01Before you adopt AI
See whether your data is in a state to start AI at all.
You getA six-axis readiness result and what blocks it
Start a free diagnosisAI-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.
Diagnosis is free. See sample results first, no sign-in needed.
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.
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.
02When the use case is set
Validate whether this data fits that specific use case.
You getBefore and after under the same conditions, with the verdict
Validate fit with your AI03When AI is already running
AI results falling short? Check whether preparing the data improves them.
You getA check of your model and data together
Validate fit with your AI04When something changed
After data, models, or policies change, check that it still holds.
You getVersion records and revalidation results
Try it on your dataServing 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
How it works
Hand over the data and the goal.
Hand over the data and the goal.
Get back data that passed your criteria.
Prepare data for AI, fit it to your use case, and check the result yourself. The criteria you confirm carry over to the next file and the next change.
ExampleCustomer churn prediction
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01You hand over
customers.csvCRM
customer_id plan last_login C-1042 Pro 09-28 c1042_ Pro null C-2210 Basic 10-02 C-3307 null 10-11 C-4415 Pro 10-09 4415 Pro 10-09 Use caseChurn prediction
Model and criteriaXGBoost v1.3Recall ≥ 0.82
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02Syntitan does
Six-axis assessment
- Usability
- Integrity
- Context
- Consistency
- Reproducibility
- Traceability
Prepare what the use case needs
c1042_C-1042Customer IDs linkedSame model, same evaluation set
Your criterionBefore
After
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03You get back
Prepared Data State v2Released
Qualifiedfor churn prediction
with XGBoost v1.3- Criterion
- Recall ≥ 0.82 met
- Evidence
- Before and after under the same conditions
- Changed
- Customer IDs linked, gaps filled
v1 keptv2 released
Saved for next timeUse case profile for the next file
What you stop redoingAsking the data team to clean files and waiting. Revalidating from scratch every time the data changes.
Position in the stack
Between your data
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.
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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.
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Syntitan AI-Ready Data Platform
Three areas, one flow from assessment to revalidation.
- Data Readiness
- Use Case Validation
- Operational Assurance
LLM Capsule
Core capability · standaloneContext-Preserving Data Layer for AI
- Substitute
- Execute
- Restore
DTS
Core capability · standaloneCore technology that reconstructs the data you need
- Supplement
- AI-native Data Reconstruction
- Standalone
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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,
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.
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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
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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
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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.
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,
A record of customers,
certifications, and patents.
Deployments across finance, the public sector, and defense, plus certifications, patents, and marketplace channels.
- Customers 15+ Across finance, public sector, defense, telecom and retail
- Awards & certifications 17 8 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
Actual recordInsurance
Insurance call classification
ScopeThin training data supplemented for a 64-class model
Held constantSame modelSame evaluation criteria64 classes
Weighted F158.55%88.55%
January 2025, self-measured · See the measurement conditions
Deployment and marketplaces
Example use cases
Different tasks,
Different tasks,
the same data bottleneck.
Pick the AI task your team repeats. Each panel shows where the data gets stuck and how it is resolved, as an illustrative example.
Example Financial services
Fraud detection & AML
- 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.
Example Healthcare
Clinical decision support
- Data ReadinessProblemWhere the data stands today
- Patient data cannot go into an LLM as it is, and rare-disease cohorts are too small to train on.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- LLM Capsule runs the clinical LLM on a working version that substitutes patient identifiers while keeping clinical context and structure. DTS reconstructs rare-disease data to a size the model can train on.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified forclinical decision supportThe original patient records stay in the hospital. Clinical work uses the substituted working version, and research uses the reconstructed cohort.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- Syntitan keeps the substituted working version and the reconstructed cohort together as one data version, records it on every clinical LLM run, and revalidates it when records change.
Example Public sector
Policy sentiment & citizen services
- Data ReadinessProblemWhere the data stands today
- Citizen records sit in separate agencies under privacy law, so LLM services cannot use those records directly.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- DTS reconstructs agency citizen records into a form that is not restored to individual records, and LLM Capsule substitutes identifiers in the records the citizen-service LLM uses while keeping their context.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified forpolicy sentiment analysisLLM services analyze shifts in policy sentiment and answer citizen requests. The agency can see which records each answer used and how Capsule substituted them.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- When agency records change, you review what changed and revalidate the data. Every run records the data version it used.
