Syntitan · AI-Ready Data Platform

Syntitan prepares enterprise data for AI, verifies it, and tracks every run.

Start with a readiness diagnosis. Then prove it under the same conditions, comparing data before and after and binding every run to its version.

Start a free AI-Ready diagnosis See how it works

Upload one file to start. Diagnosis is free.

01 / 08Core Readiness
Check your data's core AI readiness first.Diagnose the current data's Core Readiness across six axes, independent of any specific model or agent.
Overview

Existing data, prepared for your AI and verified into operations.

Not sure which AI yet? Start with the basic diagnosis. Already chose one? Start by qualifying your data for that task.

Baseline · core readiness

One file for a six-axis diagnosis, then improve only what you need. The score shows the basic state of your data. Whether it fits a specific AI is checked in the steps below.

Qualification · target fit
  1. 01Set the targetDecide which task, which AI, what counts as success, and which cases to evaluate on.
  2. 02Prepare the dataRefine only the data that affects that task’s result, keeping relationships and context intact.
  3. 03Compare under the same conditionsKeep the AI and run conditions fixed, and compare results on the data before and after refinement.
  4. 04Check against the criteriaJudge whether the criteria from step 01 were met, with results and evidence. Qualified, not qualified, or inconclusive.
Assurance · operating evidence
  1. 05Record and requalifyLog the data and conditions each run used, and recheck the verdict when they change.Changed? Back to 01 and qualify again
  • Not a cleanup or preprocessing tool.It verifies whether tidy data actually fits a specific AI task.
  • It doesn’t replace your storage or models.Your data platform and models stay where they are. Syntitan fills only the gap between them.
  • Not a model evaluator.The model stays fixed. Only the data changes.
  • One check is never the end.When data, model, or policy changes, only the affected scope is requalified.
Product areas

Syntitan works in three areas.

AI Readiness six-axis scores before and after refinementExample screen
Usability
Can sensitive data be used with AI?
Integrity
Are gaps, duplicates, and skew visible?
Context
Does the AI know what each field means?
Consistency
Is it free of duplicates, noise, and mixed formats?
Reproducibility
Can this data state be reused?
Traceability
Can changes, versions, and authors be tracked?
Basic improvement · optional

Improve only the common issues the diagnosis surfaced. Review AI-suggested items such as missing values or inconsistent categories, turn them on or off, and the results and change history stay as metadata.

The score shows the basic state of your data, not a success probability for a specific AI or a pass verdict.

The first diagnosis is free inside Syntitan.

Start a free AI-Ready diagnosis
Steps

Start with Baseline. Qualification and Assurance each take four steps.

Baseline Core readiness

  1. Upload your dataStart with one data file
  2. Six-axis diagnosis Overall scoreUsabilityIntegrityContextConsistencyReproducibilityTraceability
  3. Improve Baseline optionalFix only the common issues found

The score shows the basic state of your data. Whether it fits a specific AI is checked in Qualification below.

Qualification Target fit

  1. Define TargetTask · AI version · criteria · evaluation cases
  2. Target RefinementPreparation that keeps relations and context
  3. Proof RunAI conditions fixed, only the data differs
  4. Qualification ResultQualifiedNot QualifiedInconclusive

Assurance Operating evidence

  1. Release / Release StateEvidence of the version and its conditions
  2. Run BindingEach run linked to its version and conditions
  3. Change Event / HistoryWhat changed · Diff compares two states
  4. RequalificationRe-run the same Proof Run on the affected scope · Reproduce recalls that state

Three ways to run a Proof Run.

Start a Proof Run
Preview with built-in modelsNo model of your own yet? Syntitan’s built-in models compare before and after right away. Figures are marked as estimates.
Compare under your AI conditionsSet your task metric, evaluation set, and model version as the target, then compare data before and after under those conditions.
Re-run in your own environmentExport before/after data, change manifest, and harness. Re-run with the same model, seed, and split.
Refinement engine

Only the refinement your target needs, in six methods.

We refine only the data that affects your target, keeping relationships and context intact. When data is scarce or cannot be used as is, Syntitan’s DTS and LLM Capsule take over.

Feature derivation & augmentation

Creates new columns through binning and categorization, and adds composite signals via cross-column operations.

LLM Capsule

Sensitive data detection & substitution

Swaps sensitive values for context-preserving substitutes the AI can use, then maps results back to the real values inside your environment.

