Nano
Synthc
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.
Thousands of companies want to build AI.
They can't get the data.
In healthcare, using real patient data for model development requires years of IRB approvals and data sharing agreements. In finance, regulators prohibit using real customer records for testing. The result: AI projects stall, models underperform, and innovation dies in the compliance review.
NanoSynthc eliminates the data bottleneck. We generate synthetic datasets that preserve every statistical property of your real data — distributions, correlations, temporal patterns, edge cases — without containing a single real person's information. Your AI gets the data it needs. Your compliance team sleeps at night.
See a live demoPrivacy by Math
Differential privacy guarantees — not policy promises.
Statistical Fidelity
99%+ distribution match validated per dataset.
Compliance Ready
HIPAA, GDPR, and sector-specific regulations covered.
Unlimited Scale
Generate millions of records in hours, not months.
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, correlations, and edge cases your models need.
Distribution-Preserving Generation
NanoSynthc learns the multivariate structure of your real data and generates synthetic records that match joint distributions, conditional probabilities, and temporal patterns.
Statistical Validation Engine
Every synthetic dataset ships with a fidelity report — KL divergence, correlation matrices, utility benchmarks, and privacy risk scores. You don't trust us; you trust the math.
Differential Privacy Guarantees
Configurable epsilon-delta differential privacy budgets ensure formal, provable privacy guarantees. Not marketing claims — mathematical proof that re-identification is impossible.
Multi-Format Output
Generate tabular data, time series, transaction logs, medical records, or free-text narratives. Export as CSV, Parquet, JSON, or directly to your data warehouse.
Conditional & Scenario Generation
Need 10,000 high-risk loan applications? 50,000 rare disease patient profiles? Generate targeted slices and stress-test edge cases that barely exist in your real data.
Every industry has a data problem.
We solve each one differently.
You need millions of credit applications to train fraud detection models, but regulators prohibit using real customer data for development and testing.
NanoSynthc generates 1 million realistic credit applications — income distributions, credit scores, default patterns — all statistically faithful, zero real customers.
Models trained on NanoSynthc data achieve within 1.5% accuracy of real-data baselines, with zero compliance risk and no 6-month data governance approval cycle.
HIPAA, GDPR, and institutional review boards make it nearly impossible to share patient data across research teams, hospitals, or borders.
Generate synthetic patient cohorts that preserve disease prevalence, treatment outcomes, and demographic distributions — shareable with any team, anywhere.
Research timelines shrink from years to weeks. Multi-site studies become possible without a single data sharing agreement.
You have a brilliant model architecture but only 500 labeled examples. You can't ship a product on 500 rows.
NanoSynthc amplifies your seed data into hundreds of thousands of training examples with controlled augmentation and minority class oversampling.
Go from prototype to production-grade model without waiting 18 months to collect enough real-world data.
Actuarial models need to perform under extreme scenarios (pandemics, market crashes) — but those events produce tiny, sparse datasets.
Generate millions of synthetic claims under configurable stress scenarios: 3x hospitalization rates, 40% market drops, regional catastrophes.
Scenario planning backed by realistic synthetic data instead of spreadsheet guesswork.
New product lines have no historical data. Recommendation engines and demand forecasting models can't train on products that don't exist yet.
Generate synthetic purchase histories, browsing patterns, and demand curves based on analogous product categories and market signals.
Launch with day-one personalization and accurate demand forecasts — no cold-start problem.
Designing clinical trial protocols requires simulating patient populations — but access to historical trial data is locked behind institutional silos.
Generate synthetic trial cohorts with realistic adverse event profiles, dropout rates, and endpoint distributions for protocol simulation.
Optimize trial design before enrolling a single patient. Reduce protocol amendments and accelerate time-to-approval.
From real data profile to synthetic dataset.
Data Profiling
We analyze your real dataset's schema, distributions, correlations, and edge cases. We identify sensitive fields and define privacy constraints.
Model Training
NanoSynthc trains a generative model (VAE, GAN, or diffusion-based) on your data structure — learning patterns, not memorizing records.
Privacy Calibration
We configure differential privacy budgets and run membership inference attacks against the model to verify that no real record can be reconstructed.
Synthetic Generation
Generate any volume of synthetic records — thousands to millions — with configurable parameters for class balance, scenario conditions, and temporal coverage.
Fidelity Validation
Every dataset is delivered with a statistical fidelity report: distribution comparisons, correlation preservation scores, and ML utility benchmarks.
Delivery & Integration
Synthetic data is delivered in your preferred format with API access for on-demand generation. Optional: on-premise deployment for continuous generation.
Privacy is not a feature.
It's the entire point.
NanoSynthc doesn't anonymize your data — anonymization can be reversed. We generate entirely new data that has never existed, based on learned statistical properties. There is no real person behind any synthetic record.
Every dataset undergoes automated membership inference attacks and nearest-neighbor distance checks to verify that no record in the output maps back to any record in the input. We deliver the privacy proof alongside the data.
Flexible pricing for every stage.
Start with a single dataset. Scale to an on-premise platform. We grow with you.
Per-Dataset Generation
Pay per synthetic dataset generated. Volume discounts for recurring generation needs.
Platform License
Annual license for on-premise or private cloud deployment. Generate unlimited datasets internally.
Managed Service
We handle everything — profiling, generation, validation, delivery. You get production-ready synthetic data, we handle the pipeline.
Stop waiting for data. Start generating it.
Send us a sample schema or describe your dataset. We'll generate a free proof-of-concept synthetic dataset within 48 hours.