Synthetic Data Infrastructure

The training data your models can't find

Distribution-faithful synthetic data for frontier AI. Clinical, financial, and defense domains — generated at scale, delivered clean.

Request a sample How it works
100%
Rare event recall
90%
Lower compute cost
34x
Parameter efficiency
0
Patient records exposed
The Problem
AI training data is running out
Public data is exhausted. Domain-specific data is locked behind regulation, cost, and scarcity. Your models plateau without it.
Challenge

Synthetic generators collapse

GANs and diffusion models lose rare events after 2-3 generations. Tail distributions degrade progressively. Your models inherit this drift.

Challenge

Real data can't leave the building

HIPAA, ITAR, GDPR, and the EU AI Act make direct data licensing a legal minefield. The most valuable data is the least accessible.

Maxxor

Preserves what others lose

Energy-based generation learns the true statistical landscape — including tails, correlations, and rare events. No mode collapse. Provable fidelity bounds.

Maxxor

New data, no exposure

Synthetic samples that are statistically representative but contain zero real records. Verifiable through re-identification testing. Privacy by construction.

Pipeline
From protected data to training-ready synthetic
Three steps. No GPUs required. No patient records leave the perimeter.
01 — INGEST

Source Data

Clinical EHR, financial time series, RF signals, sensor telemetry. Licensed from domain partners under strict access controls.

02 — LEARN

Maxxor Engine

Energy-based model learns the full joint distribution — including rare events, tail behavior, and temporal dynamics. CPU-native on Intel Granite Rapids.

03 — GENERATE

Synthetic Output

Unlimited non-overlapping synthetic datasets. Validated with KL divergence and Wasserstein distance. Delivered HIPAA-clean.

Domains
Data where it matters most
We focus on domains where scarcity creates the highest value and the biggest barrier to model performance.
Healthcare & Clinical
Synthetic patient cohorts, rare disease augmentation, clinical trial simulation. HIPAA-clean with provable privacy guarantees.
AVAILABLE NOW
Financial Time Series
Synthetic order books, volatility regimes, stress-test scenarios. Tail events preserved at 100% recall for risk modeling.
AVAILABLE NOW
RF & Spectrum
Synthetic threat signatures, jamming patterns, spectrum occupancy maps for defense and telecom R&D.
AVAILABLE NOW
Cybersecurity
Synthetic attack vectors, zero-day scenario data, privacy-safe network traffic replicas for SOC training.
Q2 2026
Industrial IoT
Synthetic failure signatures, anomaly-enriched sensor streams, edge-case augmentation for predictive maintenance.
Q2 2026
Autonomous Systems
Synthetic edge-case scenarios, weather variations, multi-sensor fusion training data for AV and robotics.
Q3 2026
Differentiation
Energy-based vs. everything else
Dimension GANs / Diffusion Maxxor
Distribution fidelity Progressive drift after 2-3 generations Full joint distribution preserved
Rare event recall Mode collapse drops rare patterns 100% — energy minima encode rare states
Compute GPU clusters — $10K-$100K/mo CPU-native — 90% lower cost
Validation Visual inspection / FID scores KL divergence + Wasserstein bounds
Parameter efficiency 1B+ parameters for complex domains 34x more efficient

Your models need better data. We make it.

Request a sample synthetic dataset from any active domain. See the fidelity metrics for yourself.

Request a sample Partnership inquiry