01Before you adopt AI
Check if your data is ready to start with AI.
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.
Diagnose your data, prepare what the use case needs, and test the fit on your own model or agent under the same conditions.
Recheck when conditions change.
Diagnosis is free. Sign in and start with sample data or your own.
Work StudioHomeExample
Data that was hard to feed into AI, made ready to use.Start by seeing what is wrong with your data.
From preparation to results in one placeNo data expert? That’s fine.Syntitan proposes how to prepare your data first.
Examples by industryHere is what you can do with it.Pick a case close to your work and see right away how the data changes.
Prepare data for AII want to use customer order records for AI analysis, with personal details replaced.Names are hidden, but the same customer keeps the same value. Date and country formats are unified, and rare overseas orders are supplemented.
Before
▶CustomerOrder dateCountry
Kim Haneul2026/09/21Korea
Kim Haneul2026.09.24KR
Lee Doyun21-09-2026Republic of Korea
After
CustomerOrder dateCountry
Customer_A2026-09-21KR
Customer_A2026-09-24KR
Customer_B2026-09-21KR
Ready to use with AI
What was preparedThe original stays as is
Privacy protectionFormat alignmentCase supplementationReadiness
Check how well AI predictsI want to know which customers will churn next month.Align fee formats and fill in missing plans, then compare how many more churning customers are found under the same conditions.
Before
▶CustomerPlanMonthly fee
C-01Premium55,000 KRW
C-0255000
C-03basic33000 KRW
After
CustomerPlanMonthly fee
C-01Premium55,000
C-02Premium55,000
C-03Basic33,000
Check how well AI predicts
What to predict
ChurnChurning customers foundout of 200 actual
Prepare data for AII want to prepare support records for an AI chatbot.Customers are grouped and masked even when contact formats differ. Duplicate records are merged so the same content is not learned twice.
Before
▶CustomerContactInquiry
Hong Gildong010-1234-5678Refund
Hong Gildong010-1234-5678Refund
Hong Gildong01012345678Late delivery
After
CustomerContactInquiry
Customer_AContact_ARefund
1 duplicate inquiry merged
Customer_AContact_ALate delivery
Ready to use with AI
What was preparedThe original stays as is
Privacy protectionFormat alignmentDuplicate cleanupReadiness
Check how well AI predictsI want to pick out products likely to be defective in advance.Unify temperature units and exclude sensor errors, then compare how many more defects are found under the same conditions.
Before
▶ProductTemperaturePressure
P-0785℃1.2 bar
P-0899991.3
P-09185°F
After
ProductTemperature (℃)Pressure (bar)
P-0785.01.2
P-081.3
Sensor error excluded
P-0985.01.25 (avg)
Check how well AI predicts
What to predict
DefectDefective products foundout of 60 actual
Prepare data for AII want to align scattered sales records before analyzing revenue.Date formats and currency units are unified so revenue can be combined on one basis. Missing amounts are flagged, not filled in arbitrarily.
Before
▶OrderDateAmount
O-112026.9.348,000 KRW
O-1209/03/26
O-132026-09-03$36
After
OrderDateAmount
O-112026-09-0348,000 KRW
O-122026-09-03
Needs review
O-132026-09-0336 USD
Ready to use with AI
What was preparedThe original stays as is
Date alignmentCurrency alignmentReadiness
Check how well AI predictsI want to know which loans are likely to become delinquent.Unify income and amount units in KRW and fill in blanks, then compare how many more delinquent loans are found under the same conditions.
Before
▶LoanMonthly incomeLoan amount
L-113M30,000,000 KRW
L-12300000025M
L-1318000000
After
LoanMonthly incomeLoan amount
L-113,000,00030,000,000
L-123,000,00025,000,000
L-132,800,000 (avg)18,000,000
Check how well AI predicts
What to predict
DelinquencyDelinquent loans foundout of 120 actual
Usage
Year‹2026›Recent projects
Recent agent reports
Subscription intent survey (ages 20–39)Persona surveyCUBIG Data.csv(v1) +2 moreCompletedSSam ParkAug 7
High churn-risk customersChurn predictioncustomer_activity_2025.csv(v2)CompletedAAlex KimOct 11
Win-back campaignCRM campaigncustomer_activity_2025.csv(v1) +1 moreFailedSSora LeeOct 6
Launch price rangeNew product pricingtv_sales_2025.csv(v2)CompletedDDaniel ChoiOct 2
Six axes show what to fix. DTS proposes the handling, you decide what to apply, and each successful run saves a new version.
