Syntitan · AI-Ready Data Platform

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

Start by assessing data readiness. Once the use case and AI are set, compare before and after preparation under the same conditions, then tie every run to its released version and revalidate after changes.

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

Built onData Readiness Score (six axes)PII detection

Overview

Your existing data, prepared for your AI,
validated, and carried into operations.

Not sure which AI yet? Start by assessing your data. Already chose one? Start by validating your data for that use case.

Data Readiness · 1–2

Assess one file on six axes, 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.

Use Case Validation · 3–6
  1. 01Define the use caseDecide which use case, which model or agent, what counts as success, and which cases to evaluate on.
  2. 02Prepare the dataPrepare only the data that affects that use case’s result, keeping relationships and context intact.
  3. 03Validate under the same conditionsKeep the AI and run conditions fixed, and compare results on the data before and after preparation.
  4. 04Review the resultsJudge whether the use case criteria you set were met, with results and evidence. Qualified, not qualified, or inconclusive.
Operational Assurance · 7–8
  1. 05Release, review changes, and revalidateRelease the validated data as a version, log the data and conditions each run used, and recheck the verdict when they change.Changed? Revalidate what it affects
  • Not a tool that stops at cleanup or preprocessing.It validates whether prepared data actually fits a specific AI use case.
  • 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 revalidated.
Product areas

Syntitan works in three areas.

Assess Data Readiness before and after: 75 to 84 after improving selected issuesExample screen
Usability
Can sensitive data be used with AI?
Integrity
Are gaps, errors, 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 assessment is free inside Syntitan.

Start a free AI-Ready diagnosis
Steps

Three areas, eight steps.
Start at the step that fits your situation.

Data Readiness Steps 1–2

  1. Upload your dataStart with one data file
  2. Assess Data Readiness Overall scoreUsabilityIntegrityContextConsistencyReproducibilityTraceability
  3. Improve Data Readiness optionalFix only the common issues found

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

Use Case Validation Steps 3–6

  1. Define Use CaseUse case · model or agent version · criteria · evaluation cases
  2. Prepare Data for Use CasePreparation that keeps relations and context
  3. Validate Data Fit with Model or AgentAI conditions fixed, only the data differs
  4. Review Validation ResultsQualifiedNot QualifiedInconclusive

Operational Assurance Steps 7–8

  1. Release Data VersionThe validated version and its conditions, fixed as a Release State
  2. Review Changes & RevalidateRun Binding links each run to its version · changes are recorded and Diff compares two states · the same fit validation re-runs on the affected scope, and Reproduce recalls that state

Three ways to validate fit.

Validate fit with your AI
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 use case metric, evaluation set, and model or agent version, 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 modules

Only the preparation
your use case needs,
in six methods.

We prepare only the data that affects your use case, keeping relationships and context intact. When data is scarce or cannot be used as is, 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.

Assessment scores, preparation steps, before-and-after comparison, release history.
See where each piece of evidence lives, on representative product screens.

01 / 08Assess Data ReadinessData Readiness
Assess Data Readiness

Assess your data's basic readiness across six axes, and pinpoint what to fix first.

Data readiness score · six axes 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

The assessment is free in Syntitan, from upload to the 6-axis score.

Start a free AI-Ready diagnosis
Validate Data Fit with Model or Agent

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

Evaluation task
Binary classificationtarget churn_flagAnomaly detectionComing soonRegressionComing soonMulti-classComing soon
Your original data and readiness score stay as they are. The prepared data is saved as a new version.
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
ModelXGBoostv1.3 · version fixed
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 preparation3 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.

Validate fit with your AI
Define Use Case

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

Customer RetentionSave Use Case Profile
Fixed for fit validation
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, preparation and fit validation follow this use case.

Try it on your data
Improve Data Readiness Optional

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

From your assessment Consistency 55Integrity 60Context 65These axes need attention first
MetadataDuring improvement, 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 ratioIntegrity score
Review recommended
Context loss riskContext
3 fields detected
Why it is off by default
Preserve signal
Changing these fields may remove context an AI use case relies on. Review before turning it on.
MetadataSignal contributionField dependency

Improving data readiness is optional. If a use case is already defined, you can go straight to Use Case Validation.

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

Try it on your data
Prepare Data for Use Case

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
Use case 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 SUCCESS CRITERIA
Low-impact formatting issueDoes not affect use case metrics
Unused metadata completenessField not used in this use case
Not every low readiness item needs to be fixed. Skipped items are recorded with their reason.
Resulting data state
Original Data StateUse-case-specific PreparationPrepared Data State v2

After addressing only the prioritized issues, fit validation runs under the same use case.

