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PII Redaction API
x402
PII Redaction API is a paid API for AI agents from agents.crowdspark.me, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-31).
Scrubs personally identifiable information (emails, phones, SSNs, credit cards, IBANs, IPs, MACs, API keys, JWTs, private keys, wallet addresses) from text using label, hash, or star masking
Price / call$0.003
Success—
StatusUnknown
How this service works
Pay-per-call utilities for AI agents over x402, USDC on Base.
Output
A JSON object containing: 'redacted' (the sanitized text with PII replaced by mask tokens), 'findings' (array of objects with type, start, end, and length for each detected item), 'counts' (object mapping each PII type to the number of occurrences found), 'total_found' (integer total count), and 'mask' (the masking mode used).
Example use cases
Sanitize support tickets before LLM processing
“Before passing these customer support messages to our AI summarizer, scrub out any emails, phone numbers, credit card numbers, or SSNs and replace them with labels like [EMAIL] — here's the ticket: 'Please charge my card 4111111111111111 and email confirmation to sarah@company.com, my SSN is 456-78-9012.'”
Remove leaked secrets from developer logs
“I've got a chunk of application log text that might contain API keys, JWTs, or private keys — can you redact them using hash masking so I can safely store the logs? Here's the text: 'Authorization: Bearer eyJhbGciOiJIUzI1NiJ9.abc123 and API_KEY=sk-abc123xyz'”
Anonymize user data for analytics pipeline
“We need to anonymize this batch of user-submitted feedback before it goes into our analytics dashboard — replace any personal info like emails, IPs, MAC addresses, or phone numbers with stars, then tell me how many of each type you found. Here's the text: 'User 192.168.1.1 (mac: AA:BB:CC:DD:EE:FF) wrote: call me at 800-555-0199 or email bob@domain.org.'”
Technical details
Endpoint
POSThttps://agents.crowdspark.me/v1/text/pii-redact?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, deterministic PII scrubbing before text reaches an LLM, a log store, or an analytics pipeline. It covers a broad set of sensitive entity types (emails, phones, SSNs, credit cards, IBANs, IPs, MACs, API keys, JWTs, private keys, and crypto wallet addresses) in a single call. It is especially useful for agents processing untrusted user input, sanitizing support tickets, or ensuring compliance before data storage. Prefer it over regex-based solutions when you need structured findings metadata (offsets, counts by type) in addition to the redacted text.
Side effects
- Text exceeds 100,000 character limit — returns error
- Invalid mask value outside label/hash/star enum — returns validation error
- Malformed JSON body — returns 400 bad request
- Missing text field — returns validation error
- Rate limit exceeded without payment header — returns 402 or 429
- Network timeout on very large payloads
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| mask | string | label (default) [EMAIL], hash [EMAIL:ab12cd34], or star |
| text | string | Text to redact, max 100,000 characters |
Output
Schema · Response
{
"type": "json",
"example": {
"mask": "label",
"counts": {
"email": 1,
"phone": 1,
"credit_card": 1
},
"findings": [
{
"end": 20,
"type": "email",
"start": 8,
"length": 12
}
],
"redacted": "Contact [EMAIL] or [PHONE]. Card [CREDIT_CARD].",
"total_found": 3
}
}Tags
x402
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