01Before you adopt AI
AI-Ready Data Operating Layer
Make your enterprise data ready for AI.Keep it ready as things change.
Diagnose how ready your data is, refine what the task needs, and verify the fit.
All under the same conditions. When conditions change, check again.
Diagnosis is free. See sample results first, no sign-in needed.
Core readiness on six axes, before you pick an AI. A shared state score, not a pass for a specific task.
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
Starting points
Clean data and AI-Ready data are not the same thing. Start from where you stand.
AI-Ready means verified for the task, model, and conditions you chose.
Pick one of four starting points and we show you that answer first.
02When the task is set
Verify whether this data works for that specific AI task.
Start a Proof Run03When AI is already running
Check whether refining the data improves results, before you swap the model.
Start a Proof Run04When something changed
Data, model, or policy changed. Requalify only what it affected.
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
Position in the stack
Between your data and your models, Syntitan fills the layer that was missing.
Works alongside your storage, models, and tools. Nothing gets replaced.
Syntitan takes one job: make the data AI-Ready and reproduce 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 diagnosis to requalification.
- Baseline
- Qualification
- Assurance
LLM Capsule
Core capability · standaloneContext-Preserving Data Layer for AI
- Substitute
- Execute
- Reconstruct
DTS
Core capability · standaloneCore technology that rebuilds the data you need
- Refine
- Rebuild
- 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
From diagnosis to requalification, one Syntitan carries it through.
Three areas on one data version.
Start with a diagnosis alone, verify when the task is set, requalify in operation.
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01Core readiness Free · one file
Baseline
Start before you pick a task. One file, six-axis scores, and you fix only what you choose to.
One fileSix-axis scoreFix only what you pickNot a task verdict yet
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02Fit for the task Once the task is set
Qualification
Refine only what the chosen task, AI, and criteria need, then compare before and after under the same conditions.
Same AI conditionsBefore vs afterQualifiedNot QualifiedInconclusive
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03Operating evidence In operation
Assurance
Bind real runs to a data version. When conditions change, requalify only the scope they affected.
ReleaseRun BindingChange historyRequalify 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 takes sensitive data. DTS takes scarce or restricted data.
Each can be adopted on its own, 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.
Without training on the original, 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, certifications, patents, and procurement channels across finance, healthcare, and the public sector.
- Customers & partners 15+ Across finance, healthcare, public sector, legal, marketing, and cloud
- Awards & certifications 10+ 2 Ministerial Prizes · 2 GS Grade 1 · 2 ISO · KISA
- Patents 12 8 domestic (4 registered) · 1 overseas registration included
- Founded 2021 Seongnam-si, Korea · UK entity established
Deployment and procurement
Example use cases
Same data bottleneck,
Same data bottleneck,
different industries.
The same data bottleneck in nine industries. Each panel is an example under the same conditions.
Example Financial services
Fraud detection & AML
- BaselineProblemWhere 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.
- QualificationWhat we applyRefined and verified for the task
- DTS reconstructs the rare scenarios to fill the gap. Syntitan records that data as one version, in the state it verified.
- Qualification ResultResultUsable for this task, 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.
- AssuranceOperate and recordRecorded and requalified in operation
- Syntitan releases the qualified version, records which version each model run used, and requalifies the data when transaction patterns shift.
Example Healthcare
Clinical decision support
- BaselineProblemWhere the data stands today
- Patient data cannot go into an LLM as it is, and rare-disease cohorts are too small to train on.
- QualificationWhat we applyRefined and verified for the task
- LLM Capsule runs the clinical LLM on a copy with patient identifiers replaced. DTS reconstructs rare-disease data to a size the model can train on.
- Qualification ResultResultUsable for this task, or not
- Data qualified forclinical decision supportThe original patient records stay in the hospital environment. The team builds clinical insights and research models from the substituted copy.
- AssuranceOperate and recordRecorded and requalified in operation
- Syntitan fixes the substituted copy and the reconstructed cohort as one version, records it on every clinical LLM run, and requalifies it when records change.
Example Public sector
Policy sentiment & citizen services
- BaselineProblemWhere the data stands today
- Citizen records sit in separate agencies under privacy law, so LLM services cannot use those records directly.
- QualificationWhat we applyRefined and verified for the task
- DTS reconstructs agency citizen records into a form that identifies no individual, and LLM Capsule replaces identifying details in the records the citizen-service LLM uses.
