A hybrid engine combining GAN, Diffusion, and Transformer, four validation gates,
and deployment from on-premise to cloud — take a detailed look inside RealDataEcho.
Distributions, fine-grained patterns, and time-series relationships — the three hard problems of structured data synthesis
are each assigned to the best-performing generative model, and a gating layer automatically selects the optimal combination.
Compared with single-model synthesis, distribution similarity improves by +18% and time-series fidelity by +24%. Domain-specific weights are tuned automatically.
3-engine unified gating →Generator–discriminator adversarial training faithfully reproduces the complex joint distribution of the source data.
The noise-to-data reverse process restores subtle correlation structures and inter-column dependencies without loss.
Self-attention learns temporal dependencies and long-range patterns to naturally synthesize transaction, log, and event data.
The eight column types you meet in finance, healthcare, and marketing — automatically recognized and routed within a single pipeline.
Continuous variables such as amounts, durations, and scores. Preserves distribution shape and even outliers.
Discrete variables such as regions, grades, and codes. Handles high-cardinality categories reliably.
Temporal variables such as transaction and enrollment dates. Learns seasonality and day-of-week patterns automatically.
Sequences such as transactions, logs, and sensor data. Preserves multivariate long-range dependencies.
Multiple tables linked by foreign keys. Synthesizes while maintaining referential integrity.
Churn, consent, and similar flags. Automatically corrects class imbalance.
Short strings such as addresses, memos, and product names. Synthesized token by token at the semantic level.
Latitude/longitude, administrative districts, and more. Anonymizes while preserving spatial distribution.
Not a single de-identification technique — a five-stage defense system runs from pre-detection to post-validation. Each layer has its own independent pass criteria, and if even one stage fails, the synthetic output never ships.
Beyond "does it look like real data" to "does a model trained on it perform as well as one trained on the real thing" — measured with objective metrics, and shipped only on a pass.
Measures per-column distribution agreement with the KS test and Wasserstein distance.
Distance between Pearson/Spearman correlation matrices — how well inter-column dependencies are preserved.
Model trained on synthetic data vs. model trained on the original — AUC/F1 equivalence testing.
Quantifies source exposure risk with MIA and DCR (distance to closest record).
From air-gapped networks to cloud SaaS — on-premise, where your data never leaves, is the most popular choice.
Installed directly on your internal servers. Not a single line of data leaves your network.
Isolated deployment in your dedicated VPC. Cloud efficiency and security together.
Ready to use as SaaS. For fast validation in PoC, research, and development.
The 3-engine hybrid architecture and four validation gates — all in a single 30-minute demo.