Submit a file for parsing (sync or async)
Parse candidate documents (CVs, resumes, cover letters) and extract structured data.
Processing Modes:
- Asynchronous (default): Submit job and poll for results using the provided status URL
- Synchronous: Get immediate results (suitable for smaller files)
Supported Formats:
- PDF files (.pdf)
- Microsoft Word documents (.doc, .docx)
- Image files (.jpg, .jpeg, .png)
- Plain text files (.txt)
File Requirements:
- Maximum total pages: 50 pages across all files
- Files are automatically converted to PDF for processing
Language Support: Currently supported languages include German, English. More languages can be requested.
Authorization
API Key Authentication 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
Query Parameters
Processing mode: Set to false for synchronous processing (immediate results). Use true for asynchronous processing (polling required).
trueRequest Body
multipart/form-data
TypeScript Definitions
Use the request body type in TypeScript.
Response Body
application/json
application/json
application/json
application/json
application/json
curl -X POST "https://loading/v1/parse/?run_async=true" \ -F cv="string"{
"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. Nachweisliche Erfolgsbilanz bei der Steigerung der Prognosegenauigkeit auf über 90% mittels XGBoost und der Leitung technischer Projekte. Kompetent in Python, R und SQL.",
"personal_impression": "Matteo Guscetti zeigte sich als selbstbewusster, ausgesprochen kommunikativer Kandidat mit hoher Teamfähigkeit. Seine offene, besonnene Art zeugt von exzellenter kultureller Passung und Lernbereitschaft.",
"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. Arbeitete mit automatisierten Test-Pipelines (pytest) und Toolkits zur Abhängigkeitsverwaltung (poetry)",
"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": 2.34
}{
"job_id": "550e8400-e29b-41d4-a716-446655440000",
"status_url": "https://api.talentkiwi.tech/v1/parse/550e8400-e29b-41d4-a716-446655440000/status",
"result_url": "https://api.talentkiwi.tech/v1/parse/550e8400-e29b-41d4-a716-446655440000/results"
}{
"detail": "string"
}{
"detail": "string"
}{
"detail": "string"
}Custom Developments
Build a custom integration between talentkiwi and your ATS or internal system.
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.