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Generative AI · Synthetic Data

Real data is scarce.
Synthetic data isn't.

Synthsai learns the statistical shape of your data and generates as much privacy-safe synthetic data as your models need — tabular, time-series, and relational.

Try the live generator Book a demo
98.4%
Avg. statistical fidelity
<0.1%
Re-identification risk
40B+
Synthetic rows generated
6
Dedicated engines
The constraint

Real data is the bottleneck,
not your model

Approval queues, privacy risk, and limited volume slow every ML team down before training even starts.

// Working with real data only
Locked behind approval queues and legal review — weeks of waiting before a single training run
Contains real PII your team is legally liable for under GDPR, HIPAA, and CCPA
Limited volume — impossible to stress-test edge cases or rare class scenarios at scale
Reflects real-world bias and class imbalance — your model inherits every blind spot
// Generating with Synthsai
Generate on demand via API — no approval queue, no waiting, no compliance bottleneck
No real individual's data in the output — provably private with a differential privacy budget
Unlimited volume — generate billions of records for training, testing, and sharing
Rebalance classes and synthesize edge cases deliberately — not accidentally
Try it yourself

Generate a preview, right now

Pick a template, set your parameters, and watch a synthetic dataset take shape in your browser.

synthsai.generator — live in your browser
READY
420
Fidelity
—
Privacy
—
output.synthetic Ready
Click "Generate synthetic data" to produce a preview dataset — right here, in your browser.

This preview runs entirely client-side to demonstrate the concept — no real data is uploaded, and no external model call is made. Production Synthsai runs the full generative pipeline server-side.

Process

From schema to synthetic dataset

Four stages — repeatable via API, not a one-off export.

01 · UPLOAD
Point us at a sample
Upload a sample dataset or define a schema by hand — no real data ever has to leave your environment.
02 · LEARN
Learn the distribution
Generative models learn the joint statistical distribution — not just column-level averages or marginals.
03 · GENERATE
Produce synthetic rows
Generate as many synthetic records as you need, on demand or via a live streaming API endpoint.
04 · VALIDATE
Check fidelity & privacy
Every dataset ships with a fidelity report and a re-identification risk score before you export a single row.
Why Synthsai

Built for teams that can't risk
a real data leak

Statistical usefulness and provable privacy, without the tradeoff you'd expect.

Statistical fidelity
Synthetic data matches the real distribution's correlations, outliers, and edge cases — not just column averages. Models trained on Synthsai output perform within 1.6% of those trained on real data.
Provable privacy
Differential privacy guarantees with a configurable ε budget. A re-identification risk score ships with every export — not a vague promise, a quantified number you can hand to your DPO.
Schema-aware generation
Upload a sample or define a schema manually. Synthsai infers column types, value constraints, and foreign-key relationships automatically across multi-table schemas.
Export anywhere
CSV, JSON, Parquet, or a live API endpoint. Plug directly into your existing training pipeline, data warehouse, or CI test fixtures — no custom adapters required.
Generation engines

Purpose-built for every data shape

Different data types need different generative approaches. Synthsai runs a dedicated engine for each.

Tabular
Tabular Synthesis
Column-aware generative models trained on your schema's full joint distribution. Preserves inter-column correlations invisible to column-marginal methods.
Sequential
Time-Series Generator
Preserves seasonality, trend, autocorrelation, and cyclic patterns across generated sequences. Works on irregular as well as fixed-frequency series.
Relational
Multi-Table Synthesis
Keeps referential integrity across foreign keys and joins, table to table. Generate a synthetic database — not just a synthetic spreadsheet.
Text
Free-Text Corpus
Generates realistic notes, reviews, support tickets, and clinical text at scale — fine-tuned on domain vocabulary for high domain coverage.
Privacy
Differential Privacy Engine
Injects calibrated noise with a provable, configurable privacy budget (ε). The budget is surfaced transparently — you control the fidelity/privacy tradeoff explicitly.
Streaming
Event & Geospatial
Synthesizes location traces, clickstreams, and IoT event logs with realistic timing distributions and spatial coherence — not random scatter.
Case study

How Meridian Health generated
2M synthetic patient records

"Our external partners couldn't tell the difference in their models' performance. That's exactly the point — the synthetic data was so statistically faithful that it became a full substitute for our production EHR."

NW
Dr. Naomi Weiss
Director of Research, Meridian Health
2M
Synthetic records generated
0
Real identifiers exposed
98.4%
Statistical fidelity score
Trust

Privacy isn't a feature here —
it's the product

Every dataset is validated before it ever reaches your hands.