- Qualification ResultResultUsable for this task, or not
- Data qualified forpolicy sentiment analysisLLM services answer shifts in policy sentiment and citizen requests. The agency can see which records each answer used and how Capsule substituted them.
- AssuranceOperate and recordRecorded and requalified in operation
- Syntitan detects changes across agency records and requalifies the data, and every run records the data version it used.
Example Telecom
Real-time inference & network operations
- BaselineProblemWhere the data stands today
- When inference results change, no one can tell which input data version or network configuration change caused it.
- QualificationWhat we applyRefined and verified for the task
- Syntitan records the data and pipeline state as versions and logs the difference when a change comes in.
- Qualification ResultResultUsable for this task, or not
- Data qualified forreal-time inferenceYou compare the changed run with the last qualified run on the same data basis, see what changed on the data side, and reuse that version.
- AssuranceOperate and recordRecorded and requalified in operation
- Syntitan binds every run to its version and requalifies the models a topology change affects.
Example Manufacturing
Quality inspection & defect detection
- BaselineProblemWhere the data stands today
- Rare defect classes are underrepresented in training data, so the model misses uncommon defects in production.
- QualificationWhat we applyRefined and verified for the task
- DTS reconstructs inspection data for the rare defect types, and Syntitan verifies the data before and after under the same conditions.
- Qualification ResultResultUsable for this task, or not
- Data qualified fordefect detectionThe inspection model trains on data that includes rare defects, with a record of which data it trained on.
- AssuranceOperate and recordRecorded and requalified in operation
- When line settings or product specs change, Syntitan detects the change and requalifies the inspection model.
Example Insurance
Claims processing
- BaselineProblemWhere the data stands today
- Claim documents carry policyholder details as they are, so they cannot go into LLM processing.
- QualificationWhat we applyRefined and verified for the task
- LLM Capsule runs the LLM on a copy with policyholder details replaced, and Business-Ready Reconstruction restores the original values through the mapping kept inside the company.
- Qualification ResultResultUsable for this task, or not
- Data qualified forclaims processingClaim summaries and rationale come back matched to the real case and flow straight into review.
- AssuranceOperate and recordRecorded and requalified in operation
- Each run records which fields Capsule replaced and which data version it used. When document formats change, Syntitan requalifies the data.
Example Retail & e-commerce
Recommendation engine
- BaselineProblemWhere the data stands today
- When recommendation quality drops, it takes days to find what changed in the data or the runtime.
- QualificationWhat we applyRefined and verified for the task
- Syntitan records the data state and the runtime details of that moment as one version, and logs which version every run used.
- Qualification ResultResultUsable for this task, 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.
- AssuranceOperate and recordRecorded and requalified in operation
- Syntitan detects catalog and log schema changes and requalifies the recommendation model.
Example Defense
LLM support in isolated networks
- BaselineProblemWhere the data stands today
- Documents cannot leave the isolated network, and stripping context makes LLM answers useless.
- QualificationWhat we applyRefined and verified for the task
- LLM Capsule builds a copy inside the isolated network with identifying details replaced. Only that copy, structure and context intact, goes to the LLM on the approved path.
- Qualification ResultResultUsable for this task, or not
- Data qualified fordocument analysisThe originals stay inside the network while analysis and Q&A results come back in the original terms.
- AssuranceOperate and recordRecorded and requalified in operation
- Capsule keeps the substitution set as a version and records it per run. When document formats or classification rules change, Syntitan requalifies it.
Example Industrial · OT/ICS
OT network data analysis
- BaselineProblemWhere the data stands today
- Raw OT data reveals plant configuration and cannot leave the site for analysis, and anomaly cases are rare.
- QualificationWhat we applyRefined and verified for the task
- DTS reconstructs the data without plant configuration, keeps the patterns anomaly detection needs, and fills in the rare anomaly cases.
- Qualification ResultResultUsable for this task, 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.
- AssuranceOperate and recordRecorded and requalified in operation
- When equipment joins or protocols change, Syntitan detects the change and requalifies the model.
Learn
Worth reading
Worth reading
before you decide.
What AI-Ready means, and where your data stands.
Try it yourself
Check now whether your data produces real results.
Whatever brought you here, it starts with a data diagnosis.
Each button opens at the point that matches why you pressed it.
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Start a free AI-Ready diagnosis
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Start a Proof Run
A sample before-and-after comparison opens first, with the verdict evidence.
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Try it on your data
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Diagnosis is free. Refinement, Proof Run, and operations are paid. Sample results open without sign-in.
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