Example Telecom
Real-time inference & network operations
- Data ReadinessProblemWhere the data stands today
- When inference results change, no one can tell which input data version or network configuration change caused it.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- Syntitan records the data and pipeline state as versions and logs the difference when a change comes in.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified forreal-time inferenceYou compare the changed run with the last qualified run under the same evaluation conditions, see what changed on the data side, and reuse the qualified version.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- Syntitan binds every run to its version. When the topology changes, you review what changed and revalidate the affected data.
Example Manufacturing
Quality inspection & defect detection
- Data ReadinessProblemWhere the data stands today
- Rare defect classes are underrepresented in training data, so the model misses uncommon defects in production.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- DTS reconstructs inspection data for the rare defect types, and Syntitan verifies the data before and after under the same conditions.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified fordefect detectionThe inspection model trains on data that includes rare defects, with a record of which data it trained on.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- When line settings or product specs change, you review the change and revalidate the inspection data.
Example Insurance
Claims processing
- Data ReadinessProblemWhere the data stands today
- Claim documents carry policyholder identifiers as they are, so they cannot go into LLM processing.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- LLM Capsule runs the LLM on a working version that substitutes policyholder identifiers while keeping the claim context, and Business-Ready Reconstruction restores the original values through the mapping kept inside the company.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified forclaims processingClaim summaries and rationale come back matched to the real case and flow straight into review.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- Each run records which fields Capsule substituted and which data version it used. When document formats change, Syntitan revalidates the data.
Example Retail & e-commerce
Recommendation engine
- Data ReadinessProblemWhere the data stands today
- When recommendation quality drops, it takes days to find what changed in the data or the runtime.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- Syntitan records the data state and the runtime details of that moment as one version, and logs which version every run used.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified forproduct recommendationYou compare the degraded run with a qualified run on the same basis and see what changed in the data.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- When the catalog or log schema changes, you review the change and revalidate the recommendation data.
Example Defense
LLM support in isolated networks
- Data ReadinessProblemWhere the data stands today
- Documents cannot leave the isolated network, and stripping context makes LLM answers useless.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- LLM Capsule substitutes identifying details inside the isolated network to make a working version. Only that working version, with structure and context intact, goes to the LLM on the approved path.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified fordocument analysisThe originals stay inside the network while analysis and Q&A results come back in the original terms.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- Capsule keeps the substitution set as a version and records it per run. When document formats or classification rules change, Syntitan revalidates it.
Example Industrial · OT/ICS
OT network data analysis
- Data ReadinessProblemWhere the data stands today
- Raw OT data reveals plant configuration and cannot leave the site for analysis, and anomaly cases are rare.
- Use Case ValidationWhat we applyPrepared and validated for the use case
- DTS reconstructs the data without plant configuration, keeps the patterns anomaly detection needs, and fills in the rare anomaly cases.
- Review Validation ResultsVerdictUsable for this use case, or not
- Data qualified foranomaly detectionThe team trains the anomaly model on reconstructed data and verifies it under the same conditions as the site data. The original telemetry stays on site.
- Operational AssuranceOperate and recordRecorded and revalidated in operation
- When equipment joins or protocols change, you review the change and revalidate the data.
Learn
Worth reading
Worth reading
before you decide.
What AI-Ready means, and where your data stands.
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
- GlossaryAI-Ready Data
- ArticleAI Readiness Assessment for Enterprise Data: Six Axes and a Data Readiness Score
- ArticleAI-Ready Data vs Clean Data: Why Clean Isn’t Enough
- LatestClean Data, Wrong Record: What Two New AI Agent Benchmarks Show
- LatestRealistic Synthetic Scenarios Can Still Miss Critical Cases
Try it yourself
Check now whether your data
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.
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Start a free data readiness diagnosis
A sample diagnosis opens first. Sign in to run your own data free.
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Validate fit with your AI
A sample before-and-after comparison opens first, with the verdict evidence.
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Try it on your data
Already decided? Sign in to upload your data.
The assessment is free. Data preparation, fit validation, and operations are paid. Sample results open without sign-in.
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