Restricted data
Sensitive or regulated data cannot go to AI as it is. Compliance constraints keep it out of training, validation, and inference.
Company
About CUBIG
CUBIG builds the AI-Ready Data Operating Layer
that prepares, improves, reconstructs, and validates enterprise data for AI use.
Category
What is an AI-Ready Data Operating Layer?
An AI-Ready Data Operating Layer sits between data management and AI execution.
It prepares data for a specific AI use case, validates it under fixed conditions,
and keeps the evidence as 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, works in three areas: Data Readiness, Use Case Validation, and Operational Assurance. DTS and LLM Capsule can be adopted on their own and also run as core capabilities inside Syntitan.
Mission
Why we exist.
Enterprise AI stalls between pilot and production when no one can show that the data is ready for the task, and still ready after something changes.
What we believe
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.
What we do
Make data usable, reliable,
Make data usable, reliable,
and stable for production AI.
Unusable data
Data exists but is not ready for the task: missing values, bias, coverage gaps, or too few examples. The PoC works. Production falls short.
Unstable execution
After deployment, data and execution conditions change, and results drift. Without a record of which data version ran under which conditions, finding the cause gets hard.
We reconstruct restricted and scarce data into states AI can use.
We prepare data for each use case and validate it under fixed conditions.
We release the data state behind each run as a version, so results can be reproduced and revalidated.
That is how a PoC reaches a production decision.
Story
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 between enterprise data management and AI execution. Alongside it, DTS reconstructs scarce or restricted data into the state an AI task needs, and LLM Capsule lets AI work on operational data that cannot move raw, restoring results through an internal mapping kept by the customer. Each can be adopted on its own or used inside Syntitan.
At a glance
Where we are today.
2021
Founded
Seongnam, Korea · Belfast, UK
AWS · Microsoft
Marketplace partner
LLM Capsule on AWS and Microsoft Marketplace
2
Global entities
CUBIG Corp (KR) · CUBIG Ltd (UK)
Team
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,
so we know exactly where production AI fails.
CUBIG Team
AI infrastructure engineers
Practitioners who have operated AI in regulated enterprise environments: finance, healthcare, manufacturing. Every product decision comes from something we had to fix ourselves.
Research & Privacy
Data reconstruction specialists
The research team behind the DTS engine and the substitution and reconstruction layer inside LLM Capsule. Measured results, not policy promises.
Enterprise Engineering
Platform & integration
Responsible for Syntitan: Release State, Run Binding, and the integration layer that connects to existing ML pipelines, data platforms, and runtime environments.
Platform & capabilities
One platform, two core capabilities.
Syntitan is the long-term platform.
DTS and LLM Capsule can be adopted on their own,
and they also run as core capabilities inside Syntitan.
Platform
Syntitan
The AI-Ready Data Platform, in three areas. Data Readiness assesses data on six axes. Use Case Validation prepares data for a set use case and validates its fit with a chosen model or agent under the same conditions. Operational Assurance releases the data as a version, links each run to it, and revalidates after changes.
More →Capability
DTS
AI-native Data Reconstruction. Reconstructs scarce, imbalanced, or restricted data into the patterns and relationships an AI task needs, without moving the original. Adopted on its own, and used inside Syntitan and LLM Capsule.
More →Capability
LLM Capsule
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 Business-Ready Reconstruction restores results through an internal mapping kept by the customer.
More →
Customers & ecosystem
Trusted by enterprise
Trusted by enterprise
and government.
From global cloud and analyst partners
to Korean banks, insurers, public agencies, and national defense,
CUBIG operates where the data stakes are highest.
Technology & Cloud
Finance
Public sector & Defense
Enterprise & Healthcare
Values
How we work.
01
A layer, not features
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.
02
Production is the only test
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?
03
Evidence over assertion
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.
Contact
Get in touch.
Enterprise & Architecture
Book architecture review
Map your production constraints (data that is locked, damaged, or drifting) to the right path across Syntitan, DTS, and LLM Capsule.
Book architecture review →General inquiries
Press, partnerships, research
Research collaboration, press, partnership discussions, or anything not covered above.
[email protected]Korea headquarters
CUBIG Corp
4F, NAVER 1784, 95 Jeongjail-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea.
United Kingdom
CUBIG Ltd
21 Arthur Street, Belfast, Antrim, BT1 4GA, United Kingdom.
Make your AI runs
reproducible in production.
Start with the product that fits your problem:
Syntitan, DTS, or LLM Capsule.