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SiteCheck PII Redaction
x402
SiteCheck PII Redaction is a paid API for AI agents from api.sitecheck-api.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-19).
Replaces personally identifiable information (emails, phone numbers, IBANs, credit card numbers, IP addresses, and optionally person names) in text with labeled placeholders
Price / call$0.002
Success—
StatusUnknown
How this service works
Replaces personal data in a text with placeholders: e-mail addresses, phone numbers, IBANs (mod 97 checked), payment card numbers (Luhn checked only), IPv4 addresses and, with names true, person names found by the chat model. Returns the redacted text and counts per type. Original values are never returned.
Output
Returns the input text with PII replaced by labeled placeholders like [EMAIL], [PHONE], [CARD], [IBAN], [IP], and optionally [NAME]. Also returns a counts object showing how many of each PII type were found, a source field describing the detection methods used (e.g. Luhn check for cards, mod-97 for IBANs, libphonenumber for phones), and a checkedAt timestamp.
Example use cases
Sanitize support tickets before LLM processing
“Before you summarize these customer support tickets, please scrub out all emails, phone numbers, and credit card numbers so no PII reaches the model — here's the raw text: 'Customer Jane Doe called at 555-234-5678 and her card ending 4111111111111111 was declined.'”
GDPR compliance for user-submitted content
“I need to anonymize this user-generated comment before we store it — replace all emails, IBANs, and person names with placeholders: 'Please refund to GB29NWBK60161331926819, my name is Maria Gonzalez and email is maria@example.com.'”
Remove IP addresses from server logs
“Strip all IP addresses and email addresses from this log snippet so I can share it with the vendor: '2024-01-10 ERROR user admin@corp.com request from 192.168.1.42 failed.'”
Technical details
Endpoint
POSThttps://api.sitecheck-api.workers.dev/api/redact?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, pattern-based PII redaction without sending data to a heavyweight NLP pipeline. It is ideal for pre-processing text before passing it to an LLM, for compliance workflows requiring IBAN/card/email removal, or when you need deterministic placeholder labels (e.g. [EMAIL]) rather than full entity-recognition annotations. Prefer alternatives if you need redaction of highly domain-specific PII (e.g. medical record IDs) or require a full named-entity recognition model across many entity types beyond the supported five.
Side effects
- Text exceeds 20,000 character limit — request will be rejected or truncated
- False positives: valid-looking but non-PII sequences matched by patterns (e.g. random number matching Luhn)
- False negatives: unusual phone formats not recognized by libphonenumber
- Name detection may miss uncommon or non-Western names when names flag is true
- Network or payment failure returning 402 if USDC balance is insufficient
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Text, up to 20,000 characters |
| names | boolean | true to also replace person names (uses the chat model) |
| types | array | Any of email, phone, iban, card, ip (default all) |
Output
Schema · Response
{
"type": "json",
"example": {
"text": "Mail [EMAIL], card [CARD], IBAN [IBAN], server [IP], call [PHONE].",
"counts": {
"ip": 1,
"card": 1,
"iban": 1,
"name": 0,
"email": 1,
"phone": 1
},
"source": "patterns: card (Luhn), IBAN (mod 97), e-mail, IPv4, phone (libphonenumber)",
"checkedAt": "2026-10-14T07:01:47.674Z"
}
}Tags
x402
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