Enterprise AI fails on data,
not models.

CUBIG helps regulated enterprises turn sensitive, fragmented and hard-to-use data into AI-ready operating states for models, LLMs and agentic workflows.

Talk to Architect Explore Syntitan

AI-ready data is usable, reliable, and stable in production. CUBIG builds the operating layer that gets enterprise data there. Past restriction, past quality gaps, past execution drift.

Problem

AI is stuck between data management and AI execution.

The model is not the bottleneck. The data state is. Storage and pipelines move data; they don't make it AI-ready for models, LLMs, and agentic workflows.

Enterprise AI Is Still Failing

Gartner, Feb 2025

60%

of AI projects will fail by 2026 without AI-optimized data infrastructure

Gartner, Jul 2025

30%

of GenAI projects abandoned after PoC, before reaching production

S&P Global, 2025

42%

of US enterprises halted most AI initiatives (up from 17% the prior year)

Barriers to Reliable AI

  • Restricted Data

    Sensitive or regulated data can't be used safely with AI. Privacy rules, access controls, and compliance requirements block it from reaching models.

  • Unusable Data

    Data exists, but it's not usable. Missing values, bias, coverage gaps, imbalance, or restricted access make it unfit for AI training and validation.

  • Unstable Execution

    Data and execution conditions change after deployment. Schema shifts, pipeline updates, runtime variance mean results can't be reproduced.

Missing Layer

Existing platforms manage data. CUBIG makes it AI-ready.

CUBIG sits between your data platform and your AI layer.
Not a replacement, but what makes the rest work for AI.

Two Entry Paths

AI-ready data path + sensitive AI workflow enablement path

Whether your blocker is data or workflow, start with the path that fits your team.

One operating layer. Two entry paths.

Path A · AI-ready data

For data leaders, ML teams, AI platform owners (CISO / DPO sign-off)

Turn enterprise data into AI-ready operating states

For data that's locked, scarce, or unstable on AI. Rebuilt into AI-ready operating states.

Path B · Sensitive AI workflow

For AI adoption leads and workflow owners (CISO / DPO sign-off)

Run LLM, RAG, and agent workflows on sensitive data

For workflows where sensitive data blocks LLM execution. Enabled without exposing raw data.

Platform

Syntitan, the AI-ready operating layer for enterprise data

This is the layer your data platform and AI stack are missing. Enterprise data becomes transformed, validated, and bound to every AI run.

More

That was one step. Syntitan operates the full layer, through release.

  1. Diagnose

    See readiness gaps.

  2. Enhance

    Improve data and context.

  3. Prepare

    Route restricted data through a protected path.

  4. Release

    Freeze the AI-ready state.

  5. Bind

    Connect every run to that state.

  6. Trace

    Compare, reproduce, review.

  7. Activate

    Run agents on the prepared state.

Capabilities

One AI-ready operating layer.
Five capabilities.

DTS, LLM Capsule, validation, operating control, agent connection. Five capabilities that close every structural gap between enterprise data and real AI execution.

DTS

AI-ready Data Transformation Engine

Fix unusable or restricted datasets.

Rare classes, privacy rules, and access limits leave data unusable. DTS rebuilds it into AI-ready form while preserving original structure and statistics.

More AWS Marketplace NCP Marketplace

LLM Capsule

Context-preserving data layer for AI

Run LLMs on data that can't pass raw to the model.

Sensitive fields block adoption at the data access layer. Structure-preserving substitution keeps the context LLMs need, and raw values never leave your environment.

More AWS Marketplace

Validation

Quantify how well transformed data preserves original structure, statistics, and bias before it ever reaches a model. Quality and usability are certified, not assumed.

Operating control

Run Binding, Release State, Diff, and Reproduce hold AI execution to a stable data state. Every run stays traceable, reproducible, and audit-ready.

Agent connection

Connect AI-ready data and workflows to enterprise systems and agents, so production AI runs inside the tooling your team already uses, not beside it.

Use cases

Same blocker. Different industries.
One operating layer.

Financial services, healthcare, public sector, telecom/NOC, manufacturing/OT. The data state blocking production AI looks the same everywhere. CUBIG removes it.

FINANCIAL SERVICES

Fraud Detection & AML Analytics

“Improved anomaly detection reliability and audit-traceable model runs across rare fraud and AML patterns.”

OUTCOME
THE BLOCKER
  • Rare fraud and AML patterns are underrepresented in training data.
  • Compliance audits cannot trace which data version produced which decision.
EXAMPLE DATASET
  • transaction_id
  • account_id
  • amount
  • merchant_id
  • mcc_code
  • timestamp
  • location
  • is_fraud

HEALTHCARE

Clinical Decision Support & Research

“Clinical insights and research models generated without exposing PHI, even for rare disease cohorts.”

