Seeded from your schema
Provide a schema, a handful of real examples, or a written specification. The generator conditions on what you give it rather than inventing a shape.
NXAARA Platform service
Generate the training data you are not allowed to copy
When the real data is sensitive, scarce or badly imbalanced, Synthetic Data Studio produces a usable substitute — and then checks it for leakage and utility before you are allowed to train on it.
Synthetic data is only useful if somebody checks it
Generating plausible-looking records is easy. Generating records that are safe to use and actually improve a model is not, and the gap between those two things is where most synthetic data projects quietly fail.
Two failure modes matter. The first is leakage: the generator memorises a real record and reproduces it closely enough that a person could be re-identified. The second is uselessness: the synthetic set is statistically bland, the model trained on it learns a simplified version of reality, and performance on real inputs drops.
Studio runs checks for both before a dataset is marked usable. Nearest-neighbour distance against the source data catches records that are suspiciously close to a real one. A train-on-synthetic, test-on-real comparison tells you whether the data carries genuine signal or merely looks correct.
A dataset that fails either check is flagged, and the report says which records or which columns caused it. You can regenerate with different parameters, or accept it with a documented justification — but you cannot walk past the finding without noticing it.
- Text generation — instructions, dialogue, support conversations, structured extraction pairs
- Tabular generation — records that preserve distributions and cross-column relationships
- Document generation — forms, invoices and reports with realistic layout variation for extraction training
- Class balancing — targeted generation for the rare cases your real dataset barely contains
- Privacy checks — nearest-neighbour distance and re-identification risk scoring
- Utility checks — train-on-synthetic, test-on-real, so you know the data carries signal
Capabilities
What Studio generates and verifies
Generation and verification are one workflow here, not a tool plus a separate audit somebody remembers to run later.
Targeted class balancing
Fraud, defects, rare diagnoses, edge cases — generate specifically the classes your real data barely contains, without duplicating the few real examples you have.
Arabic and bilingual data
Generate Arabic and mixed Arabic-English data, including Gulf dialect variation, for a domain where good open training data is genuinely scarce.
Privacy scoring
Nearest-neighbour distance against source records, plus re-identification risk, reported per dataset with the problem records identified.
Utility scoring
A model trained on the synthetic set is tested against real held-out data. If the gap is large, the report says so plainly.
Versioned handoff
An approved dataset is versioned and passed straight to Model Foundry, carrying its checks with it as part of the lineage record.
How it works
From specification to an approved dataset
The checks come after generation and before training — the order is the entire point.
Define the target
Describe the schema, the classes you need and how many records. Attach real examples if you have them to condition on.
Generate a sample
Produce a small batch first and read it. Most specification problems are obvious in fifty records and expensive in fifty thousand.
Generate the set
Run the full job once the sample looks right. Large sets generate on GPU Cloud capacity.
Verify
Privacy and utility checks run automatically and produce a report you can hand to a reviewer.
Approve and use
An approved set is versioned and available to Model Foundry. A failed one tells you exactly what to change.
When synthetic data is the right answer, and when it is not
Synthetic data is a strong answer to three problems: the real data exists but you are not permitted to move or share it; the real data exists but the class you care about appears eleven times in it; or the real data does not exist yet because the product has not launched.
It is a weak answer to one problem in particular — not having thought carefully about the task. A generator conditioned on a vague specification will produce a large volume of confidently wrong examples, and a model trained on those will be confidently wrong in exactly the same way.
The honest framing is that synthetic data augments real data far more reliably than it replaces it. Teams that mix a well-checked synthetic set with whatever real data they have consistently do better than teams doing either alone.
| Data types | Text, instruction pairs, dialogue, tabular records, structured documents |
|---|---|
| Languages | English, Arabic including Gulf dialect variation, and mixed bilingual |
| Conditioning | Schema definition, real example seeds, or written specification |
| Privacy checks | Nearest-neighbour distance, re-identification risk score, flagged record list |
| Utility checks | Train-on-synthetic, test-on-real comparison against a real held-out set |
| Output formats | JSONL, CSV, Parquet, rendered document images with labels |
| Handoff | Direct versioned handoff to Model Foundry with checks attached |
| Retention | Source seed data is used for the job and is not retained beyond it unless you ask us to |
Where it is used
Where this earns its place
Regulated data that cannot leave
Health, banking and government records that a vendor is not permitted to see. Generate a substitute inside the boundary and train on that instead.
Rare classes
Fraud patterns, equipment failures, unusual clinical presentations — the cases that matter most and appear least in your real dataset.
Arabic training corpora
Good Arabic instruction data is scarce, particularly in Gulf dialect. Generating it is often faster and cleaner than trying to license it.
FAQ
Common questions
How do I know the synthetic data has not just memorised real records?
Every dataset is scored for nearest-neighbour distance against the source records it was conditioned on. Records that sit suspiciously close to a real one are flagged individually, and the report identifies them so you can inspect and remove them rather than taking the score on trust.
Will a model trained on synthetic data actually work on real inputs?
That is precisely what the utility check measures. We train on the synthetic set, test against real held-out data, and report the gap. If the gap is wide, the dataset is not ready and the report says which parts are weak.
Can I generate data without giving you any real data at all?
Yes. You can condition purely on a schema and a written specification. The output is usually less faithful than a seeded generation, but for structure, format and edge-case work it is frequently sufficient.
Is synthetic data acceptable to a regulator?
It depends entirely on the regulator, the sector and how the data is used, and we will not pretend otherwise. What we can do is give you the privacy and utility evidence in a form a reviewer can read. Whether that satisfies a specific obligation is a question for your compliance function and your counsel.
What happens to the real data I use as seeds?
It is used for the generation job and is not retained afterwards unless you explicitly ask us to keep it in your project storage. It is never used to train base models or shared across accounts.
Generate a sample set and read the report
Start with a few hundred records against a real schema. The privacy and utility report will tell you more about feasibility than any sales conversation.