Rafid Candidate Profile Extractor

x402

Rafid Candidate Profile Extractor is a paid API for AI agents from api.rafidsystem.com, paid per call via x402, $0.1/call, status unknown (last checked 2026-10-10).

Extracts structured candidate profile data from raw CV/resume text or document URLs, returning parsed skills, experience, education, seniority, and career metadata.

Price / call$0.1

Seller volume →—

Success—

StatusUnknown

How this service works

Rafid Agent API provides structured intelligence and paid APIs for AI agents across property, company and risk, supplier, finance, document, website, automotive, logistics, recruitment, trading, voice, video and business-planning workflows.

Output

Returns a JSON object containing: candidate name, headline, professional summary, location, list of skills with category and confidence scores, work experience entries with job title, company, dates, technologies, and responsibilities, education history, certifications, languages, industries, career progression, seniority estimate, management experience flag, total experience years, recent role, candidate keywords, and an evidence quality object with a score and flags for missing or ambiguous information.

Example use cases

ATS pipeline resume ingestion

“I've got a raw resume text from a new applicant — can you extract their full candidate profile including skills, job history, seniority, and years of experience so I can load it into our hiring system?”

Recruiter shortlisting assistant

“Here's the CV text for a candidate applying for a senior data role. Pull out their Python and SQL skills, seniority estimate, and total experience years so I can decide whether to shortlist them.”

Bulk candidate screening from document URLs

“I have a public link to a candidate's CV at https://example.com/cv.pdf — can you extract their structured profile, including recent role, technologies used, and evidence quality score?”

Technical details

Endpoint

POSThttps://api.rafidsystem.com/api/v1/x402/recruitment/extract-candidate-profile?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a richly structured, schema-versioned candidate profile extracted from raw resume text or a document URL, with confidence scoring, seniority estimation, and evidence quality grading included. Prefer it over generic document parsers when recruitment-specific entities (job titles, technologies, career progression, management experience) are required in a normalized JSON format suitable for ATS or screening pipelines.

Side effects

Empty or too-short CV text returns low evidence quality score with missing_information flags
Unreadable document URL returns error or empty profile fields
Non-standard CV formats may produce null fields for name and location
Very long documents exceeding 200,000 character limit are rejected
Ambiguous date ranges result in null duration_months values
Non-English CVs may parse less accurately unless language parameter is set explicitly

Input

Schema · Request body

Field	Type	Description
cv_text	string
language	string
document_url	string
target_schema_version	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "skills": [
      {
        "name": "Python",
        "category": "Professional",
        "evidence": [
          "Python"
        ],
        "confidence": 0.9
      }
    ],
    "projects": [],
    "candidate": {
      "name": null,
      "headline": "Senior Data Analyst",
      "location": null,
      "professional_summary": null
    },
    "education": [],
    "languages": [],
    "experience": [
      {
        "company": "",
        "end_date": "",
        "job_title": "Senior Analyst at Example 2020-2024",
        "start_date": "",
        "achievements": [],
        "technologies": [
          "Python",
          "SQL",
          "Microsoft Power BI"
        ],
        "duration_months": null,
        "employment_type": null,
        "responsibilities": []
      }
    ],
    "industries": [],
    "recent_role": "Senior Analyst at Example 2020-2024",
    "certifications": [],
    "evidence_quality": {
      "score": 60,
      "ambiguities": [],
      "missing_information": [
        "education"
      ]
    },
    "candidate_keywords": [
      "Python",
      "SQL",
      "Microsoft Power BI"
    ],
    "career_progression": [],
    "seniority_estimate": "senior",
    "management_experience": null,
    "total_experience_years": 5
  }
}

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