Missing value treatment

Preserves meaningful missingness patterns as signal and fills the remaining gaps with statistical methods.

Outlier, distribution & category refinement

Detects outliers and corrects distribution and category skew so models train reliably.

DTS

Data augmentation & class balancing

Generates extra samples for minority classes and rebalances class ratios to a normal range.

Low-signal column removal

Selectively removes low-importance columns and those that could contaminate predictions.

Dataset Combine

Scattered data into one dataset

Syntitan works out how to merge your datasets for you.

Same-shape data gets stacked, and different data gets linked on a shared key.
The result becomes a new dataset, and the originals stay untouched.

* Transformed datasets are excluded.

Screen preview

Walk through the Syntitan screens, step by step.

Diagnosis scores, refinement steps, before-and-after comparison, release history.
See where each piece of evidence lives, on the real screens.

01 / 08Core ReadinessBaseline
Core Readiness

Diagnose whether your data is ready for AI across six axes, and pinpoint what to fix first.

Baseline score · core readiness 75%Caution
Usability
90%
Integrity
60%
Context
65%
Consistency
55%
Reproducibility
90%
Traceability
90%
AI analysis results

Core readiness is partial. Consistency (55%), Integrity (60%), and Context (65%) need attention first. The other three axes are within range. This score is not a success probability for any specific AI.

Usability Traceability Reproducibility Consistency Context Integrity

This diagnosis is free in Syntitan, from upload to the 6-axis score.

Start a free AI-Ready diagnosis
Proof Run

Same model and conditions. Only the data switches between before and after refinement. Figures are a representative example.

Evaluation task
Binary classificationtarget churnedAnomaly detectionComing soonRegressionComing soonMulti-classComing soon
Your Baseline score and data do not change.
Same model, same conditions. Only the data changed, and so did the results.Before and after values for the key metrics are below. Figures are a representative example. Example verdict · Criteria met (Recall ≥ 0.82, F1 ≥ 0.80) · Data qualified for customer churn prediction
ModelXGBoostone of 5 built-in models
F1
0.750.82▲ 0.07
Recall
0.710.84▲ 0.13
Precision
0.790.80▲ 0.01
Performance comparisonBeforeAfter
1.00.80.60.40.20
0.750.82
F1
0.710.84
Recall
0.790.80
Precision
Conditions · example
Datasetcustomer_retention.csv · 8,208 rows · 24 columns
PurposeCustomer churn prediction · binary classification
Targetchurned · positive = Yes · minority 21%
ModelXGBoost · split and seed fixed
Applied refinements3 sensitive columns handled312 duplicate rows removedSchema context standardizedClass balance 1,742 → 2,610 rows (+50%)

The test sample is 1,642 rows. At this size the 95% confidence interval for accuracy is about ±2.4 pts, so smaller changes cannot be separated from measurement noise.

Compare before and after on your own data under the same conditions.

Start a Proof Run
Define Target

Fix the conditions to check against (task, model, evaluation, success criteria) as one set.

Customer RetentionSave Target Profile
Fixed for Proof Run
Task typeBinary classificationPositive: Churnedchurn_flag
Model / AgentXGBoost · v1.3ClassificationVersion locked
Evaluation SetChurn Golden Set · v41,642 casesLabeled
Execution EnvironmentProduction-like · Pipeline v2Seed fixedSame split
Checked at Result
Success CriteriaRecall ≥ 0.82 · F1 ≥ 0.802 conditionsAll required
Policy / ApproverPolicy Set v2 · Data OwnerPII excludedApproved

Once the criteria are set, refinement and the Proof Run follow this Target.

Try it on your data
Improve Baseline Optional

Select the issues you want to refine. Improving every issue is not required. This step is optional.

From your Baseline Consistency 55Integrity 60Context 65These axes need attention first
MetadataDuring refinement, metadata required for AI use, such as data quality status, processing results, and change history, is added automatically.
AI Recommendation
Category inconsistencyConsistency
7 columns detected
How it works
Normalize categories
Merges variant spellings and casing into a single canonical form.
MetadataCategory variantsConsistency score
AI Recommendation
Missing valuesIntegrity
1,284 cells detected
How it works
Context-aware imputation
Preserves meaningful missing patterns as signals and imputes the remaining gaps.
MetadataOverall null ratioCompleteness score
Review recommended
Context loss riskContext
3 fields detected
Why it is off by default
Preserve signal
Refining these fields may remove context the model relies on. Review before turning it on.
MetadataSignal contributionField dependency

Improving Baseline is optional. If a Target is already defined, you can go straight to Qualification.

