Train and validate models without exposing personal data
Analytics and data science need volume and realistic patterns. Gigantics prepares them from your data without personal information reaching the analysis environment.
Models ask for real data and the risk grows with it
For data, analytics and CDO teams.
Direct access to production
To train, teams end up working on production copies with full personal data.
Unrepresentative samples
Trimming or masking by hand alters the patterns the model needs to learn.
Inconsistent retraining
Each extraction produces different values and the dataset versions stop being comparable.
Governance of the training data
Knowing where each dataset came from and what it contained becomes impossible once copies multiply.
Real volume and patterns, without the personal data
Know what's personal before extracting
Every field with personal information is identified and classified by risk.
Evidence of the origin of each dataset
Signed reports document what was classified, who approved it and when.
Keep the pattern, not the data
Synthetic data by field type, random shuffling of values that preserves the distribution, and synthesis to extend volume.
Versioned datasets for every training run
Direct delivery to the analysis environment or download in JSON, CSV or SQL.
Questions from data and AI teams
What's the difference between anonymizing and synthesizing for a model?
Anonymizing starts from your real data and replaces its values. Synthesizing generates new records from the schema, and lets you extend volume beyond the original to train or test at scale.
Is the data distribution preserved?
Random shuffling changes the order of the values within the dataset while keeping its original distribution, useful when the analysis depends on the properties of the set rather than which value belongs to which record.
Can I retrain with datasets consistent across versions?
Yes. With a dictionary, successive generations keep the same substitution mapping, so a model that is retrained periodically works with internally consistent data.
How does this relate to the EU AI Act?
Article 10.5 allows processing special categories of data to correct bias in high-risk systems only if it cannot be achieved with synthetic or anonymized data. Under the schedule approved in July 2026, that obligation becomes enforceable from December 2, 2027. The specific assessment belongs to your legal team.
Bring your models useful data, not personal data
We show you how to prepare a training dataset on an architecture like yours.