01
Detect
Identify PII and sensitive entities
Analyze documents and datasets to locate names, addresses, identifiers, financial information, health information and other entity categories defined by the project.
MULTILINGUAL DATA ANONYMIZATION FOR AI
Protect sensitive data. Preserve its value for AI.
Pangeanic detects and transforms personally identifiable information and other sensitive data so organizations can use real documents and enterprise data more safely in AI systems, analytics, information sharing, evaluation and knowledge retrieval.
Our multilingual anonymization technology identifies PII and other sensitive entities and applies configurable protection policies according to how the data will be used. Information can be masked, pseudonymized or removed while preserving the context and structure required for downstream processing.
PRIVACY BEFORE THE MODEL
Some of your most valuable AI data is also the hardest to use
Clinical records, legal files, insurance claims, financial documents, customer communications and internal knowledge repositories contain exactly the information that can make an AI system more useful. They can also contain personal, confidential or regulated information that should not move unprotected into downstream systems.
Anonymization reduces that exposure before data is used for model training, fine-tuning, evaluation, analytics, semantic search or retrieval augmented generation. The objective is not simply to delete information. It is to protect what identifies or reveals sensitive information while retaining as much useful context as the task allows.
Pangeanic connects this privacy layer with AI Data Services , model evaluation and Sovereign AI systems , so privacy becomes part of the data pipeline rather than a final remediation step.
FROM DETECTION TO USABLE DATA
Anonymization is a data policy, not a simple filter
Sensitive information should not always be treated in the same way. The right transformation depends on the type of data, the level of risk, the business context and what the organization needs to do with the information afterwards.
THE OBJECTIVE
Protect information that identifies people or reveals sensitive data without unnecessarily destroying the structure, context and operational value of the source.
02
Decide
Apply the appropriate protection policy
Different entity types can receive different treatment according to the use case, sensitivity level and the organization’s data governance requirements.
03
Transform
Mask, pseudonymize, replace or suppress
Detected information can be replaced, labeled, obscured or removed while retaining the consistency and contextual information needed by downstream systems.
04
Validate
Verify that protection is sufficient
Workflows can include quality controls and human review where the domain, risk profile or acceptance criteria require an additional verification layer.
05
Use
Move protected data into downstream AI workflows
Protected data can then support analytics, enterprise search, model evaluation, RAG, training, fine-tuning or other internal AI processes.
ONE ENGINE, DIFFERENT POLICIES
Discuss your data requirements A clinical record, a legal contract and a dataset used for model evaluation do not require the same anonymization strategy. Pangeanic adapts the protection policy to the intended use of the data.
PROTECTION METHODS
Sensitive data does not always need to disappear
The right transformation depends on what the organization needs to preserve. Some workflows require readable, realistic data. Others only need entity structure, document layout or the complete removal of sensitive information.
01 Pseudonymization
Replace identity while preserving natural context
Substitute identifiable values with realistic alternatives when documents need to remain readable and structurally useful for analytics, testing or AI workflows.
ORIGINAL
Maria Gonzalez submitted a claim in Madrid on May 14.
↓ PROTECTEDLaura Martin submitted a claim in Seville on May 14.
Useful when: downstream systems need natural language and realistic document structure.
02 Structured replacement
Preserve the entity type, not the original value
Replace sensitive information with structured labels so models and analysts can retain semantic information about what was present.
ORIGINAL
Carlos Ruiz works for Acme Legal.
↓ PROTECTED[PERSON_01] works for [ORG_01].
Useful when: entity classes and relationships still matter to the task.
03 Masking
Conceal the value while retaining its position
Obscure sensitive information when a document or interface must preserve the presence and approximate structure of the original field.
ORIGINAL
Account number: ES91 2100 0418 4502
↓ PROTECTEDAccount number: **** **** **** 4502
Useful when: layout, field presence or partial operational context must remain visible.
04 Suppression
Remove information that no longer adds value
Delete sensitive information completely when retaining any representation of that value is unnecessary for the intended use.
ORIGINAL
Contact: person@company.com
↓ PROTECTEDContact:
Useful when: the sensitive value has no downstream analytical or operational purpose.