Pick only the common issues you need from what the diagnosis found.

Try it on your data
Target Refinement

Data is prioritized by how much it affects the result. Not every low item is fixed.

Customer RetentionBinary classification · XGBoost v1.3 · Recall ≥ 0.82 · F1 ≥ 0.80
Target-relevant issuesAFFECTS SUCCESS CRITERIA · WILL BE PREPARED
Rare churn cases underrepresentedAffects Recall
Tenure context partially missingAffects Recall · F1
Category inconsistency (2 high-impact features)Affects F1
Not prioritizedNO EFFECT ON THIS TARGET
Low-impact formatting issueDoes not affect target metrics
Unused metadata completenessField not used in this task
Not every low Baseline item needs to be fixed. Skipped items are recorded with their reason.
Resulting data state
Original Data StateTarget-specific InterventionRefined Data State v2

After refining only the prioritized issues, the Proof Run is prepared under the same Target.

Try it on your data
Release

Create an operational reference from a qualified data state. Creating a version is separate from operational approval.

Customer RetentionBinary classification · XGBoost v1.3 · Qualification Result #PR-1842
Qualified Data StateCustomer Retention · REFINED DATA STATE v2
Qualification: QualifiedRecall 0.84 · F1 0.82 · Precision 0.80
Release ConditionsLocked with this release
  • XGBoost v1.3
  • Churn Golden Set v4
  • Policy Set v2
  • Production-like Environment

A Release connects a qualified data state with the conditions it was qualified under. Approval is still required before operational use.

Where this Release goes
Qualified Data State v2Release v1Run Binding

The version you confirm becomes the reference for run records and requalification. Operational use comes after approval.

Try it on your data
Run & Requalify

Track what is actually running, and requalify when conditions change. Figures are examples.

Customer RetentionBinary classification · Release v1 · Production Run #1842
A condition changed after this Release was qualifiedPolicy Set v2 → v3 · detected Sep 3, 2026. The previous result no longer covers the current setup.
In operation nowRun Binding · PRODUCTION RUN #1842
Data state
Refined Data State v2
Model
XGBoost v1.3
Policy
Policy Set v3
Release
Release v1
In operation since Aug 21, 2026
What was qualifiedRelease State · Proof Run #PR-1842
Data state
Refined Data State v2
Model
XGBoost v1.3
Policy
Policy Set v2
Result
Recall 0.84 · F1 0.82
Qualified under Policy Set v2

Requalification re-runs only what the change affects. A changed condition does not invalidate the data. It invalidates the previous result.

How this Release has moved
Release v1Run Binding #1842Policy change · v2 to v3Requalification

Bind actual runs to the Release, and when conditions change, requalify only the affected scope.

Try it on your data
Qualification Result

Judge whether the data met the defined Target criteria. Improvement and meeting the criteria are kept separate, with the Proof Run evidence.

Customer RetentionBinary classification · XGBoost v1.3 · Recall ≥ 0.82 · F1 ≥ 0.80
Data Qualifiedfor Binary classification with XGBoost v1.3
MetricOriginalRefinedSuccess criteriaResult
F10.750.82≥ 0.80Pass
Recall0.710.84≥ 0.82Pass
Precision0.790.80No degradationPass
EvidenceRepeated-run mean · variance · failure cases · holdout · applied interventions
Proof ReportView the full Proof Run evidence and qualification result as a report.

Retained recall 0.94 → 0.93. Catching more churn cases slightly lowers recall on the retained class. This does not affect the criteria above. Figures are examples.

Qualified is not an operational approval. This result says the data meets the Target’s criteria. Approving it for production use is a separate step in Assurance.

Try it on your data
01 / 08Core Readiness
Starting points

Every path starts with a data diagnosis. Only where you find the answer differs.

BaselinestartQualificationAssuranceanswer here

01Before adoption · feasibility

Is our data ready to start AI at all?

Diagnose core readiness first. Once the AI is chosen, verify fit for that task.

Start a free AI-Ready diagnosis
BaselinestartQualificationAssuranceanswer here

02Existing AI performance check

Are AI results below expectations because of the data?

Before swapping the model, compare with only the data changed, same conditions.

Start a Proof Run
BaselinestartQualificationAssuranceanswer here

03Requalify after changes

Which data produced this result, and does it still hold after changes?

Bind each run to its data version. Recheck when conditions change.

