AI-Ready Data Operating Layer

Make your enterprise data ready for AI.Verified for your AI, and kept that way.

Assess how ready your data is, prepare what the use case needs, and validate its fit for the task with your chosen model or agent.
All under the same conditions. When conditions change, revalidate.

Start a free data readiness diagnosis Validate fit with your AI

Diagnosis is free. See sample results first, no sign-in needed.

Syntitan Data Readiness: six-axis scores from the readiness assessment

Assess Data Readiness gives six-axis scores, even before you pick a use case. A shared state score, not a pass for a specific use case.

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 in the 2025 Hyper-Synthetic Data report

Starting points

Clean does not mean AI-Ready.
Start from where you stand.

AI-Ready means validated for your use case, model, and conditions.
Pick one of four starting points and see the answer you need first.

03When AI is already running

AI results falling short? Check whether preparing the data improves them.

Validate fit with your AI

04When something changed

After data, models, or policies change, check that it still holds.

Try it on your data

Serving the same AI to many customers? Reuse the shared validation procedure, and adjust it to each customer's data, business rules, and success criteria.

See how in Syntitan
Position in the stack

Between your data
and models, Syntitan fills
the missing layer.

Works alongside your storage, models, and tools. Syntitan makes the data AI-Ready, verifies it for your AI, and reproduces that state on every run.

  • Every run should be able to say which data state it ran on.

    • Fraud detection
    • Customer analytics
    • Enterprise copilots
    • AI agents
    • Risk simulation
    • Document search & summary

    Each run is bound to a specific data state and recorded, so any result can be traced back.

  • Syntitan AI-Ready Data Platform

    Three areas, one flow from assessment to revalidation.

    • Data Readiness
    • Use Case Validation
    • Operational Assurance

    LLM Capsule

    Core capability · standalone

    Context-Preserving Data Layer for AI

    • Substitute
    • Execute
    • Restore

    DTS

    Core capability · standalone

    Core technology that reconstructs the data you need

    • Supplement
    • AI-native Data Reconstruction
    • Standalone

  • Organized for management, not yet verified as ready for AI.

    • DatabasesSQL · NoSQL
    • DocumentsContracts · Internal
    • CRM & ERPSalesforce · SAP
    • Object StorageS3 · Data Lake
    • Logs & IoTSensors · Streams
    • APIs & LegacyREST · SOAP

    Every source is here and clean. That still does not make it AI-Ready.

Three areas

One Syntitan,
from assessment
to revalidation.

Syntitan is CUBIG's AI-Ready Data Platform.
Assess first, validate once the use case is set,
revalidate in operation, all on one data version.

How Syntitan works See how the readiness assessment works
  1. 01Steps 1–2 Free diagnosis · one file

    Data Readiness

    You can start even before you pick a use case. One file, six-axis scores, and you improve only what you choose to.

    One fileSix-axis scoreImprove only what you pickUse case verdict comes next

  2. 02Steps 3–6 Once the use case is set

    Use Case Validation

    Prepare only what the chosen use case, model or agent, and criteria need, then compare before and after under the same conditions.

    Same AI conditionsBefore vs afterQualifiedNot QualifiedInconclusive

  3. 03Steps 7–8 In operation

    Operational Assurance

    Release the validated data as a version and bind real runs to it. When conditions change, review what changed and revalidate only the affected scope.

    Data version releaseRun BindingChange historyRevalidate only what changed

Criteria and records carry over to the next run.Works with your platform. See how it connects

Core capabilities

Data you cannot use as it is.
Two capabilities make it usable.

LLM Capsule for sensitive data. DTS for scarce or restricted data.
Each can be adopted alone, and inside Syntitan they work together.

LLM Capsule

Context-Preserving Data Layer for AI

AI work runs even on data
that cannot leave as it is.

It goes to the AI as a working version that keeps context, structure, and relationships. Results come back as real values inside your environment.

DTS

Core technology that reconstructs the data you need

Scarce or restricted data gets rebuilt
as much as the task needs.

It reconstructs data with the patterns, distributions, and relationships the task needs. Inside LLM Capsule, it takes over the data that substitution alone cannot cover.
Proof of adoption

A record of customers,
certifications, and patents.

Deployments across finance, the public sector, and defense, plus certifications, patents, and marketplace channels.

See all Proof

  • Customers 15+ Across finance, public sector, defense, telecom and retail
  • Awards & certifications 15 6 certifications · 9 awards, incl. 3 Ministerial Prizes
  • Patents 17 5 registered · 12 pending (as of July 2026)
  • Founded 2021 Seongnam-si, Korea · UK entity established
  • Intellyx Digital Innovator Award 2026
  • NextRise Global Innovator 2024
  • Information Security Product Innovation Award 2024, DTS
  • KISA Fast Track 2024
  • GS Certified Grade 1, LLM Capsule 2024
  • GS Certified Grade 1, DTS 2025
  • Startup World Cup Finalist 2025
  • 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
  • T-Challenge 2026 Runner-up among finalists, Deutsche Telekom & T-Mobile US
Deployment and marketplaces
Example use cases

Same data bottleneck,
different industries.

The same data bottleneck in nine industries. Each panel is an illustrative example.

See all cases

ExampleFinancial services

Fraud detection & AML

AI: Fraud and AML decision model Data: Transaction and account logs

Data ReadinessProblemWhere the data stands today
Rare fraud and AML patterns barely appear in training data, so the model misses them. Later, no one can find the data behind a decision.
Use Case ValidationWhat we applyPrepared and validated for the use case
DTS reconstructs the rare scenarios to fill the gap. Syntitan records that data as one version, exactly as it was validated.
Review Validation ResultsVerdictUsable for this use case, or not
Data qualified forfraud detectionThe model decides on data that includes rare patterns, and each decision traces back to the data it ran on.
Operational AssuranceOperate and recordRecorded and revalidated in operation
Syntitan releases the qualified version and records which version each model run used. When transaction patterns change, you review what changed and revalidate the affected data.
Learn

Worth reading
before you decide.

What AI-Ready means, and where your data stands.

See the Learn Hub

Start here What Is AI-Ready Data? Definition and Why Clean Data Isn’t Enough Clean data is not AI-Ready data. This guide covers what ready means and how to see where your data stands. Read the guide
Go deeper
Try it yourself

Check now whether your data
produces real results.

Most checks start with a free data readiness assessment.
Each button opens the screen that fits your goal.

The assessment is free. Data preparation, fit validation, and operations are paid. Sample results open without sign-in.

Prefer to talk it through first? Book architecture review