Transform your sensitive documents into safe, high-utility training assets for AI and ML, with zero real PII remaining.
Turn your contracts, reports, medical records, and financial documents into compliant training datasets that preserve the patterns and domain knowledge your models need, with zero real PII remaining.
Your proprietary data is your strongest competitive advantage for AI. But training a model on raw sensitive documents creates legal exposure and data leakage risk. Hexagone AI transforms your data into safe training assets that preserve full utility.
When you fine-tune a language model or train a machine learning pipeline, the model memorizes patterns from its training data. If that data contains real client names, contract values, or personal details, the model can reproduce them during inference. This is not a theoretical risk: research has demonstrated that language models can output verbatim training examples when prompted.
Simple approaches like redacting text with [REMOVED] tags destroy the very patterns that make your data valuable. A sentence like "The [REMOVED] of [REMOVED] signed a [REMOVED] for [REMOVED]" teaches the model nothing useful.
Hexagone AI replaces every sensitive element with a realistic synthetic equivalent that maintains the semantic structure, formatting, and relationships of the original document. The model learns the same patterns (how contracts are structured, how medical reports reference conditions, how financial summaries present data) without memorizing any real information.
The synthetic replacement is consistent across an entire document set: if "TechVision Solutions" becomes "DataBridge Analytics" in one document, it becomes "DataBridge Analytics" in every document where it appears. This preserves cross-document relationships that are essential for training.
Preparing domain-specific training datasets from internal documents for LLM fine-tuning, NER models, or classification systems.
Running statistical analysis, topic modeling, or trend detection across client data without exposing individual identities.
Creating anonymized versions of proprietary datasets for academic collaboration or open-source contribution.
Ensuring AI training pipelines meet regulatory requirements with full audit trails and re-identification testing reports.
Every anonymized dataset produced by Hexagone AI undergoes automated re-identification testing. Our proprietary process (developed with university researchers) runs linkage attacks, membership inference, and PII leakage scoring to verify that no original data can be recovered. You receive a compliance report for your records, ready for auditors.
We can help you unlock your sensitive data safely. Book a call and we will walk you through exactly how this would work for your organization.
Configure exactly what each audience sees. One source document, multiple tailored outputs, with zero over-exposure.
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