Due Diligence Scan (BlindOracle)

x402

Due Diligence Scan (BlindOracle) is a paid API for AI agents from api.craigmbrown.com, paid per call via x402, $1/call, status unknown (last checked 2026-09-15).

Runs an automated due diligence scan covering financials, litigation, key personnel, IP, media sentiment, and red flags for a given entity or topic.

Price / call$1

Seller volume →—

Success—

StatusUnknown

How this service works

Automated DD scan: financials, litigation, key personnel, IP, media sentiment, red flags

Output

Returns a JSON object with a summary string, an array of findings covering financials, litigation, key personnel, IP, and media sentiment, and an array of actionable recommendations or red flags identified during the scan.

Example use cases

Pre-investment startup screening

“Run a due diligence scan on NovaTech AI — I want to see their financials, any lawsuits or legal issues, who the key people are, their IP situation, and any red flags I should know about before writing a check.”

Partnership counterparty vetting

“I'm about to sign a distribution agreement with GlobalEdge Logistics. Can you do a quick due diligence scan on them — litigation history, financial health, media sentiment, and any obvious red flags?”

Executive background research

“Before we bring on Marcus Reed as our new CFO, can you run a due diligence scan on him — looking for any litigation, financial misconduct, media mentions, and reputation red flags?”

Technical details

Endpoint

POSThttps://api.craigmbrown.com/v1/services/ops.due-diligence-scan

When to prefer

Choose this endpoint when you need a rapid, automated multi-dimensional due diligence pre-screen on a company, individual, or topic — covering financials, litigation, personnel, IP, and media sentiment in a single call. Prefer it over manual research aggregation or narrow single-domain checks when you need a consolidated risk and red-flag summary quickly, especially in investment, partnership, or hiring contexts.

Side effects

Vague or ambiguous prompt may return generic or incomplete findings
Entity not found or insufficient public data may result in sparse findings array
Malformed prompt input returns validation error
Service timeout if research corpus is large
Rate limiting or payment failure under x402 protocol

Input

Schema · Request body

Field	Type	Description
prompt	string

Output

Schema · Response

{
  "type": "json",
  "schema": {
    "type": "object",
    "properties": {
      "summary": {
        "type": "string"
      },
      "findings": {
        "type": "array",
        "items": {
          "type": "string"
        }
      },
      "recommendations": {
        "type": "array",
        "items": {
          "type": "string"
        }
      }
    }
  },
  "example": {
    "summary": "..."
  }
}

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How this service works

Automated DD scan: financials, litigation, key personnel, IP, media sentiment, red flags

Output

Example use cases

Pre-investment startup screening

Partnership counterparty vetting

Executive background research

“Before we bring on Marcus Reed as our new CFO, can you run a due diligence scan on him — looking for any litigation, financial misconduct, media mentions, and reputation red flags?”

Technical details

Endpoint

POSThttps://api.craigmbrown.com/v1/services/ops.due-diligence-scan

When to prefer

Side effects

Vague or ambiguous prompt may return generic or incomplete findings
Entity not found or insufficient public data may result in sparse findings array
Malformed prompt input returns validation error
Service timeout if research corpus is large
Rate limiting or payment failure under x402 protocol

Input

Schema · Request body

Field	Type	Description
prompt	string

Output

Schema · Response

{
  "type": "json",
  "schema": {
    "type": "object",
    "properties": {
      "summary": {
        "type": "string"
      },
      "findings": {
        "type": "array",
        "items": {
          "type": "string"
        }
      },
      "recommendations": {
        "type": "array",
        "items": {
          "type": "string"
        }
      }
    }
  },
  "example": {
    "summary": "..."
  }
}