Gartner® “Deploy Domain-Specific Agentic AI to Lower AI Costs and Drive Innovation” (2026)
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 a starting point and get the answer you need first.
02When a model task is set
Check whether this data fits that model task.
You getBefore and after under the same conditions, against your criteria
Validate fit with your AI03When an agent handles the work
Find the situations your agent misses before your users do.
You getTest cases and the situations that were missed
Try it on your data04When something changed
Find what to recheck after data, models, or policies change.
You getRun records tied to data versions (rechecks after changes coming soon)
Try it on your dataServing one AI to many customers? Reuse the shared checks and adjust them to each customer's data, 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 it back checked against your criteria.
Prepare the data, fit it to your use case, and check the result yourself. Your criteria carry over to the next file (reuse after changes is coming soon).
ExampleCustomer churn prediction
-
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
-
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
-
03You get back
Version 2New version
Criteria metfor 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 startv2 new
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 with the storage, models, and tools you already use. Syntitan prepares and checks the data for your AI, and records the data state each run used.
-
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 paths. Start with the question you need answered now.
- Ready for Raw Data
- Ready for Model
- Ready for Agent
LLM Capsule
Core capability · standaloneContext-Preserving Data Layer for AI
- Substitute
- Execute
- Recover results
DTS
Core capability · standaloneCore technology that prepares the data your AI task needs
- Supplement
- Data Enablement for AI
- 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 ways to start
Start from
Start from
the question
you need to answer now.
Syntitan is CUBIG's AI-ready data platform.
Every path keeps the original dataset and saves each result as a version.
-
01Start from the data Free diagnosis · one file
Ready for Raw Data
Diagnose six axes, review the handling DTS proposes, and apply only what you choose. Each successful run saves a new version.
Six-axis diagnosisApply · Hold · ExcludeNew versionExport as file or MCP
-
02When a model task is set Your model · agreed criteria
Ready for Model
Fix the data and evaluation conditions for one model task. Compare before and after preparation against your agreed criteria.
Same evaluation conditionsBefore vs. afterAgreed criteriaNot a model certification
-
03When an agent handles the work Run connection coming soon
Ready for Agent
Turn work materials into tasks and test cases and find the situations your agent misses. Connecting live runs, fixing, and rechecking are coming soon.
Tasks and casesMissed situationsSame criteriaRun connection: coming soon
When data or conditions change, reuse what still holds and recheck only what changed (coming soon). 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
Run AI work on data
that cannot leave as it is.
The AI gets a substituted working version with context, structure, and relationships intact. Inside your environment, the result gets its original values restored.
DTS
Data Enablement for AI
Fill missing cases, adjust skewed data,
and prepare it for the task.
The prepared data keeps the patterns and relationships the task needs. Inside LLM Capsule, DTS handles what 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 18 9 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 an AI task your team repeats. See where the data gets stuck and how it is resolved (example).
Example Financial services
Fraud detection & AML
- DiagnoseProblemWhere 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.
- Prepare and compareWhat we applyPrepared and validated for the use case
- DTS fills the gap with the rare scenarios the task needs. Syntitan records that data as one version, exactly as it was validated.
- Check the resultVerdictUsable for this use case, or not
- Criteria met forfraud detectionThe model decides on data that includes rare patterns, and each decision traces back to the data it ran on.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- Syntitan saves the version that met the criteria and records which version each run used. When transaction patterns change, review the change and revalidate the affected data.
Example Healthcare
Clinical decision support
- DiagnoseProblemWhere the data stands today
- Patient data cannot go into an LLM as it is, and rare-disease cohorts are too small to train on.
