Use Cases

Clean training data before model fine-tuning

If you're an AI infrastructure platform, redact processes training datasets in batch via CLI, removing customer PII before fine-tuning—ensuring privacy-safe models, GDPR compliance, and protecting customer data in model weights.

Problem

Fine-tuning datasets often contain customer PII that leaks into model weights, creating persistent privacy violations. Once PII is baked into model parameters, it cannot be removed—exposing organizations to GDPR fines and data breach liability.

Traditional data cleaning tools cannot process training formats at scale or identify context-sensitive PII in conversational datasets. Without automated sanitization, teams must choose between model quality and privacy compliance.

How Superagent solves it

Superagent redact processes training datasets in batch via CLI, removing customer PII before fine-tuning begins. Redact preserves dataset structure and conversational utility while ensuring privacy-safe model weights. Available via API, SDKs, CLI, and web playground.

  • Batch processes training datasets, removing PII while preserving conversational patterns and model utility.
  • Handles common training formats including JSONL, Parquet, and conversation datasets at scale.
  • Ensures privacy-safe fine-tuning so customer data never leaks into model parameters.
  • Documents all redactions via AI Trust Center, proving GDPR compliance in model training with mappings to EU AI Act, ISO/IEC 42001, and NIST AI RMF.

Benefits

Privacy-safe fine-tuning ensures customer PII never persists in model weights.

GDPR compliance in model training with documented redaction and audit trails.

Protect customer data in models without sacrificing fine-tuning quality or performance.

Scale training workflows confidently with automated sanitization at every stage.

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Ready to clean training data for privacy-safe models?

Deploy redact CLI to process datasets in batch and ensure customer PII never leaks into models.