Synthetic data services for regulated industries

Real Insights
Synthetic Freedom

Synthaina is a deeptech innovation company delivering privacy-by-design synthetic data services for regulated domains. We combine advanced generative modelling, rigorous statistical validation, and ISO/IEC-aligned bias detection to enable secure, compliant, and accelerated AI development — across healthcare, finance, cybersecurity, and beyond. Working as a technical co-creator rather than a generic vendor, SYNTHAINA AI also delivers bespoke IT solutions — designed and deployed for your specific domain, technology stack, and regulatory environment, from prototype to production.

Synthetic Data Services

Data Generation · Validation · Bias Detection · Privacy

Aligned with

GDPR · ISO/IEC standards · sector-specific regulatory frameworks

Bespoke IT Solutions

Designed and deployed for your domain and stack

Generation is easy.
The science is everywhere else.

A useful synthetic dataset is one that statistical reviewers can defend, that downstream models trust, and that is free of the biases embedded in the source. That bar is set by the validation and bias audit that follow generation — not by the generator itself. Our services are engineered around that asymmetry.

01 — Services

What we deliver.

01 / Service

Data Generation

Eight generators, one interface — produce synthetic tabular data at research quality.

A toolkit of state of the art optimized generators for producing high-quality synthetic tabular data. Statistical, Bayesian, and deep-learning generators are available out of the box, each paired with an immediate evaluation suite so you know the quality of what you produced before you download it.

8 generators: MVND, LogMVND, BGMM, ABMS, VAE_ABMS, TabGAN, TabVAE, TabDiff
Per-feature evaluation
Privacy: k-anonymity score & disclosure risk
Interactive visualisations

02 / Service

Fidelity Validation

Validation is the science — not the afterthought.

A modular evaluation toolkit that measures the statistical, structural, and graph-level fidelity of any synthetic dataset against its real counterpart. It produces a structured report and a full suite of visualisations — giving you a defensible, reproducible record of synthetic data quality that reviewers and auditors can scrutinise.

Per-feature metrics: KS, JS, Wasserstein, Hellinger, KL, TVD, Chi-Square
Global metrics: covariance similarity, correlation distance, mutual information
Embedding & graph fidelity: CKA, Jaccard neighbourhood overlap, spectral distance
Visualisations: KDE plots, correlation heatmaps, PCA/UMAP/t-SNE overlays, kNN graphs
Data quality metadata: completeness, outlier rates, feature counts
Structured JSON + PDF report — reviewer-ready out of the box

03 / Service

Data Bias Assessment

ISO/IEC standard-aligned bias auditing with LLM-powered evaluation.

An LLM powered bias assessment toolkit built on the ISO/IEC standard. It combines 15 pre-training fairness metrics with a structured questionnaire across nine bias categories, scored by a local or API-connected LLM. The result is a radar chart, a Sankey diagram, and a full exportable report — giving you both a number and a narrative for every risk.

15 pre-training fairness metrics: CI, DPL, DD, JS, TVD, KS, NMI, BR, BD, CORR, LR, CDD, NCMI, CBD, L2
Structured questionnaire across 9 bias categories
Interoperability visualization
LLM scoring: local on-premise models or cloud API
Radar chart by ISO category & Sankey diagram (Question → Category → Risk)
Full assessment report with recommendations

02 — Principles

Why Synthaina.

Four commitments that distinguish Synthaina from generation-only tools and from generalist data-science platforms.

⟢ 01

Validation-first

We do not just generate. We treat statistical validation as the primary deliverable — the generator is a means to it, not the product.

⟢ 02

Bias-aware by design

Fairness metrics and LLM-powered qualitative assessment are part of the core workflow — bias detection is not a separate step bolted on later.

⟢ 03

Regulation-aligned

GDPR and sector-specific AI governance standards — guided by our provided services outputs and reports, not added as an afterthought.

⟢ 04

On-premise first

You have your infrastructure. We deploy our services.

03 — News

Latest from Synthaina.

Publications, releases, and updates from the team.

Publication Dec 2025

Synthetic Data Blueprint (SDB): A modular framework for the statistical, structural, and graph-based evaluation of synthetic tabular data

Pezoulas, V.C., Tachos, N.S., Georga, E., Marias, K., Tsiknakis, M., Fotiadis, D.I.

Introduces SDB — a modular Python toolkit covering per-feature distribution metrics, global structural similarity, and embedding/graph-level fidelity with reproducible JSON + PDF reporting. Published on arXiv.

Read on arXiv →

Publication Jun 2026

TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling