Realistic, privacy-safe datasets that preserve the statistics of the real thing without exposing a single real person — for testing, training and rare-case coverage.
When real data is scarce, sensitive or badly imbalanced, it becomes a bottleneck: models cannot train, teams cannot share, and whole projects stall on a privacy sign-off that never comes. Synthetic data breaks that deadlock by generating records that preserve the statistical structure of the real thing without containing a single real individual.
The value is not just privacy. Synthetic generation lets you deliberately create the rare cases that real datasets barely contain — the fraud pattern that happens once in ten thousand transactions, the edge case that breaks your model in production. You can balance skewed classes, stress-test against scenarios you have never actually seen, and populate lower environments without copying production data into them.
Done carelessly, though, synthetic data can leak. A generator that memorises its training set reproduces real people. That is why measurement matters as much as generation: we quantify re-identification risk and downstream utility, and document both, so you know exactly what you are shipping.
We begin from your real schema and the questions your models need to answer, then choose a generation method to match — statistical models for simple tabular data, deep generative models for complex or high-dimensional data, and specialised approaches for time-series and text. The goal is fidelity where it counts, not synthetic data that merely looks plausible.
Every dataset we deliver comes with two measured properties: a privacy score that quantifies re-identification risk, with differential-privacy guarantees applied where you need them, and a utility score that shows how a model trained on the synthetic data performs against a real hold-out. You see both before you rely on anything.
Populate staging and QA environments with realistic data that contains no real customers.
Generate the fraud, fault or edge cases your real data barely contains.
Share datasets across borders and teams without moving PII.
Correct skewed datasets so models stop ignoring minority classes.
Train an initial model before enough real data even exists.
Create controlled scenarios to stress-test fraud and anomaly systems.
We agree the target schema, the fidelity that matters and the privacy bar the data must clear.
We study your real data's structure and statistics, and set aside a real hold-out for later validation.
We choose and configure the generation method to fit the data type and constraints.
We generate the synthetic dataset and tune it toward the fidelity you need.
We measure re-identification risk and downstream utility against the hold-out.
We deliver the dataset, the pipeline and a documented privacy and utility report.
We select the generation approach from the data type and fidelity you need, and always pair it with privacy and utility measurement.
We measure re-identification risk directly and apply differential-privacy guarantees where required, then document the residual risk so nothing is taken on faith.
We validate fidelity and downstream performance against a real hold-out set before delivery, and show you the numbers.
Tabular, time-series and text are well supported; images and multimodal on request.
Usually it augments rather than replaces — closing gaps, balancing classes and unblocking environments where real data cannot go.
Compact, efficient models that run at the edge — low latency, full privacy.
Benchmark, stress-test and adversarially probe models before they ship.
Cooperating agents with tool-calling, handoff and MCP/A2A protocols.
Book a 30-minute call. We will tell you honestly whether we can help.
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