talentkiwi
IntegrationsApi reference

Get job results (for async jobs)

GET
/v1/parse/{job_id}/results

Retrieve the parsed results from a completed asynchronous job.

Prerequisites:

  • Job must have completed status (check via /status endpoint)
  • Job must belong to your API key

Response Structure: The results contain structured candidate information including:

  • Personal details (name, contact information)
  • Work experience and employment history
  • Education background
  • Skills and competencies
  • Additional parsed fields based on document content

Authorization

API Key Authentication
X-API-KEY<token>
Enter your secret API key to authorise requests.

You can obtain your key by contacting us: gian@dionitech.com

**Example**: `sk_live_12345abcde...`

In: header

Path Parameters

job_id*Job Id

Response Body

application/json

application/json

application/json

application/json

application/json

curl -X GET "https://loading/v1/parse/string/results"
{
  "data": {
    "personal_info": {
      "full_name": "Matteo Guscetti",
      "email": "matteo.guscetti@yahoo.com",
      "nationality": "Schweiz",
      "gender": "Männlich",
      "civil_status": "",
      "car_license": false,
      "birth_date": "1996-12-16",
      "mobile_phone": "+41797251712",
      "home_phone": "",
      "street_and_number": "",
      "city": "",
      "state": "",
      "country": "",
      "postal_code": "",
      "linkedin": "",
      "availability": ""
    },
    "professional_summary": "Erfahrener Data Scientist mit 2+ Jahren fundierter Expertise in der Entwicklung und Implementierung fortschrittlicher ML-Modelle, einschliesslich Reinforcement Learning und CNNs.",
    "personal_impression": "Matteo Guscetti zeigte sich als selbstbewusster, ausgesprochen kommunikativer Kandidat mit hoher Teamfähigkeit.",
    "experience": [
      {
        "id": "exp_1",
        "company": "Siemens",
        "position": "Data Scientist",
        "employment_type": "Praktikum",
        "start_date": "01.2022",
        "end_date": "07.2022",
        "city": "Zürich",
        "state": "Zürich",
        "country": "Schweiz",
        "postal_code": "",
        "description_1to1": "Entwicklung eines Reinforcement-Learning-Algorithmus zum Ausgleich von Stromnetzen mit hohem Anteil erneuerbarer Energien.",
        "reason_for_change": ""
      }
    ],
    "education": [
      {
        "id": "edu_1",
        "institution": "Eidgenössische Technische Hochschule (ETH) Zürich",
        "institution_type": "Universität",
        "city": "Zürich",
        "state": "Zürich",
        "country": "Schweiz",
        "postal_code": "",
        "degree": "Master in Datenwissenschaft",
        "degree_level": "Master",
        "field_of_study": "Datenwissenschaft",
        "start_date": "02.2020",
        "end_date": "07.2022",
        "concluded_successfully": true,
        "is_further_education": true,
        "gpa": "5.51/6",
        "description_1to1": "Austauschsemester am Imperial College London im Herbst 2020."
      }
    ],
    "skills": {
      "fields_of_experitse": [
        "Data Science",
        "Machine Learning",
        "Business Development"
      ],
      "hard_skills": [
        "Reinforcement Learning",
        "Quantitative Analyse",
        "Startup Bewertung"
      ],
      "it_skills": [
        "Python",
        "PyTorch",
        "R",
        "SQL",
        "Git",
        "CNN",
        "XGBoost"
      ],
      "soft_skills": [
        "Teamführung",
        "Kommunikation",
        "Problemlösung"
      ]
    },
    "languages": [
      {
        "language": "Italienisch",
        "standard_proficiency": "C2",
        "proficiency": "Muttersprache"
      },
      {
        "language": "Englisch",
        "standard_proficiency": "B2",
        "proficiency": "gut"
      }
    ],
    "hobbies_interests": [
      "Reisen",
      "Fotografieren",
      "Wandern"
    ]
  },
  "status_code": 200,
  "execution_time": 3.45
}

{
  "detail": "Job failed to process. Results are not available."
}

Empty
{
  "detail": "string"
}
{
  "detail": "string"
}
{
  "detail": [
    {
      "loc": [
        "string"
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}

Create PDF project from ATS applicants POST

Receive an ATS ``create-pdf`` request, resolve/provision the consultant as a TalentKiwi user, create a project with the requested candidates, kick off background ATS imports, and return a ``redirectUrl`` pointing at the new project. Authentication is via the ``X-Shared-Secret`` header — the same SHA-256 hash lookup used for regular API keys. Steps: 1. Resolve the organisation from the API key. 2. Fetch org-level HR4YOU credentials from ``DomainResourceMapping``. 3. Look up the consultant's e-mail via the HR4YOU ``/consultants/{userId}`` endpoint. 4. Resolve or auto-provision the TalentKiwi user. 5. Create a new project (default open project type). 6. Optionally attach a job post (when ``projectId`` is provided). 7. Create one candidate per ``applicantId``, tag with ATS info. 8. Enqueue a background import per candidate. 9. Return the redirect URL.

Get job status (for async jobs) GET

Check the current status of an asynchronous document parsing job. **Job Statuses:** - `pending`: Job is queued and waiting to be processed - `running`: Job is currently being processed - `completed`: Job finished successfully, results are available - `failed`: Job failed due to an error **Polling Recommendations:** - Check status every 5-10 seconds for small files - Check status every 15-20 seconds for larger files - Stop polling once status is `completed` or `failed`