Sanitize Training Data
With privacy-preserving anon. and full referntial integrity across multiple modes of data
.avif)
.avif)
TRUSTED BY
Buying or selling Enterprise data?
PII is buried in raw 
source data
Sensitive data may 
poison model training
Simple scrubbing breaks entity coherence
Selling datasets with 
PII exposes risk
Runtime AI Data
Control for Training Data
Sanitize before delivery
Detect and replace sensitive values inside the supplier's own data preparation workflow, before anything ships.
Preserve Referential Integrity
Keep structure, context, and relationships instead of stripping out everything that looks sensitive.
Cross-border Transfer
Keep structure, context, and relationships instead of stripping out everything that looks sensitive.
Choose How to Protect Your Data
An opaque reference, reversible with authority. Use when the original must be recoverable.
Partially visible, such as a retained last four. Use when a reviewer needs the field, not the value.
Removed entirely. Use when the value must not appear in the output in any form.
A realistic stand-in. Use when the data has to stay usable and look natural for training.
A realistic stand-in. Use when the data has to stay usable and look natural for training.
Sanitize Any Training Data, in Any Modality
Multi-modal detection
Entity coherence
One person, one stand-in, across every file, format, and source system.
Repeatable AI pipelines
Lock the entity spec once. Every batch runs the same way, at intake cadence.
Context-preserving tokenization
Sanitize sensitive data before it reaches short or long-term memory.
Control data. De-risk compliance.
De-identify unstructured data to simplify compliance. Meet regulatory needs.
Compliance audit
Complete audit trail of every data access and AI interaction.
Lorem lipsum
Lorem lipsum
Lorem lipsum
Lorem lipsum
