Sanitize datasets for ML without leaking PII.
Strip personal data from training corpora, evaluation sets, and prompt libraries so your models learn patterns, not identities. All processing happens on your machine.
What Piixie detects in ML datasets.
Piixie identifies and anonymizes personal data across customer logs, user-generated content, system telemetry, and code repositories before they enter your training pipeline.
Customer data
Names, email addresses, phone numbers, account numbers from support logs, chat transcripts, and feedback forms.
User-generated content
Usernames, real names in posts, location data, personal photos, life events, health conditions disclosed in forums and reviews.
System identifiers
IP addresses, session IDs, device identifiers, cookies, user agent strings, API keys, authentication tokens.
Behavioral patterns
Browsing history, click patterns, search queries, download history, geolocation data, timestamps revealing activity.
Survey and form data
Respondent names, demographic details, open-ended responses with personal disclosures, income and health data.
Code and metadata
Developer names in commits, email addresses in code comments, API keys in config files, credentials in log files.
Support chat log, before and after.
Piixie detects PII across customer conversations, account references, and personal identifiers in training data.
How Piixie processes training data.
From raw datasets to PII-free corpora, every step happens on your machine. No data ever crosses a network boundary.
1. Load training data
Drop customer support logs, chat transcripts, survey responses, forum exports, or code repositories into Piixie.
2. Detect PII across formats
The local LLM identifies names, emails, IPs, account numbers, credentials, and personal disclosures in structured and unstructured data.
3. Anonymize for ML
Replace PII with consistent tokens (same person always maps to same token) or synthesize realistic substitutes that preserve data distribution.
4. Export clean dataset
PII-free training data ready for your ML pipeline. Audit log documents every removal for EU AI Act compliance.
Regulatory frameworks Piixie addresses.
Local processing eliminates entire categories of compliance risk across every framework that governs data used in AI training.
- Training data is sanitized locally before entering any ML pipeline, meeting EU AI Act requirements for data governance and quality.
- GDPR data minimization is enforced at source: personal data is stripped before it becomes part of model training, not after.
- Customer support logs, chat transcripts, and feedback forms are anonymized without transmitting customer data to third parties.
- Consistent token replacement preserves data relationships (same person = same token) while eliminating re-identification risk.
- Quasi-identifier combinations (age + location + job title) are detected and anonymized, not just direct identifiers.
- Audit logs document exactly what PII was removed from training data, providing evidence for AI governance reviews.
How ML teams use Piixie.
From NLP training to code generation, Piixie fits into existing ML data pipelines.
Sanitize support logs for NLP training
Strip customer names, emails, and account numbers from support transcripts before training chatbots, classifiers, or summarization models.
Clean survey data for analysis
Remove respondent identifiers from survey exports while preserving statistical utility for market research and product development.
Prepare prompt libraries
Anonymize real-world prompts and completions before using them as few-shot examples, evaluation sets, or fine-tuning data.
De-identify code repositories
Strip developer names, emails, API keys, and credentials from code repositories before using them in code generation model training.
Build AI on clean data.
Start sanitizing training datasets locally. No cloud exposure, no PII leaking into your models.