Try it on your data

Serving the same AI to many customers

Reuse the shared validation procedure, and adjust it to each customer.

Even with the same model, every customer has different data, business rules, and success criteria. Reuse the shared procedure and evaluation cases, then adjust the data, rules, and criteria for each customer instead of rebuilding validation from scratch.

Book architecture review Examples by company type →
Platform fit

Keep the tools you have. Syntitan fills only the layer between them.

Storage, processing, and access control stay with your current tools. Between data management and AI execution, Syntitan connects task-specific refinement, verification, versions, and run records.

Tool you already useWhat it doesWhat Syntitan adds
Tools in the data management layer
Data platformStorage · processing · accessScores, refines, and freezes versions on top of it.
Data quality toolNull rates · type errorsJudges whether AI runs on this data and what blocks it.
Observability toolDetects that data changedRe-verifies under the same conditions whether the change alters the result.
Sensitive-data transformationTransforms sensitive valuesLinks diagnosis, refinement, verification, versions, and run records in one flow.
Tools in the AI execution layer
Agent toolingRuns the agentsVerifies the data agents use and binds each run to that data version.
If this flow lives across documents, meetings, and notebooksIt is time to look. Let’s map the integration for your stack together.
Book architecture review
Capabilities

LLM Capsule and DTS handle data you cannot use as is, or that is scarce or restricted.

Both are core Syntitan capabilities, and each can be adopted on its own.

Core capability · available on its own

LLM Capsule

Context-Preserving Data Layer for AI

AI works on a context-preserving working version of sensitive data. Results reconnect to real values in your environment.

When to use

  • When the original data cannot go to the AI as is
  • When results must come back as real business values
About LLM Capsule →

Core capability · available on its own

DTS

Rebuilds scarce or restricted data as much as the task needs

Rebuilds restricted, imbalanced, or inaccessible data into data with the patterns, distributions, and relationships the task needs, without moving the original. Inside Capsule, it takes over the data that substitution alone cannot cover.

When to use

  • When training or evaluation data is scarce or imbalanced
  • When the original data cannot be moved or used as is
About DTS →

Syntitan links data versions, actual runs, and requalification evidence into one flow.

Customers

Syntitan already runs
in day-to-day operations.

Teams that need to prepare and verify data before it reaches AI run that work on Syntitan.

  • Samsung Securities Finance · Securities
  • CJ Freshway Food · Distribution
FAQ

Frequently asked questions

Syntitan is an AI-Ready Data Platform. It diagnoses your data and prepares it for a specific task and AI, then proves the result under the same conditions. Every run stays bound to a versioned Release State.

A platform that prepares enterprise data for a specific AI task, verifies that state, and keeps it verified in operation. It fills the layer between data management (storage, processing, access) and AI execution. It does not replace where you store data or the models you run.

The three areas of Syntitan. Baseline is the core diagnosis you can run before choosing an AI, giving a combined score across six criteria. Qualification sets the task and AI criteria, refines only what that target needs, then verifies with a Proof Run under the same conditions and returns a verdict. Assurance freezes the qualified version, binds actual runs to it, and requalifies when something changes.

Yes. Agents are verified the same way as models. You define which task and which data, then verify. Each run is bound to the data version it used, so you can trace which data produced a result.

Clean data can still be a poor fit for a specific AI task. Syntitan verifies whether it works under that task and model.

No. Your data platform, data quality tools, observability tools, and models stay. Syntitan adds data verification and operating evidence on top.

No. The model stays fixed; Syntitan compares data before and after preparation under the same conditions to verify data fit.

That is a result too. Under these conditions, the applied data refinement showed no confirmed improvement. The remaining failure cases and evaluation evidence guide the next refinement or AI configuration review. If evidence is insufficient, the result is Inconclusive.

No. When data drifts or data, models, prompts, or policies change, Syntitan records the change and requalifies the affected range under the same conditions.

A Release State is a fixed, versioned data state; Run Binding links every run to the one it used. Diff shows what changed between two states; Reproduce brings a past state back for requalification.

LLM Capsule gives the AI a context-preserving working version, not the original, and maps results back to real values inside your environment. Deployment details are on the Trust Center.

See whether your data fits the AI task you chose.

Diagnose core readiness, or run a Proof Run under your own AI conditions.

Try it on your data Start a free AI-Ready diagnosis Book architecture review

Diagnosis is free. Preparation, Proof Run, and operations are paid.