Trusted Synthetic Data Solution

Data once locked by regulation,
turned into data you use in the field
— that is synthetic data

RealDataEcho safely synthesizes sensitive data from the public sector, finance, and enterprises while preserving business rules. The generated data can be used as-is for AI training, analytics, testing, and service development.

Business logic preserved Downstream performance validated Re-identification risk prevented
Synthetic Data Sample (Summary)
Record IDTypeCategoryRiskUpdated
REC-0001IndividualConsumerLow2024-05-20
REC-0002Small BusinessRetailLow2024-05-20
REC-0003CorporateManufacturingLow2024-05-19
REC-0004IndividualEducationLow2024-05-19
REC-0005InstitutionPublicLow2024-05-18
Rules & Relationship Graph (Example)
Integrity Pattern Relation Attribute Quality Category Behavior
StrongMediumWeak
Validation Report (Summary)
92 /100
Distribution similarity0.94Good
Correlation structure0.91Good
Rare pattern fidelity0.89Good
Downstream performance0.92Good
Validation result: criteria met
RealDataEcho meets all three conditions for data utilization

Business Logic

Reflects business context and rules
to generate data you can actually use.

Rule consistency guaranteed

Performance

Validates AI and analytics model performance
so it is ready for real-world use.

Performance validated

Privacy

Checks and minimizes re-identification risk
to support safe data use.

Re-identification risk check
Can you actually use your data?

Is your data an asset — or a cost?

Many companies and institutions merely store data of enormous value

01

Finance (Banks · Insurance · Cards · Credit Bureaus)

They hold vast payment, transaction, and credit data,
but regulation blocks both model training and external sales.

02

Public Sector (Government · Public Institutions)

They hold vast administrative and statistical data with open-data mandates,
but privacy concerns constrain release and sharing.

03

Enterprise (AI · Analytics · Services)

AI and analytics need data,
but source data cannot be passed to vendors or shared across teams.

Until now, corporate and institutional data has not been an asset that creates value — it has been a cost that must be stored and managed.

BEFORE

Blocked by regulation and sensitive data, it ends as a cost

STEP 01 Source Data
STEP 02 Attempt to use as-is
STEP 03 Regulation & sensitive-data conflict
Work halted · Projects delayed
AFTER

Safe data
creates value

STEP 01 Source Data
STEP 02 · KEY RealDataEcho Synthetic Data
STEP 03 Safe Utilization
Value creation · AI · Analytics · Services
SOLUTION

A structured-data AI engine with a 5-step integrated workflow

A specialized solution that automates synthetic data generation,
flexibly applying each stage — collection, cleansing, analysis, synthesis, and visualization.

01

Source Data

Safely collect sensitive data
from diverse sources.

02

Rule & Pattern Analysis

Comprehensively analyze structure, relationships,
distributions, patterns, and domain rules.

03

Synthetic Data Generation

Generate safe synthetic data
based on the analysis.

04

Privacy Validation

Verify re-identification risk and similarity
to confirm safety.

05

AI & Analytics

Use validated data for AI training,
analytics, and service development.

Solution at a Glance
Data Connection
Rule & Pattern Analysis
Synthetic Data Generation
Privacy Validation
AI & Analytics
Synthetic Data Sample (Summary)
Customer IDAgeRegionPurchaseTenureChurn
CUST-000130sSeoul125,00014 moNo
CUST-000240sBusan98,0007 moNo
CUST-000320sDaegu63,0003 moYes
Validation Report Summary
92 /100
Distribution similarity0.94Good
Correlation structure0.91Good
Rare pattern fidelity0.89Good
Downstream performance0.92Good
Validation result: criteria met

Security

Strong security design and access controls
keep your data safe.

Validation

A multi-layer validation system guarantees
privacy and quality at once.

Utilization

Data ready to use immediately,
from AI training to service development.

Scalability

An architecture that scales flexibly
to large volumes and diverse domains.

Core Values

Meeting all three conditions for data utilization

RealDataEcho delivers business context, privacy, and performance validation together.

Business Logic

Reflects business context and rules
to generate data you can actually use.

Rule consistency guaranteed
92% Rule consistency
Rule consistency check Pass
Domain rules
Relationship rules
Value range rules
Business constraint rules

Privacy

Checks and minimizes re-identification risk
to support safe data use.

Re-identification risk check
0.94 /1.00 Privacy score
Key privacy metrics
Distribution similarity0.94Good
Correlation structure0.91Good
Rare pattern fidelity0.89Good
Downstream performance0.92Good

Performance

Validates AI and analytics model performance
so it is ready for real-world use.

