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PDF Redaction Risk Map — Compute Revenue Lab
x402
PDF Redaction Risk Map — Compute Revenue Lab is a paid API for AI agents from x402.bakdupoffroad.com, paid per call via x402, $0.045/call, status down (last checked 2026-09-15).
Scans a base64-encoded PDF for sensitive data signals (emails, phone numbers, SSNs, IBANs, credentials, credit cards) and returns a page-level risk map indicating where PII-like patterns were detected before the document is shared or published.
Price / call$0.045
Success—
StatusDown
How this service works
Private PDF preflight and authorized-video intelligence APIs paid per call with x402 USDC on Base.
Output
Returns a JSON object containing a redaction risk map with: per-type signal counts (email, phone, SSN-like, IBAN-like, credential-like, credit card-like), a list of page numbers where signals were found, a list of pages with unresolved image content, a confidence level ('bounded-pattern-scan'), a flag indicating whether the source is safe, a SHA-256 hash of the input document, and the total page count. Also includes economics breakdown of the $0.045 USDC call cost.
Example use cases
Pre-share PII audit for legal contracts
“Before I send this NDA to the client, can you scan it for any accidentally included email addresses, phone numbers, or credential-like strings? Here's the PDF encoded in base64 — tell me which pages have signals I should review.”
Compliance preflight for HR document release
“We're about to release a batch of HR documents and need to make sure none of them contain unredacted SSNs or IBAN numbers. Can you run this base64 PDF through a redaction risk scan and tell me the signal counts by type?”
Publishing safety check for research report
“I'm about to publish this research report as a PDF — can you check it for any PII patterns like emails or credit card numbers that might have slipped in? Give me a page-level breakdown of where any signals appear.”
Technical details
Endpoint
POSThttps://x402.bakdupoffroad.com/v1/document/redaction-risk-mapWhen to prefer
Choose this endpoint when you need a lightweight, privacy-safe PII signal scan on a PDF before sharing, publishing, or archiving it — especially when you want page-level granularity across multiple signal types (email, phone, SSN, IBAN, credentials, credit cards) without sending document text to a third party. Prefer this over general-purpose LLM document analysis when cost predictability ($0.045/call), speed, and the guarantee that customer text is never retained or returned are priorities. Not suitable for full OCR, image-based redaction, or guaranteed complete redaction verification.
Side effects
- PDF exceeds the service's maximum decoded-size limit — request rejected
- Invalid or malformed base64 encoding — parsing error returned
- Payment not provided or insufficient — HTTP 402 returned before processing
- PDF is encrypted or password-protected — content may not be scannable
- Image-heavy pages with no extractable text — flagged as unresolved image pages, signals may be missed
- Network timeout on large documents
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| pdfBase64 | string | A base64-encoded PDF, up to the service's advertised decoded-size limit. Customer text is never returned or retained. |
Output
Schema · Response
{
"type": "json",
"example": {
"jobId": "00000000-0000-4000-8000-000000000000",
"result": {
"riskMap": {
"confidence": "bounded-pattern-scan",
"sourceSafe": true,
"limitations": [
"pattern counts are leads, not a guarantee of complete redaction"
],
"signalCounts": {
"email": 2,
"phone": 1,
"ssnLike": 0,
"ibanLike": 0,
"credentialLike": 0,
"creditCardLike": 0
},
"pagesWithSignals": [
1,
3
],
"unresolvedImagePages": []
},
"pageCount": 3,
"sourceSha256": "0000000000000000000000000000000000000000000000000000000000000000",
"schemaVersion": "1"
},
"economics": {
"netMicroUsd": 45000,
"energyMicroUsd": 0,
"revenueMicroUsd": 45000,
"platformFeeMicroUsd": 0
}
}
}Tags
x402
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