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Synthetic Data Infrastructure

Generate production-grade synthetic data without exposing a single real record.

SynthVault creates statistically faithful, privacy-safe datasets that preserve relationships, distributions, and edge cases — so regulated teams can build, test, and share without compliance risk.

0real records exposed
ε-deltadifferential privacy
SOC 2Type II certified

Trusted by data teams in regulated industries

01 What It Is

More than anonymization. This is data replication at the structural level.

Traditional masking and tokenization destroy the statistical relationships that make data useful. SynthVault uses generative AI and differential privacy to build entirely new datasets that mirror the statistical properties of your source — down to correlations, distributions, and rare edge cases — while containing zero real records.

How the synthesis engine works

Anonymization
  • Correlations destroyed by masking
  • Edge cases and rare classes lost
  • Re-identification risk remains
  • Models trained on it underperform
SynthVault
  • +Full statistical fidelity preserved
  • +Rare events reproduced faithfully
  • +Zero real records — nothing to leak
  • +Provable DP privacy guarantees
02 The Platform

The full synthetic data stack in one platform.

01

Ingestion Engine

Connect to warehouses (Snowflake, BigQuery, Postgres), streaming pipelines (Kafka, Kinesis), or flat files. Schema detection and relationship mapping are automatic.

02

Synthesis Core

Choose from GANs, VAEs, or diffusion-based models. Auto-selects the optimal architecture based on data type and cardinality.

03

Validation Suite

Statistical fidelity reports, privacy leakage tests (membership inference, attribute disclosure), and drift detection against source distributions.

04

Distribution Layer

Export to Parquet, CSV, or direct warehouse push. Role-based access, audit trails, and API endpoints for CI/CD integration.

Explore the platform in depth

03 Workflow

Connect. Synthesize. Validate. Deploy.

01

Connect

Point to your source database or upload schema. No data leaves your VPC.

02

Configure

Select tables, set privacy budget (epsilon), choose synthesis model.

03

Generate

AI builds the synthetic dataset. Full lineage and reproducibility logs.

04

Validate & Share

Run privacy and fidelity reports. Share with vendors, teams, or training pipelines.

See the Full Workflow

04 Who It Serves

Built for teams who ship models and protect data.

ML Engineers

Train and test models on synthetic data that preserves class imbalance and feature correlations. No more data access requests.

Data Engineers

Populate staging and dev environments with realistic data. Stop using production dumps.

Compliance Officers

Meet GDPR, HIPAA, and CCPA requirements for data minimization. Full audit trails and DP guarantees.

AI Product Teams

Share datasets with external vendors, annotation teams, and research partners without legal review cycles.

05 Core System

The moat is not one model. It is the fidelity and privacy engine around it.

Generative models are commodities. SynthVault's advantage is the system that measures, constrains, and proves what those models produce — across three tightly integrated layers.

0%
Avg. distribution match
0+
Regulated organizations
0
Privacy incidents
0B+
Synthetic rows generated
Layer 01

Generation Layer

GANs, VAEs, diffusion models, and tabular transformers. Auto-architecture selection based on data profile.

Layer 02

Privacy Layer

Differential privacy (epsilon-delta), k-anonymity, l-diversity, and membership inference testing. Configurable privacy budget.

Layer 03

Governance Layer

Role-based access, data lineage, synthetic-to-source mapping, automated compliance reporting.

Field Reports

What teams say

"

We cut our vendor onboarding time from 6 weeks to 3 days. SynthVault gave us synthetic patient records that our ML team couldn't distinguish from real data.

Dr. Elena VossChief Data Officer, Coastal Health System

"

Our compliance team finally stopped blocking every data science project. The differential privacy reports are bulletproof.

Marcus ChenVP Data Engineering, Meridian Bank

"

We use SynthVault to generate 10M synthetic insurance claims for stress testing. The distributions match our production data within 0.3%.

Sarah KimLead Data Scientist, Apex Insurance

Pricing

Predictable pricing. No per-record surprises.

Monthly Annual SAVE 20%

Developer

$499 /mo

per month, billed monthly

For solo ML engineers and data scientists building proofs-of-concept.

  • 1M synthetic rows / month
  • 3 data sources
  • CTGAN + TVAE models
View Full Pricing

Enterprise

Custom

 

For organizations with on-premise requirements, custom models, and dedicated support.

  • Unlimited synthetic rows
  • Cloud + on-prem deployment
  • Dedicated support + SLA
View Full Pricing

Stop choosing between data utility and data privacy.

Your first synthetic dataset is free. Connect your warehouse and see the fidelity report in minutes.