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PII Redaction and Anonymization
x402
PII Redaction and Anonymization is a paid API for AI agents from agentsvc.io, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-12).
Detects and removes or masks personally identifiable information (PII) from text using pattern matching and Claude Haiku for context-aware entity recognition
Price / call$0.003
Success—
StatusUnknown
How this service works
PII redaction for agent pipelines: remove personal data from text before it goes to another model, a log, a ticket system or a dataset. Emails, IBANs (checksum verified), card numbers (Luhn verified), phone numbers and IP addresses are found by exact patterns; names of people, street addresses, birth dates and ID numbers by Claude Haiku 5.5.
Output
Returns the redacted text with PII replaced according to the chosen mode (placeholder labels like [EMAIL_1], block masks ████, or full removal), plus an optional mapping object linking each placeholder back to its original value. Detected entity types include PERSON, EMAIL, PHONE, ADDRESS, IBAN, CARD, IP, DATE_OF_BIRTH, ID_NUMBER, ORGANIZATION, and URL.
Example use cases
Sanitize support tickets before logging
“Take this customer support message and redact all personal info — names, emails, phone numbers, and any card numbers — using placeholders so the logs don't contain sensitive data. Return the mapping so we can look up the originals if a case escalates.”
GDPR-safe dataset preparation
“I have a batch of user-submitted feedback texts I need to anonymize before adding them to our training dataset. Strip out all PII — people's names, addresses, dates of birth, and ID numbers — and replace them with masks. Use the 'mask' mode so the redacted spans look like ████.”
Pre-LLM text scrubbing for agent pipelines
“Before you send this customer email to the other AI model for classification, please redact everything sensitive — emails, IBANs (checksum-verified), phone numbers, and any names — using placeholder mode, but keep our support address support@acme.com in place.”
Technical details
Endpoint
POSThttps://agentsvc.io/api/v1/proxy/pii-redact?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to scrub PII before passing text to another model, logging system, ticket tool, or dataset, and you want both pattern-based accuracy (Luhn-verified card numbers, IBAN checksum) and LLM-based contextual detection (names, addresses, dates of birth). Prefer it over simple regex approaches when mixed entity types are present or when compliance (GDPR, HIPAA) demands high recall. The placeholder-with-mapping feature is especially useful when you need to de-anonymize later in a controlled workflow.
Side effects
- Text exceeds 8,000 character limit — request is rejected
- Invalid enum value for mode or types fields — validation error
- Malformed input JSON — 400 bad request
- Transient Claude Haiku availability issue causing contextual entity detection to fail
- Payment not included or insufficient — 402 Payment Required
- False negatives: contextual entities like names in unusual formats may be missed
- False positives: legitimate non-PII text resembling patterns may be incorrectly redacted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| keep | array | Values to leave in place, e.g. your own support address |
| mode | string | placeholder: [EMAIL_1]; mask: ████; remove: delete |
| text | string | Text to redact, max 8,000 chars |
| types | array | Which kinds to redact. Default all except ORGANIZATION and URL |
| return_mapping | boolean | Return placeholder to original value mapping |
Tags
x402
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