POLICIES CAN BE COMBINED
Design an anonymization policy Different entities can follow different rules inside the same document
A single workflow can pseudonymize names, remove email addresses, replace organizations with entity labels and partially mask account identifiers. The policy is defined according to data sensitivity and downstream use.
ENTERPRISE AI USE CASES
When the data exists, but cannot safely move into AI yet
Many organizations already have the documents, conversations, case files and knowledge repositories they need. The difficulty begins when those assets also contain personal, confidential or regulated information that should not reach the next system unprotected.
01 ENTERPRISE RAG
Connect internal knowledge without turning retrieval into a data leak
Contracts, emails, support cases, reports and internal repositories can make enterprise assistants dramatically more useful. They can also expose names, identifiers, financial information and other sensitive data to retrieval and generation layers.
Typical question
Explore Sovereign AI systems → “Can we connect our internal documents to an AI assistant without sending raw PII to the model?”
02 TRAINING & EVALUATION
Turn real operational data into usable AI material
Production data is often more representative than synthetic examples, but it may contain sensitive information. Anonymization helps prepare realistic datasets for training, fine-tuning, benchmarking and model evaluation while reducing unnecessary exposure.
Typical question
Explore AI Data Services → “We have thousands of real conversations and documents. How can we use them to train or evaluate a model?”
03 HEALTHCARE
Protect patient identity while preserving clinical context
Clinical notes, medical reports and healthcare records contain information that can support research, analytics and AI systems together with identifiers that can reveal patient identity.
Common requirement
Remove or transform identifiers without destroying symptoms, treatments, temporal relations or clinically relevant context.
04 LEGAL
Reuse contracts and case files without retaining unnecessary identities
Legal documentation combines people, companies, addresses, identifiers and sensitive facts with structures that must remain meaningful for search, classification, review or specialized model development.
Common requirement
Protect identifiable parties while preserving relationships between actors, dates, events and legal content.
05 BANKING & INSURANCE
Analyze real business cases without propagating sensitive financial data
Claims, policies, KYC documents, applications and financial communications contain patterns valuable for automation and AI alongside information that should not be replicated into downstream systems.
Common requirement
Build realistic working or evaluation datasets while reducing the presence of identifiable customer and account data.
06 PUBLIC SECTOR
Reuse public administration data while separating identity from value
Case files, decisions, citizen submissions and administrative documents can support research, interoperability, search and AI services when personal information is handled with appropriate protection.
Related experience
Pangeanic contributed to MAPA, a European project focused on multilingual anonymization for public administrations.
THE PATTERN IS THE SAME
Discuss your use case The data contains value. Identity is not always part of that value.
The right policy depends on what the organization needs to preserve, what must be protected and which system will receive the data afterwards.
MULTILINGUAL PRIVACY ENGINEERING
Explore the MAPA project →
Privacy has geography. Sensitive data has language.
Personal information does not follow one universal pattern. Names, addresses, identity documents, account references, healthcare terminology and administrative identifiers change across countries, languages and document types.
For international organizations, anonymization therefore requires more than applying the same rules to translated content. Detection models, entity taxonomies and transformation policies must reflect the linguistic and documentary conventions of the data being processed.
Pangeanic combines multilingual data operations, entity recognition, configurable transformation and human validation to support privacy preserving AI workflows across languages and domains.
PUBLIC EVIDENCE
MAPA: multilingual anonymization across 24 EU languages
Pangeanic coordinated MAPA, the Multilingual Anonymisation Toolkit for Public Administrations, combining annotated data, Named Entity Recognition, transformation policies and evaluation for administrative, legal and medical documents.
24 official EU languages
3 initial document domains
MAPA multilingual privacy infrastructure
TALK TO US ABOUT YOUR DATA
Have valuable data you still cannot safely use in AI?
Let us assess what needs to be protected, what information must remain useful and how anonymization can fit into your RAG, training, evaluation or private AI workflows.
PII & sensitive data Multilingual documents Training & evaluation Enterprise RAG Private deployment
INITIAL PROJECT REVIEW
Tell us what you need to do with your data
A short description of your use case will help us route the request to the right data, privacy or engineering team.
Discuss my project AI Data · Anonymization · Evaluation · Sovereign AI · Engineering