Try it on your data
Release Data Version

Release a validated data state as a version. Releasing a version is separate from approving it for operational use.

Customer RetentionBinary classification · XGBoost v1.3 · Validation Result #VR-1842
Qualified Data StateCustomer Retention · PREPARED DATA STATE v2
Validation: 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 validated data state with the conditions it was validated under. Approval for operational use is still a separate step.

Where this Release goes
Prepared Data State v2Release v1Run Binding

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

Try it on your data
Review Changes & Revalidate

See what is actually running, review what changed, and revalidate when conditions change. Figures are examples.

Customer RetentionBinary classification · Release v1 · Production Run #2031
A condition changed after this release was validatedPolicy Set v2 → v3 · recorded Sep 3, 2026. The previous result no longer covers the current setup.
In operation nowRun Binding · PRODUCTION RUN #2031
Data state
Prepared Data State v2
Model
XGBoost v1.3
Policy
Policy Set v3
Release
Release v1
In operation since Aug 21, 2026
What was validatedRelease State · Validation #VR-1842
Data state
Prepared Data State v2
Model
XGBoost v1.3
Policy
Policy Set v2
Result
Recall 0.84 · F1 0.82
Qualified under Policy Set v2

Revalidation re-runs only what the change affects. The data is not wrong. The previous result just does not cover the new condition.

How this Release has moved
Release v1Run Binding #2031Policy change · v2 to v3Revalidation

Bind actual runs to the released version, and when conditions change, review the change and revalidate only the affected scope.

Try it on your data
Review Validation Results

Judge whether the data met the defined use case criteria. Improvement and meeting the criteria are kept separate, with the fit validation evidence.

Customer RetentionBinary classification · XGBoost v1.3 · Recall ≥ 0.82 · F1 ≥ 0.80
Data Qualifiedfor Binary classification with XGBoost v1.3
MetricOriginalPreparedSuccess criteriaResult
F10.750.82≥ 0.80Pass
Recall0.710.84≥ 0.82Pass
Precision0.790.80No degradationPass
EvidenceRepeated-run mean · variance · failure cases · holdout · applied interventions
Validation ReportView the full fit validation evidence and 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 use case’s criteria. Production approval is a separate process, and Operational Assurance keeps the evidence.

Try it on your data
01 / 08Assess Data Readiness
Starting points

Start at the stage that fits your situation. Criteria and records carry forward.

Data ReadinessstartUse Case ValidationOperational Assuranceanswer here

01Before adoption · feasibility

Is our data ready to start AI at all?

Assess your data first. Once the AI is chosen, validate fit for that use case.

Start a free AI-Ready diagnosis
Data ReadinessUse Case ValidationstartOperational Assuranceanswer here

02Existing AI performance check

Are AI results below expectations because of the data?

Compare results under the same AI conditions, with only the data changed.

Validate fit with your AI
Data ReadinessUse Case ValidationOperational Assurancestartanswer here

03Revalidate 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 use-case-specific preparation, validation, versions, and run records.

Tool you already useWhat it doesWhat Syntitan adds
Tools in the data management layer
Data platformStorage · processing · accessAssesses, prepares, and freezes versions on top of it.
Data quality toolNull rates · type errorsJudges whether this data can meet the criteria of the AI use case you set, and what blocks it.
Observability toolDetects that data changedRevalidates under the same conditions whether the change alters the result.
Sensitive-data transformationTransforms sensitive valuesLinks assessment, preparation, validation, 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 is stitched together across documents, meetings, and notebooksIt is time for a closer 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 revalidation evidence into one flow.

Customers

Preparing data for AI
with Syntitan.

Customers use Syntitan to prepare and validate data before it reaches AI.

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

Frequently asked questions

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

A platform that prepares enterprise data for a specific AI use case, validates that state, and keeps it validated 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. Data Readiness assesses your data before you choose an AI: a combined score across six criteria, with optional improvement. Use Case Validation defines the use case and AI criteria, prepares only the data that use case needs, then validates fit with the model or agent under the same conditions and returns a verdict. Operational Assurance releases the validated data as a version, binds actual runs to it, and revalidates when something changes.

Yes. Data for agents is validated the same way as data for models. You define the use case and the data, then validate. 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 use case. Syntitan validates whether it meets that use case's criteria under the model conditions you set.

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

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

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

No. When data, models, prompts, or policies change, Syntitan records the change and revalidates 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 revalidation.

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 covered in an architecture review.

See whether your data fits the AI use case you chose.

Assess your data, or validate fit under your own AI conditions.

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

The assessment is free. Preparation, fit validation, and operations are paid.