PII Redact

x402

PII Redact is a paid API for AI agents from pii-redact.x402supply.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-17).

Detects and redacts personally identifiable information (PII) in text or JSON using regex and checksum-based pattern matching, with configurable mask/label/remove modes.

Price / call$0.02

Seller volume →—

Success—

StatusUnknown

How this service works

Detect and redact PII in text or JSON. Recognizers are pure pattern/checksum matching (regex + Luhn/mod-97/SSN-area validation) — no LLM calls, no external services. Person names and dates of birth are matched only via nearby context (e.g. 'Mr. Smith', 'DOB: ...'), not general NER, to keep false positives low.

Output

Returns the input text or JSON with PII replaced, removed, or labeled according to the chosen mode. In mask mode, PII spans are replaced with tokens like [EMAIL] or [SSN]. In label mode, the original text is annotated with entity type tags. In remove mode, PII is deleted. The response reflects the same structure as the input (string or JSON object).

Example use cases

Sanitize user support tickets

“I need to scrub PII from this support ticket before it goes into our analytics pipeline — mask any email addresses, phone numbers, and US addresses in the text: 'Customer Jane called from 555-987-6543, lives at 42 Maple St, Boston and emailed support@jane.com'.”

Anonymize JSON user records

“Take this JSON user record and remove all SSNs, credit card numbers, and IBANs from it before we archive it — use remove mode so the fields just disappear entirely.”

Label PII in compliance audit

“I need to label (don't mask, just tag) every piece of PII in this document so our compliance team can review what sensitive data types are present — flag emails, phone numbers, dates of birth, and person names.”

Technical details

Endpoint

POSThttps://pii-redact.x402supply.com/tools/redact?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need fast, deterministic PII redaction without LLM overhead — ideal for pipelines where privacy compliance is required but you want no external AI inference, low latency, and predictable behavior. Best suited for structured data (JSON) or semi-structured text with clear PII patterns. Prefer this over LLM-based redaction when you need checksum-validated matching (e.g., Luhn for credit cards, mod-97 for IBANs) and low false-positive rates for US locale data.

Side effects

Invalid or missing 'input' field returns a validation error
Empty 'entities' array (minItems: 1 required) causes a schema validation failure
Unsupported locale (only 'us' is supported) returns an error
Malformed JSON input object may cause parsing failures
Person names or dates of birth without nearby context keywords (e.g. 'Mr.', 'DOB:') may not be detected, leading to missed PII
High false-negative rate for NER-style names not preceded by titles or context markers
Payment failure (x402) if USDC balance is insufficient

Input

Schema · Request body

Field	Type	Description
input	—
options	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "counts": {
      "ssn": 1,
      "email": 1,
      "phone": 1
    },
    "findings": [
      {
        "type": "email",
        "span_end": 40,
        "confidence": 0.95,
        "span_start": 20
      },
      {
        "type": "phone",
        "span_end": 56,
        "confidence": 0.85,
        "span_start": 44
      },
      {
        "type": "ssn",
        "span_end": 73,
        "confidence": 0.9,
        "span_start": 62
      }
    ],
    "redacted": "Contact Jane Doe at [EMAIL_1] or [PHONE_1]. SSN [SSN_1]."
  }
}

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How this service works

Output

Example use cases

Sanitize user support tickets

Anonymize JSON user records

“Take this JSON user record and remove all SSNs, credit card numbers, and IBANs from it before we archive it — use remove mode so the fields just disappear entirely.”

Label PII in compliance audit

Technical details

Endpoint

POSThttps://pii-redact.x402supply.com/tools/redact?utm_source=zero.xyz

When to prefer

Side effects

Invalid or missing 'input' field returns a validation error
Empty 'entities' array (minItems: 1 required) causes a schema validation failure
Unsupported locale (only 'us' is supported) returns an error
Malformed JSON input object may cause parsing failures
Person names or dates of birth without nearby context keywords (e.g. 'Mr.', 'DOB:') may not be detected, leading to missed PII
High false-negative rate for NER-style names not preceded by titles or context markers
Payment failure (x402) if USDC balance is insufficient

Input

Schema · Request body

Field	Type	Description
input	—
options	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "counts": {
      "ssn": 1,
      "email": 1,
      "phone": 1
    },
    "findings": [
      {
        "type": "email",
        "span_end": 40,
        "confidence": 0.95,
        "span_start": 20
      },
      {
        "type": "phone",
        "span_end": 56,
        "confidence": 0.85,
        "span_start": 44
      },
      {
        "type": "ssn",
        "span_end": 73,
        "confidence": 0.9,
        "span_start": 62
      }
    ],
    "redacted": "Contact Jane Doe at [EMAIL_1] or [PHONE_1]. SSN [SSN_1]."
  }
}