and stable for production AI.
Most enterprises have data, but most of it is not ready for AI. Some is restricted and cannot reach AI safely. Some exists but is unusable because of missing values, bias, or coverage gaps. AI that works in a pilot (PoC) often breaks down in production. Once schemas, pipelines, or conditions change, results can no longer be reproduced.
CUBIG builds the operating layer that closes these gaps. Two market entry paths, one long-term platform destination: Syntitan. Path A serves AI-ready data demand through Syntitan and DTS. Path B serves sensitive AI workflow enablement through LLM Capsule. Both converge into Syntitan.
Why we exist.
Enterprise AI stops because data is restricted, unusable, or because execution becomes unstable in production.
Most teams can make AI work in a PoC. Production is a different problem, and the cause is rarely the model or the compute. It is the state of the data underneath every run. That is where most projects stop before reaching production.
We believe these three problems, not models or compute, are what keep enterprise AI out of production. CUBIG builds the operating layer that resolves all three.
Make data usable, reliable,
and stable for production AI.
Data exists but is not usable: missing values, bias, coverage gaps, restricted access. The PoC works. Production does not.
After deployment, data and execution conditions change, so results cannot be reproduced. Traceability disappears and root cause becomes impossible.
We rebuild restricted and scarce data into AI-ready states, so it becomes usable. We fix the data state behind every run, so results stay reproducible.
That is what turns a PoC into production.
How we got here.
CUBIG was founded in 2021 by a team that had spent years building enterprise AI in regulated industries: finance, healthcare, defense. We kept hitting the same three walls. Data we could not use because of compliance. Data too damaged for training. AI that worked in a PoC but degraded after deployment.
We looked at existing tools. Data governance managed access but did not make data usable. MLOps tracked models but not the data state behind each run. None were designed to work together as one layer. The problem was not any single tool. It was the absence of a layer that handled all three blockers at once.
So we built what was missing: Syntitan, the AI-Ready Data Platform that fills the missing layer between enterprise data management and real AI execution. Two core capabilities carry the work. DTS rebuilds locked, scarce, or regulated data into AI-ready states. LLM Capsule runs LLM and agent workflows while original values stay inside your environment.
Where we are today.
The people building it.
Our team comes from enterprise AI, data engineering, and privacy technology. We have built and stress-tested AI systems at scale. That is why we know exactly where production AI fails.
Practitioners who have operated AI in regulated enterprise environments: finance, healthcare, manufacturing. Every product decision comes from something we had to fix ourselves.
The research team behind the DTS engine and the field-handling layer inside LLM Capsule. Measured guarantees, not policy promises.
Responsible for Syntitan: Release State, Run Binding, and the integration layer that connects to existing ML pipelines, data platforms, and runtime environments.
The structure that makes data AI-ready.
Syntitan is the long-term platform. DTS and LLM Capsule are core capabilities that converge into it, not standalone products beside it.
The AI-Ready Data Platform. Diagnose data readiness, prepare it for use, and fix the data state as a Release State. Every AI or agent run binds to that state, so you can reproduce it, diff what changed, and prove the result with a real run whenever you need to.
More →AI-ready data transformation engine. Rebuilds locked, scarce, or regulated data into AI-ready states, expands coverage, and restores data utility. Works within Syntitan and on its own.
More →Context-preserving data layer for AI. Runs LLM, RAG, and agent workflows on data that cannot leave in its original form. Usable results are reconstructed inside your environment. Structure-preserving substitution with business-ready reconstruction.
More →Trusted by enterprise
and government.
From global cloud and research partners to major Korean financial institutions and national defense, CUBIG operates where the data stakes are highest.
How we work.
We build the layer everything else runs on. Features solve single problems. A layer solves a whole class of problems and supports every AI system built on top of it. Every 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 say "improves accuracy" without showing what changed and how it can be verified. If we cannot prove it, we do not say it.
Get in touch.
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.
Make your AI runs
reproducible in production.
Start with Syntitan, and bring in DTS and LLM Capsule where you need them.