Candidate Analyzer
Candidate Analyzer uses semantic embeddings and language models to score candidates across 5 weighted dimensions: technical skills (30%), work experience (25%), education (15%), languages (15%), and location (15%). The service analyzes structured or raw resumes, compares them against job descriptions, and generates a detailed report with recommendations. The matching algorithm goes beyond keywords to understand the real meaning of skills and experience.
How it works
Send the resume and job description
Submit the candidate's resume (PDF, DOCX, or structured JSON) and the description of the role to fill.
Semantic vectorization
The resume and job description are converted into semantic vectors for comparison beyond simple keywords.
Multi-dimensional scoring
The candidate is evaluated across 5 weighted axes: skills (30%), experience (25%), education (15%), languages (15%), location (15%).
Report and recommendations
A detailed report is generated with the overall score, per-dimension scores, strengths, and points of attention.
API call example
import httpx
response = httpx.post(
class="text-teal">"https:class="text-text-faint italicclass="text-teal">">//api.djinn.dev/v1/analyze",
headers={class="text-teal">"X-API-Key": class="text-teal">"djinn_live_..."},
json={
class="text-teal">"candidate": {
class="text-teal">"cv_file_url": class="text-teal">"https:class="text-text-faint italicclass="text-teal">">//storage.example.com/resume_martin.pdf",
class="text-teal">"name": class="text-teal">"Sophie Martin",
},
class="text-teal">"job": {
class="text-teal">"title": class="text-teal">"Senior Python Developer",
class="text-teal">"skills": [class="text-teal">"Python", class="text-teal">"FastAPI", class="text-teal">"PostgreSQL", class="text-teal">"Docker"],
class="text-teal">"experience_years": 5,
class="text-teal">"location": class="text-teal">"Paris",
},
},
)
result = response.json()
print(fclass="text-teal">"Overall score: {result['score']}/100")
for dim in result[class="text-teal">"dimensions"]:
print(fclass="text-teal">" {dim['name']}: {dim['score']}/100 (weight: {dim['weight']}%)")
print(fclass="text-teal">"Recommendation: {result['recommendation']}")Technical specifications
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