OUTCOME
THE BLOCKER
  • PHI restrictions prevent patient data from reaching modern LLM and ML pipelines.
  • Rare disease cohorts are too small for reliable model training.
EXAMPLE DATASET
  • patient_id
  • encounter_id
  • diagnosis_code
  • lab_result
  • medication
  • timestamp
  • age_group
  • region

PUBLIC SECTOR

Policy Sentiment & Citizen Services

“Early detection of policy sentiment shifts and faster citizen-service responses, under Korea's AI-ready public data guidelines.”

OUTCOME
THE BLOCKER
  • Citizen records and policy data are siloed across agencies and regulated by privacy law.
  • LLM-based services cannot consume raw policy data directly.
EXAMPLE DATASET
  • case_id
  • agency
  • topic
  • sentiment_score
  • region
  • citizen_age_band
  • timestamp
  • resolution_status

TELECOM / NOC

Network Anomaly Detection & NOC Automation

“Stable anomaly detection through pipeline updates, with subscriber data never leaving the operator's environment.”

OUTCOME
THE BLOCKER
  • Subscriber PII and network topology can't be moved to external AI environments.
  • Rare network anomalies are sparse in training data and drift after pipeline updates.
EXAMPLE DATASET
  • subscriber_id
  • cell_id
  • traffic_volume
  • packet_loss
  • latency_ms
  • timestamp
  • region
  • alert_level

MANUFACTURING / OT

Predictive Maintenance & Quality Inspection

“Higher predictive maintenance accuracy and shorter downtime, without exposing process IP or breaking OT isolation.”

OUTCOME
THE BLOCKER
  • Process IP and OT telemetry cannot leave the plant for cloud AI training.
  • Defect cases are rare, making quality-inspection models unreliable.
EXAMPLE DATASET
  • machine_id
  • sensor_type
  • vibration_rms
  • temp_c
  • pressure_bar
  • timestamp
  • defect_label
  • line_id

DEFENSE

Defense AI Operations & Threat Analytics

“AI-assisted operational analysis under air-gapped constraints, without weakening classification or network isolation.”

OUTCOME
THE BLOCKER
  • Operational data is classified and cannot leave air-gapped environments.
  • Threat scenarios are rare and AI models can't be trained on enough variation.
EXAMPLE DATASET
  • mission_id
  • asset_type
  • region_code
  • threat_level
  • sensor_feed
  • timestamp
  • classification_tier
  • response_action
Proof

Built for production. Designed for enterprise constraints.

We're not a PoC vendor. We're the AI-ready data operating layer enterprises were missing.

Key Numbers

Customers & partners
15+

Across finance, healthcare, public sector, legal, marketing, and cloud

Awards & certifications
10+

4 Ministerial Prizes · GS · KISA

Patents
10

8 domestic (3 registered) · 2 overseas

Founded
2021

Seongnam-si, Korea · UK entity established

Certifications & Recognition

  • Intellyx Digital Innovator Award 2026
  • NextRise Global Innovator 2024
  • Information Security Innovation Award 2024
  • KISA Fast Track 2024
  • GS Certified Grade 1, LLM Capsule 2024
  • GS Certified Grade 1, CUBIG 2025
  • Startup World Cup Finalist 2024
  • ISO/IEC 27001:2022 Information Security
  • ISO/IEC 42001:2023 AI Management
  • Emerging AI+X Top 100 2026 (AIIA)
  • AI Medical Innovation Award, AI EXPO KOREA 2025
  • Deutsche Telekom T Challenge 2026 Finalist

  • Data Safety Controls

    Access control, audit logging, and separation of duties built into the operational workflow.

  • Audit & Traceability

    Run Binding, Release State, and Diff give full traceability of data lineage, transformations, and AI execution states.

  • Compliance-Ready

    Designed to operate within regulated industries. Enterprise-grade controls applied throughout.

  • Enterprise Procurement

    Available via enterprise marketplace channels with procurement support from first contact.

  • Deployment Options

    On-premises, cloud, or marketplace deployment. Flexible to fit your existing infrastructure and security posture.

  • Policy-based data boundary control

    Policy-based handling of raw data boundaries and data minimization across all workflows.

Next Step

AI only runs on data that's ready.

Turn restricted, unstable enterprise data into states your AI can actually run on.

Talk to Architect Explore Syntitan