- Prepare and compareWhat we applyPrepared and validated for the use case
- LLM Capsule substitutes patient identifiers, keeps clinical context and structure, and runs the clinical LLM on that working version. DTS fills out rare-disease data to a trainable size.
- Check the resultVerdictUsable for this use case, or not
- Criteria met forclinical decision supportThe original patient records stay in the hospital. Clinical work uses the substituted working version, and research uses the prepared cohort.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- Syntitan keeps the working version and the prepared cohort as one data version, records it on every run, and revalidates it when records change.
Example Public sector
Policy sentiment & citizen services
- DiagnoseProblemWhere the data stands today
- Citizen records sit in separate agencies under privacy law, so LLM services cannot use those records directly.
- Prepare and compareWhat 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.
- Check the resultVerdictUsable for this use case, or not
- Criteria met 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.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- Every run records the data version it used. When agency records change, review the change and revalidate.
Example Telecom
Real-time inference & network operations
- DiagnoseProblemWhere the data stands today
- When inference results change, no one can tell which input data version or network configuration change caused it.
- Prepare and compareWhat 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.
- Check the resultVerdictUsable for this use case, or not
- Criteria met forreal-time inferenceCompare the changed run with the last run that met the criteria, under the same evaluation conditions. See what changed in the data and reuse the version that met the criteria.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- Syntitan records which version each run used. When the topology changes, review the change and revalidate the affected data.
Example Manufacturing
Quality inspection & defect detection
- DiagnoseProblemWhere the data stands today
- Rare defect classes are underrepresented in training data, so the model misses uncommon defects in production.
- Prepare and compareWhat we applyPrepared and validated for the use case
- DTS fills in inspection data for the rare defect types, and Syntitan verifies the data before and after under the same conditions.
- Check the resultVerdictUsable for this use case, or not
- Criteria met fordefect detectionThe inspection model trains on data that includes rare defects, with a record of which data it trained on.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- When line settings or product specs change, you review the change and revalidate the inspection data.
Example Insurance
Claims processing
- DiagnoseProblemWhere the data stands today
- Claim documents carry policyholder identifiers as they are, so they cannot go into LLM processing.
- Prepare and compareWhat we applyPrepared and validated for the use case
- LLM Capsule substitutes policyholder identifiers, keeps the claim context, and runs the LLM on that working version. It then restores the original values inside the company.
- Check the resultVerdictUsable for this use case, or not
- Criteria met forclaims processingClaim summaries and rationale come back matched to the real case and flow straight into review.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- 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
- DiagnoseProblemWhere the data stands today
- When recommendation quality drops, it takes days to find what changed in the data or the runtime.
- Prepare and compareWhat 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.
- Check the resultVerdictUsable for this use case, or not
- Criteria met forproduct recommendationYou compare the degraded run with a run that met the criteria on the same basis and see what changed in the data.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- When the catalog or log schema changes, you review the change and revalidate the recommendation data.
Example Defense
LLM support in isolated networks
- DiagnoseProblemWhere the data stands today
- Documents cannot leave the isolated network, and stripping context makes LLM answers useless.
- Prepare and compareWhat 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.
- Check the resultVerdictUsable for this use case, or not
- Criteria met fordocument analysisThe originals stay inside the network while analysis and Q&A results come back in the original terms.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- 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
- DiagnoseProblemWhere the data stands today
- Raw OT data reveals plant configuration and cannot leave the site for analysis, and anomaly cases are rare.
- Prepare and compareWhat 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.
- Check the resultVerdictUsable for this use case, or not
- Criteria met 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.
- Track and reuseOperate and recordRecorded per run · recheck after changes coming soon
- 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.
Try it yourself
See whether your data
See whether your data
fits your AI task.
Most teams start with the free diagnosis.
Each button opens the screen for that goal.
-
Start a free diagnosis
Sign in and diagnose your own data for free, or try the sample data first.
-
Validate fit with your AI
Sign in to compare before and after on sample data or your own, with the evidence.
-
Try it on your data
Already decided? Sign in to upload your data.
The diagnosis is free. Preparation, fit validation, and operations are paid.
Prefer to talk it through first? Contact us