DataGenesis

A Skymantics Product
Enterprise-grade synthetic data
Overcome data scarcity and privacy barriers with a dynamic, multi-temporal population digital twin.

DataGenesis is our AI generation platform designed to synthesize fully dimensional Synthetic Digital Twins — from entire populations to complex operational environments — without ever exposing PII or PHI.

Production-Proven
IRS
96%
FISMA
Healthcare
The challenge
Population data is powerful.

Statistics institutions and U.S. federal agencies face an impossible trade-off: release rich data and risk reidentification, or release safe aggregates and samples that limit what researchers, contractors and partners can actually do.

Aggregates hide reality

Population data usually arrives as aggregated statistics or thin survey samples — in the U.S., public-use microdata is often just a 1% slice. Either way, analysts lose the granularity needed for population-scale modeling, projections and scenario analysis.

Anonymization can be reversed

Traditional masking and de-identification remain vulnerable to linkage attacks. The more attributes you publish, the higher the risk to individuals.

What DataGenesis does
Generate. Don't anonymize.

DataGenesis learns the statistical structure of your data and generates a new population from scratch — one you can extend with your own attributes and share safely across your ecosystem. The output preserves deep multivariable correlations without containing a single real person.

Train robust AI models

Work with a complete population across every attribute — and extend it with the attributes you care about, so models learn from full-scale, richly dimensioned data instead of thin samples.

Validate software safely

High-fidelity synthetic environments for testing, modeling and validation — without operational risk.

Simulate "what-if" scenarios

Run population-level forecasts and scenarios that historical data alone cannot predict.

96%
Accuracy at population scale.

Measured using Mean Absolute Percentage Error across validated populations — the fidelity behind the world’s most advanced population digital twin, and the threshold that makes synthetic data viable for real statistical work.

How it works

From real data to a synthetic twin, in four stages.

01
Ingest

Connect source data within your own private infrastructure. Raw data never leaves your environment.

02
Learn

Our models learn the statistical structure across diverse datasets, distributions, correlations and dependencies, and how every attribute interrelates.

03
Generate and age

Every record fabricated, none real, yet accurate and safe. It allows entities to evolve longitudinally over multiple simulated years.

04
Validate

Outputs are compared against ground truth at local and national level before release.

Proven in production
Where DataGenesis is already delivering.
Flagship deployment
U.S. Internal Revenue Service
In production · 4+ years

Testing tax enterprise systems and generating fraud typologies without risking sensitive information.

Pilot
Healthcare
Synthetic patient cohorts

Enabling clinical analytics and population health research with rich, attribute-extended cohorts.

Pilot
Disaster response
First-responder enrichment

Synthetic populations enriched with health and mobility data for emergency planning teams.

Security & infrastructure
Your data never leaves your perimeter.

DataGenesis is built for institutional deployments where data residency, sovereignty and control are non-negotiable.

Private cloud or on-premise

Deploy on Skymantics Foundry, your private cloud, or sovereign infrastructure.

Raw data stays in-house

Source data is processed entirely within your environment, never transmitted to third parties.

Federal-grade security posture

FISMA and NIST SP 800-53 aligned. Operated to U.S. federal agency standards.

Compliance-ready

Aligned with GDPR, federal data-protection frameworks and statistical disclosure control.

DATA PHILOSOPHY

“The data you trust us will never leaves the environment you control.”

Resources
Go deeper.

Technical documentation, methodology papers and case studies for analysts, statisticians and decision-makers.

Frequently asked questions
General questions.
How is DataGenesis different from anonymization or differential privacy?

DataGenesis generates new synthetic records from learned distributions. It doesn’t mask or perturb real records — there is no real individual behind any output, so linkage attacks are not applicable.

What input data is required to train the model?

Typically structured microdata about whatever entity you model — individuals, households, dwellings, businesses, vehicles, accounts and more, depending on the use case. Our team works with you during onboarding to define the schema and validation targets.

How long does a typical deployment take?

From kickoff to first validated synthetic population, most deployments fit within a single quarter — depending on data complexity and infrastructure setup.

Can the synthetic population be extended with new attributes?

Yes. DataGenesis is designed for attribute extension — health, mobility, economic, behavioral — using auxiliary datasets while preserving statistical consistency.

Is the output ready for publication and external use?

Yes. Output is designed to support open publication, third-party research and operational use cases that traditional anonymized samples cannot.

Accelerate your innovation with safe and accurate data

Book a 30-minute working session with the Skymantics team. We’ll walk through your specific needs, infrastructure, and the solution you need.

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