Services

Synthetic Data Generation

Clinical-grade synthetic patient records that pass statistical validation against real-world distributions. Built for research, model training, and regulatory submissions.

The Problem

Off-the-shelf synthetic data scores 65–75% on Train-Synthetic-Test-Real benchmarks. Researchers reject it. Regulators question it. Your models trained on it underperform. Clinical AI deserves better.

Our Pipeline

Stage 1

Structural Generation

We begin by modeling clinically accurate patient trajectories from the ground up. Each synthetic patient is assigned realistic comorbidity distributions drawn from epidemiological baselines, medication regimens with interaction-aware sequencing, and lab value progressions that follow known physiological curves. The result is a longitudinal record that looks and behaves like a real patient chart — before any real data is involved.

Stage 2

GAN / Diffusion Correction

Rule-based generators get distributions wrong — rare events are under-represented, correlations between variables drift, and temporal patterns flatten out. We correct this using GAN and diffusion models trained on MIMIC-IV and eICU. These models learn the subtle statistical signatures of real clinical data and apply corrections that rule-based engines cannot produce. The output passes marginal distribution tests that basic synthetic generators fail.

Stage 3

LLM Enrichment

Structured records are enriched with clinical free-text: admission notes, discharge summaries, radiology reports, and procedure narratives. These are generated by fine-tuned language models conditioned on the structured data, ensuring the notes are clinically consistent with the underlying record. Hallucination detection runs at generation time — flagging and regenerating any text that contradicts the patient's data. The final documents read like real clinical documentation.

Stage 4

6-Layer Validation

Every dataset passes a six-layer validation suite before delivery: statistical fidelity (marginal and joint distributions), clinical pathway accuracy (are treatment sequences medically plausible), temporal consistency (do values progress logically over time), TSTR utility (do models trained on synthetic data perform on real holdouts), NLP coherence (do notes align with structured data), and differential privacy audit (no record is a statistical proxy for any real patient).

Use Cases

Pharma R&D

Clinical trial simulation, drug interaction modeling, and synthetic cohort generation. Accelerate early-phase research without touching patient data.

Model Training

Training data for clinical NLP, diagnostic AI, and decision support systems. Balanced cohorts, rare disease oversampling, and edge-case generation on demand.

Regulatory & Compliance

De-identified datasets for regulatory submissions, internal audits, and compliance testing. Each dataset ships with a documented privacy audit trail.

Formats & Specifications

Output Formats

FHIR R4 (JSON / XML)
HL7 v2 / v3
CSV / Parquet
Custom schemas on request

Volume

1K records (pilot / validation)
100K–1M (standard research)
10B+ (enterprise / training runs)

Configurable Parameters

Demographics & population mix
Conditions, diagnoses, acuity
Temporal range (1 day to 20 years)

Quality Guarantee

Every dataset ships with a validation report: TSTR scores, distribution comparisons, privacy audit, and clinical pathway verification. If it doesn't pass our 6-layer suite, it doesn't ship.

Ready to build with clinical-grade data?

Tell us your use case. We'll scope a dataset that meets your requirements.

Start the Conversation