Synthetic data to train smarter AI
Generate realistic, privacy-safe datasets for healthcare, autonomous vehicles and any domain where real-world data is scarce.
Or email us at contact@epineone.com
Trusted by AI teams in regulated industries
Platform
Everything you need to train models without real-world data
From the first proof-of-concept to production training pipelines — Epineone gives you the data, augmentation and privacy controls to ship better models, faster.
Realistic data generation
ML-driven simulation produces datasets that look, behave and distribute like the real world — images, sensor streams, tabular records and more.
Data augmentation
Expand thin datasets into diverse, balanced training corpora that improve model accuracy across edge cases and rare scenarios.
Privacy-preserving
No real PHI or PII ever leaves your environment. Synthetic outputs are statistically faithful but contain no identifiable records.
High-performance compute
The engine scales from a single 3D scene to millions of synthetic samples in hours, not weeks — on cloud or on-prem infrastructure.
Domain coverage
Pre-tuned generators for medical imaging, EHR records, LiDAR/point clouds, driving scenes, financial transactions and more.
On-prem or private cloud
Run the engine inside your VPC or air-gapped environment so regulated data never touches the public internet.
Developer-first SDK
Generate a dataset in five lines.
Define your schema or seed with a real sample, pick a domain generator, and stream synthetic records straight into your training pipeline. Reproducible, versioned and audit-ready.
import { Epineone } from "@epineone/sdk";
const client = new Epineone({ apiKey: process.env.EPINEONE_KEY });
const dataset = await client.datasets.generate({
domain: "medical-imaging/chest-xray",
samples: 50_000,
privacy: "differential",
});Platform telemetry
What our generation engine looks like in production
Real signals from customer pipelines — sample throughput, downstream model lift, and the quality dimensions our applied ML team monitors on every release.
Synthetic samples generated
Cumulative monthly output across all customer workspaces (millions).
Downstream model accuracy
F1 score with real-only data vs. real + Epineone synthetic.
Generation latency (p50 / p95)
Average per-sample generation time across the fleet, last 24h (ms).
Dataset modality mix
Share of generated samples by modality this quarter.
Quality scorecard
Internal evaluation across six axes, scored 0–100 per release.
Privacy guarantee
Differential privacy, by default.
Every dataset ships with a signed model card and a tunable privacy budget — so you can prove to auditors that no real record can be reconstructed.
Train AI on data you couldn't get before.
Start with 10,000 free synthetic samples. No credit card, no real data required.