Applicant Authenticity Analyzer

Pricing

Pay per event

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Applicant Authenticity Analyzer

Name: Applicant Authenticity Analyzer
Rating: 5 (2 reviews)

Try for free

Upload resumes, cover letters, or job application text to detect potential fraud. The actor extracts text from PDF/DOCX/TXT files, evaluates authenticity with OpenAI, and returns a verdict, score, and justification.

Pricing

Pay per event

Rating

5.0

(2)

Developer

ParseForge

Maintained by Community

Actor stats

Bookmarked

Total users

Monthly active users

8 days ago

Last modified

🚀 Key capabilities

Accepts pasted text or publicly accessible file URLs (PDF, DOCX, TXT)
Converts documents to text automatically using embedded parsers
Evaluates authenticity, highlights risk signals, and suggests verification steps
Produces JSON output with verdict (likely_real or likely_fake), score, justification, and confidence
Uses a tuned AI prompt with temperature controls and optional custom system instructions

🧾 Input

Field	Type	Description
`maxItems`	`integer`	How many applications to process. Free users are limited to 100. Paid users can process up to 1,000,000.
`applicationText`	`string \| string[]`	Raw application text pasted in input.
`applicationFileUrl`	`string \| string[]`	URLs pointing to PDF, DOCX, or TXT documents.
`jobRequirements`	`string \| string[]`	Optional list of requirements to evaluate job fit.
`jobResponsibilities`	`string \| string[]`	Optional list of responsibilities to evaluate job fit.

Provide at least one applicationText or applicationFileUrl.
If you supply job requirements or responsibilities, the output will add a jobMatch percentage showing how well the application aligns with the role.

📦 Example input

{
  "applicationFileUrl": [
    "https://example.com/resume-john-doe.pdf",
    "https://example.com/cover-letter-jane-smith.docx"
  ],
  "jobRequirements": [
    "8+ years of backend development experience",
    "Expertise with PostgreSQL and distributed systems"
  ],
  "jobResponsibilities": "Own the design and delivery of backend services for the hiring platform."
}

📤 Output

Each dataset item contains:

{
  "sourceId": "file-1",
  "sourceType": "file",
  "reference": "resume-john-doe.pdf (application/pdf)",
  "verdict": "Likely fake",
  "score": 3,
  "confidence": "high",
  "jobMatch": "42%",
  "summary": "Application shows strong signs of templated AI generation with contradictory dates.",
  "justification": [
    "Cover letter repeats identical phrasing across multiple paragraphs.",
    "Resume claims conflicting employment dates between sections."
  ],
  "riskSignals": "Boilerplate language with minimal personalization; Inconsistent job titles and timelines",
  "consistencyChecks": "Two roles overlap for 18 months without explanation",
  "nextSteps": "Request original references to validate employment dates"
}

When the actor cannot process a document, the dataset will include an item with error describing the failure.

⚠️ Notes

Documents must be reachable via HTTPS URLs; authenticated downloads are not supported out of the box.
Legacy .doc files are not supported; convert them to .docx first.
Always comply with privacy policies and handle applicant data securely.

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