Differential privacy by default
Every generation run has a configurable, provable privacy budget (ε) — not a vague promise. You choose the tradeoff; we enforce it mathematically.
Re-identification scoring
A quantified re-identification risk score, based on nearest-neighbor distance to source data, ships with every dataset — before you ever export it.
No real data retained
Source samples are processed in an isolated, ephemeral environment and discarded immediately after model training. Nothing is stored, logged, or retained.
Full audit trail
Every generation run is written to an immutable, tamper-evident audit log you can hand directly to a regulator or internal compliance team.
Compliance roadmap: SOC 2 Type II in progress GDPR-ready pipeline HIPAA-eligible architecture
Pricing

Priced on rows, not seats

Start free, scale as your generation volume grows. No seat fees.

Starter
$ 0 / month
For individuals, research, and small experiments.
  • 100K synthetic rows / month
  • 1 active project
  • CSV & JSON export
  • Community support
Start free
Most popular
Team
$ 1,200 / month
For ML teams generating synthetic data continuously in production.
  • 10M synthetic rows / month
  • Unlimited projects
  • Full API access & live endpoint
  • All export formats (CSV, JSON, Parquet)
  • Dedicated Slack channel
Request a pilot
Enterprise
Custom
For regulated industries or high-volume deployments needing dedicated infrastructure.
  • Unlimited generation volume
  • Dedicated isolated deployment
  • Custom privacy review & DPA
  • SSO / SAML
Talk to us
The team

Researchers who take privacy seriously

A small team with deep backgrounds in generative modeling and applied cryptography.

Dr.
Dr. Samuel Onivehu John
Co-founder & CEO
Formerly generative modeling research at NVIDIA. PhD in machine learning from Stanford.
Raj Patel
Mary James
Co-founder & CTO
Applied cryptography & differential privacy, ex-Google. Holds 4 patents in privacy-preserving computation.
Dr. Mei Lin
Dr. Martha Amuwo
Head of Research
PhD in statistical learning from MIT. Focused on tabular generative models and distribution matching.
Daniel Osei
Daniel Amadi
Head of Product
Previously built data infrastructure tooling at Snowflake. Obsessed with making complex pipelines feel simple.

Team photography via Unsplash — placeholder portraits. Swap in real photos before publishing.

Feedback

What early teams say

Real feedback from teams generating synthetic data in production.

★★★★★

"We shipped a partner integration in two weeks instead of two quarters, because we never had to wait on a data-sharing agreement. Synthsai removed the single biggest bottleneck in our ML pipeline."

CD
Claire
Head of Data, Fintra Bank
★★★★★

"The privacy score on every export made our compliance review a formality instead of a six-week back-and-forth. Our DPO signed off on the first run without a single question."

MW
Marcus
CISO, Insurely
★★★★★

"We rebalanced our fraud dataset with synthetic edge cases we'd never have collected fast enough organically. Our model's minority-class recall jumped 18 points in a single sprint."

AT
Aiko
ML Lead, RetailSense
Questions

Frequently asked

The questions data and compliance teams usually ask first.

How is synthetic data different from anonymized data?
Anonymized data is still real data with identifiers removed or masked — it can often be re-identified using auxiliary information. Synthetic data is newly generated by a model that learned the statistical patterns of the original; no real record exists in the output at all.
Can synthetic data really replace real data for training?
For most use cases, yes — models trained on high-fidelity synthetic data perform comparably to models trained on the real source data. Synthsai's fidelity score lets you verify this before committing to a full training run. We recommend validating on a held-out real sample before full production rollout.
What formats can I export?
CSV, JSON, and Parquet today, plus a live API endpoint for streaming generation directly into your pipeline. Enterprise customers can request custom connectors for data warehouses and feature stores.
Is there a limit on how much data I can generate?
Each plan includes a monthly row allowance; beyond that, generation is metered per million rows. Enterprise plans support unlimited volume under a dedicated deployment with no metering.
How do you measure privacy risk?
Every dataset ships with a re-identification risk score based on nearest-neighbor distance to the source data, alongside the differential privacy budget (ε) used during generation. Both numbers are auditable and can be included in regulatory submissions.
Get started

Generate your first dataset today

Try the live generator above, or talk to us about a pilot using your actual schema.

Try the live generator View API docs
Let's talk

Let's generate
something useful

Reach out for a demo, a pilot, or just to talk through your data access problem.

Phone
+2349037867676
Office
Lokongoma Plaza, Lokoja Nigeria
HTMLEOF