Real data is a liability.
Synthetic data is a superpower.
Generate privacy-safe, statistically faithful synthetic datasets for AI training, testing, and research. No real people. No compliance risk. Full model utility.
What others don't do.
What we built.
No Upload Required
Others require you to upload real data first. We ship 10 pre-built industry templates — banking, healthcare, insurance, retail, HR. Generate 100K credit applications in one click. Zero data leaves your hands.
Schema-Only Generation
Define your data structure as a JSON schema — column names, types, ranges — and generate millions of records without a single row of real data. No seed data needed. Ever.
Built-in Bias Detection
Every generated dataset is automatically scanned for demographic imbalance and outcome disparity. Get a fairness score and specific warnings before you train. Nobody else does this by default.
Gaussian Copula Engine
Not naive random sampling. Our Gaussian Copula preserves multivariate correlations between columns. AIC-based distribution fitting across 6 distribution families for 90%+ fidelity.
3-Format Export + API
CSV, JSON, and Apache Parquet out of the box. RESTful API with Bearer token and API key auth. Integrate into CI/CD pipelines. Python SDK coming soon.
Transparent Plan Limits
Free tier with real utility (1,000 rows, 3 datasets/month). No hidden metering. No surprise invoices. Rate limits are enforced in the API — you always know exactly where you stand.
10 industry datasets. Zero setup.
Don't have seed data? Don't need it. Pick a template, set the row count, hit generate. Each template produces statistically realistic data with proper distributions, correlations, and edge cases.
Try templates freeCredit Applications
Banking · 12 columns
Patient Records
Healthcare · 15 columns
Fraud Detection
Finance · 14 columns
Insurance Claims
Insurance · 12 columns
E-Commerce Orders
Retail · 11 columns
Clinical Trials
Pharma · 13 columns
Customer Churn
SaaS · 14 columns
Employee Records
HR · 13 columns
Not random noise.
Engineered data.
Zero PII, Full Utility
Every generated record is mathematically guaranteed to contain no real personal information — while preserving the statistical distributions your models need.
Distribution Preserving
NanoSynthc learns multivariate structure and generates synthetic records that match joint distributions, conditional probabilities, and temporal patterns.
Validation Engine
Every synthetic dataset ships with a fidelity report — KL divergence, correlation matrices, utility benchmarks, and privacy risk scores.
Differential Privacy
Configurable epsilon-delta differential privacy budgets ensure formal, provable privacy guarantees. Mathematical proof, not marketing claims.
Multi-Format Output
Generate tabular data, time series, transaction logs, medical records. Export as CSV, Parquet, JSON, or directly to your data warehouse.
Scenario Generation
Need 10,000 high-risk loan applications? 50,000 rare disease profiles? Generate targeted slices and stress-test edge cases on demand.
Every industry has a data problem.
We solve each one differently.
Train Without Risk
Generate millions of realistic credit applications for fraud detection — zero compliance risk, no 6-month approval cycle.
Research Without Boundaries
Synthetic patient cohorts preserving disease prevalence and demographics — shareable across teams and borders, HIPAA-safe.
Bootstrap Your Models
Only 500 labeled examples? Amplify into hundreds of thousands with controlled augmentation and minority class oversampling.
Stress-Test Everything
Millions of synthetic claims under configurable scenarios: pandemics, market crashes, regional catastrophes.
No Cold-Start Problem
Synthetic purchase histories and demand curves for new products. Launch with day-one personalization.
Accelerate Trials
Simulate patient populations with realistic adverse events and dropout rates before enrolling a single patient.
One photo in. Thousands of training examples out.
Purpose-built infrastructure for computer vision teams. Upload a single photo of a bottle — NanoSynthc strips the background, then re-composites your object across endless positions, angles, lighting conditions, and environments. What starts as one image becomes a production-grade dataset that trains your CNN on scenarios it would otherwise never see.
Upload a single photo
Your object — a bottle, a product, a defect, a component. One image is all we need to start.
Background stripped
Our pipeline cleanly isolates your object from its scene, preserving every pixel of detail and edge.
Infinite scenes composed
We re-render your object across thousands of environments, angles, lighting conditions, and occlusions.
CNN-ready dataset
A labeled, augmented, production-grade training set delivered straight into your training pipeline.
Four steps to synthetic data.
Upload Your Schema
Upload a CSV or define your data schema. We analyze distributions, correlations, and data types automatically.
Configure Generation
Set row count, privacy level, and scenario parameters. Choose standard, high, or maximum privacy noise injection.
Generate & Validate
NanoSynthc generates your dataset and runs fidelity checks — KL divergence, correlation preservation, and privacy distance.
Download & Deploy
Download your synthetic dataset as CSV or connect via API. Use it for model training, testing, and sharing — risk-free.
Integrate with a single API call.
Your team uploads a data schema or sample CSV. NanoSynthc profiles it, learns the distributions, and generates millions of privacy-safe synthetic records — accessible via REST API or direct download. Use it to train your ML models, test your pipelines, and share across teams without compliance overhead.
import requests
# 1. Upload & profile your data
with open("credit_apps.csv", "rb") as f:
profile = requests.post(
"https://api.nanosynthc.ai/api/profile",
files={"file": f},
headers={"Authorization": "Bearer YOUR_KEY"}
).json()
# 2. Generate 100K synthetic records
result = requests.post(
"https://api.nanosynthc.ai/api/generate",
json={
"profile_id": profile["profile_id"],
"num_rows": 100_000,
"privacy_level": "high"
}
).json()
# 3. Download the synthetic dataset
csv = requests.get(
f"https://api.nanosynthc.ai/api/datasets/"
f"{result['filename']}"
).content
print(f"Generated {result['num_rows']} rows")
print(f"Fidelity: {result['validation']"
f"['overall_fidelity_score']}%")
print(f"Privacy: {result['validation']"
f"['privacy_score']}%")Start free. Scale as you grow.
Transparent pricing. No hidden fees. Cancel anytime.
Starter
For individuals exploring synthetic data.
- 1,000 rows per generation
- 3 datasets per month
- Standard privacy level
- CSV export
- Community support
Professional
For teams building production AI models.
- 100,000 rows per generation
- Unlimited datasets
- All privacy levels
- CSV, Parquet, JSON export
- Fidelity reports
- API access
- Priority support
Enterprise
On-premise deployment for regulated industries.
- Unlimited rows
- On-premise / private cloud
- Custom LLM integration
- SOC2 & ISO 27001 ready
- SSO & RBAC
- Dedicated engineer
- SLA guarantee
Common questions
Stop waiting for data.
Start generating it.
Create your free account and generate your first synthetic dataset in under 2 minutes.