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Compute Revenue Lab — PDF Private Preflight Bundle
x402
Compute Revenue Lab — PDF Private Preflight Bundle is a paid API for AI agents from x402.bakdupoffroad.com, paid per call via x402, $0.075/call, status down (last checked 2026-09-15).
Analyzes a base64-encoded PDF to assess structure, OCR needs, form topology, redaction risks, and recommended extraction pipeline before committing to full processing.
Price / call$0.075
Success—
StatusDown
How this service works
Private PDF preflight and authorized-video intelligence APIs paid per call with x402 USDC on Base.
Output
A JSON object containing: a job ID, page count, document structure validity (status and repair candidacy), an OCR page map showing which pages need OCR, form topology (AcroForm presence, field counts by type, signature fields), a SHA-256 hash of the source, a processing budget (work units, selectable vs profiled pages, estimated OCR pages avoided), a redaction risk map with pattern-scan signal counts for emails, phones, SSNs, IBANs, credentials, and credit cards, plus the recommended extraction pipeline method, and billing economics (net cost in microUSD).
Example use cases
PII risk scan before document ingestion
“I'm about to ingest a batch of customer-uploaded PDFs into our data warehouse — can you preflight this one first to check for any sensitive data signals like SSNs, IBANs, or credit card numbers before it goes anywhere?”
Optimize OCR cost for scanned documents
“I have a scanned PDF and I'm not sure if it needs full OCR or if there's already selectable text — can you run a preflight on it and tell me how many pages actually need OCR and which extraction pipeline you'd recommend?”
Validate PDF structure before form processing
“We're processing a PDF form submission and want to confirm the AcroForm structure is valid and count the form fields before running our extraction — can you preflight this PDF and give me the field count and structure status?”
Technical details
Endpoint
POSThttps://x402.bakdupoffroad.com/v1/document/private-preflight-bundleWhen to prefer
Choose this endpoint when you need a lightweight, privacy-safe preflight pass on a PDF before committing to heavier extraction or OCR pipelines. It is ideal for cost estimation (determining how many pages truly need OCR), PII triage (bounded pattern scan for common sensitive data signals), and structural validation (AcroForm field counts, repair candidacy) — all without retaining customer text. Prefer it over full extraction endpoints when you want to route documents to the right pipeline, or when you need a SHA-256 fingerprint and a recommended method before proceeding.
Side effects
- Oversized PDF exceeds the service's decoded-size limit — payload rejected
- Malformed base64 string causes decoding failure
- PDF is encrypted or password-protected, preventing structure analysis
- Network timeout if PDF is very large
- x402 payment failure if USDC balance is insufficient or Base network is congested
- Invalid content type returns schema validation error
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": {
"risks": [],
"pageCount": 4,
"structure": {
"status": "valid",
"repairMethod": "none",
"issueCategories": [],
"repairCandidate": false
},
"ocrPageMap": {
"pages": [],
"profileTruncated": false,
"profiledPageCount": 4
},
"formTopology": {
"fieldCount": 0,
"hasAcroForm": false,
"fieldTypeCounts": {},
"widgetPageCount": 0,
"needsAppearances": false,
"signatureFieldCount": 0
},
"sourceSha256": "0000000000000000000000000000000000000000000000000000000000000000",
"schemaVersion": "1",
"documentFamily": "selectable_pdf",
"processingBudget": {
"workUnits": 4,
"formFields": 0,
"sparsePages": 0,
"profiledPages": 4,
"selectablePages": 4,
"profileTruncated": false,
"estimatedOcrPages": 0,
"avoidedFullOcrPages": 4,
"recommendedPipeline": "extract-selectable-text-with-page-locators"
},
"redactionRiskMap": {
"confidence": "bounded-pattern-scan",
"sourceSafe": true,
"limitations": [],
"signalCounts": {
"email": 0,
"phone": 0,
"ssnLike": 0,
"ibanLike": 0,
"credentialLike": 0,
"creditCardLike": 0
},
"pagesWithSignals": [],
"unresolvedImagePages": []
},
"recommendedMethod": "extract-selectable-text-with-page-locators"
},
"economics": {
"netMicroUsd": 75000,
"energyMicroUsd": 0,
"revenueMicroUsd": 75000,
"platformFeeMicroUsd": 0
}
}
}Tags
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