Performance validated
92 /100 Validation report score
Model performance retention
Classification93%
Regression91%
Clustering90%
Time series92%

Preserve the DNA
of real data —
completely remove personal information

Combining differential privacy (DP), contrastive learning (FLCL), and data augmentation
to solve the sensitivity and complexity of structured data at once.

Differential Privacy (DP) Contrastive Learning (FLCL) Data Augmentation
Dashboard
Data Upload
Structure Analysis
Synthetic Data Generation
Validation Report
Deploy & Use
Project Management
API Management
Settings
Projects > Finance_CustomerData >
Finance_CustomerData ● In Production
OverviewDataGeneration SettingsValidationUtilization
Data quality score
92/100 Good
Re-identification risk check
Passed Low
Downstream performance
0.92 Good
Generated records
1.25Mrows
Generation Progress
UploadAnalyzeGenerateValidateDeploy
Complete
2024-05-20 14:22
Latest Validation Report
92 /100
Distribution similarity0.94Good
Correlation structure0.91Good
Rare pattern fidelity0.89Good
Downstream performance0.92Good
View full report →
1

Data Upload

Securely upload data
in various formats

2

Structure Analysis

Automatically analyze schema,
distributions, and correlations

3

Synthetic Data Generation

Generate high-quality data
at your chosen privacy level

4

Review Validation Report

Check utility, similarity, and re-identification risk
in one validation report

5

API · Files · On-Premises

Deploy the way you want and use it
directly in your work and systems

Security and governance are built into every step, so you can use it with confidence.
End-to-end encryption Access control & audit logs Policy-based governance Korea-based data centers
Validation Report

More important than generation: validation

RealDataEcho validates data quality, privacy,
and real-world usability in a single report.

Validation Report
Summary
Overall score and criteria pass/fail
Distribution similarity
Per-variable distribution match
Correlation structure
Inter-variable correlation checks
Rare pattern fidelity
Fidelity of rare values and patterns
Re-identification risk
Privacy risk assessment
Downstream performance
Real model performance comparison
92 /100
Overall validation result
Validation result: criteria met

The generated data meets quality, privacy, and usability criteria.

Report ID
VAL-202405-0012
Generated data
Synthetic Data Sample (Summary)
Validated at
2024-05-20 14:30
Metric Details
Distribution similarity
0.94 Good
Each variable's distribution
closely matches the source.
Correlation structure
0.91 Good
Inter-variable correlations
are well preserved.
Rare pattern fidelity
0.89 Good
Rare values and their frequencies
are reproduced faithfully.
Re-identification risk
Low
Re-identification probability is low —
assessed as safe.
Downstream model performance
92/100 Good
Real model performance
is maintained at a high level.
Synthetic data Source data Baseline
Privacy Risk
Overall risk Low
Privacy criteria met
Personal data exposureLow
Attribute inference riskLow
Linkage riskLow
Membership inference riskLow
i
About the Criteria
All metrics are evaluated against predefined criteria, set to reflect industry standards and the characteristics of the data.
View detailed criteria ›
USE CASE

Use cases for every organization that needs data

From the public sector to finance, enterprises, and AI teams — safe data utilization for every environment.

Public Sector

Safely supporting open policy and
administrative data and analytics
Expanded open-data release and use
Privacy protection and de-identification
Better policy-making and administrative efficiency

Finance

Protecting sensitive data, meeting regulation,
and strengthening risk analytics
Regulatory compliance and audit support
A safe environment for analyzing sensitive data
Risk management and better customer experience

Enterprise

Insights and service improvements
from customer and operations data
More advanced data-driven decisions
Operational efficiency and cost savings
Better customer experience and service quality
AI

AI Teams

Safe data for training,
testing, and evaluation
Safe data for training, testing, and evaluation
Model performance validation and quality gains
Reproducible, trustworthy evaluation
Try it today

Could your organization's data
be safely put to work too?

See how RealDataEcho puts data to work — through a sample report and a short demo.

Fast turnaround

Sample report within 1 business day of request

Security first

Sensitive-data protection and governance compliance

Ready for real work

Insights you can apply to your business right away

Sample Utilization Report
92 /100
Data quality score
Good
Criteria met
Key insights
  • Revenue growth driver analysis
  • Customer segment insights
  • Early risk-signal detection
Data application areas
Sales strategy Marketing efficiency Risk management Product planning