Enterprise AI fails on data, not models.AI-ready isn't a project. It's a product.

Syntitan fills the missing layer between enterprise data management and real AI execution.
Sign in and run it on your own data.

Explore Syntitan Run a sample proof

Free trial on sign-up. No sales call, no PoC.

Syntitan readiness diagnosis: six-axis data readiness scores

See how ready your data is, scored on six checks.

AI-ready data is usable, reliable, and stable in production. CUBIG gets enterprise data there. Past blocked approvals, past messy records, past results that change every run.

Gartner® “Emerging Tech: AI Vendor Race: Tech Innovators in Agentic AI — Solution Accelerators” (2026)

Gartner® “Emerging Tech: AI Vendor Race: Most Prominent Use Cases in Agentic AI by Industry” (2026)

Gartner® “Emerging Tech: Provider Differentiation Strategy—Trends for Hyper-Synthetic Data” (2025)

Named a Representative Vendor

Problem

AI is stuck between data management and AI execution.

Storage works. Models work. It's the data in between that stalls projects.

Restricted

Data your teams can't touch.

Approvals take months while workflows stay blocked.

Unusable

Records too messy or thin to train on.

Missing values and imbalance stop the model before it starts.

Unstable

States that shift between PoC and production.

Last month's working run can't be reproduced today.

30% of GenAI projects are abandoned after PoC. Only 4% of IT leaders call their data AI-ready. — Gartner (2024, 2025)

Missing Layer

Your systems manage data. Syntitan makes it run for AI.

It sits on top of Snowflake, Databricks, and Fabric, and fills the layer they leave open. Nothing gets replaced.

Where to start

Start with what's blocking you today.

Three walls teams hit between data and AI. Chances are, yours is one of them.

Syntitan AI recommendation for row augmentation on an imbalanced dataset

Gaps and errors in your data blocking AI?

From imbalance correction to synthetic augmentation, rebuild a complete AI-ready dataset.

Response rate charts from synthetic persona research in Syntitan

Want to test customer and market responses first?

Simulate synthetic personas instead of recruiting panels. Research in hours, not weeks.

Transformed dataset view with original values kept inside the environment

Need to use data without exposing the originals?

Original values stay inside. Usable results come back.

All of it happens in one place Syntitan.

Two Entry Paths

Start with data, or start with a workflow.

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

One platform. Two entry paths.

Path A · AI-ready data

For data leaders, ML teams, and AI platform owners

Make your enterprise data ready for AI, and keep it that way

For data that's locked, scarce, or unstable on AI. Rebuilt so your models can run on it.

Path B · Sensitive AI workflow

For AI adoption leads and workflow owners

Run LLM, RAG, and agent workflows on sensitive data

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

Platform

One platform. Five capabilities.

Both entry paths run on the same platform: Syntitan.

Platform

Syntitan

The AI-Ready Data Platform. Gets your data ready for AI on six measurable checks, then proves the improvement on your own workflow.

Explore Syntitan
The same six steps you'll see in the product
1
Diagnose AI readiness diagnosis on six axes
2
Refine Fix data values and context
3
Optimize Tune for your metric and model
4
Release Freeze a versioned data state
5
Proof Run Before/after on a baseline model
6
Verify Re-run in your environment
Six axes Usability Integrity Context Consistency Reproducibility Traceability
Capability

DTS

AI-ready data transformation engine. Rebuilds restricted, thin, and imbalanced data into datasets your models can learn from.

Explore DTS
Capability

LLM Capsule

Context-Preserving Data Layer for AI. Run LLM and agent workflows while original values stay inside your environment.

Explore LLM Capsule
Validation Quality and usability checks before AI runs
Operating Control Release State · Run Binding · Diff · Reproduce
Agent Connection Connects agents and workflows to enterprise systems
Use cases

Same blocker. Different industries.
One platform.

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 enterprise. Proven in production.

Audit trail, data lineage, cloud or on-prem deployment. And you can try all of it without a sales call.

More

Key Numbers

Customers & partners
15+

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

Awards & certifications
10+

4 Ministerial Prizes · GS · KISA

Patents
12

8 domestic (4 registered) · 4 overseas (1 allowed)

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.

Learn

Understand AI-ready data before you buy anything.

Definitions, comparisons, and assessments, written to be useful even if you never sign up.

What Is AI-Ready Data? Start here

What Is AI-Ready Data? (And Why Clean Data Isn’t Enough)

Clean data isn't AI-ready data. The pillar guide to what "ready" actually means, and how to tell where your data stands.

Read the guide →
Go deeper GLOSSARY AI-Ready Data ARTICLE AI Readiness Assessment: The Six Readiness Axes ARTICLE AI-Ready Data vs Clean Data: Why Clean Isn’t Enough LATEST The Real Bottleneck in Enterprise AI Adoption Is Not What the 95% Report Says LATEST OpenAI Just Made AI Delivery a Volume Business LATEST The $1.5 Trillion Problem: Your Data Is Clean. Your AI Still Can’t Use It. Explore the Learn Hub

See the difference on your own data.

Explore Syntitan Run a sample proof

Free trial on sign-up. No sales call, no PoC.

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