Imagine a recruiter spending 6–10 minutes to skim a single resume. With 500 applications per vacancy, that's 50+ hours of continuous reading. We automate this stage: an AI system scans each resume, extracts key data, evaluates fit against job requirements, and outputs a ranked list with justifications. The result — instead of a pile of files, you get a ready candidate pipeline with comments and interview questions.
Resume screening powered by LLMs cuts time-to-screen from hours to minutes and reduces hiring costs by up to 40%. AI screening is 10x faster than manual and drops missed candidates from 30% to 5%.
How AI screening works
Our system accepts resumes in any format — PDF, DOCX, direct input, or via hh.ru API. After parsing, data is normalized: experience recalculated to full months, skills mapped to a unified vocabulary (e.g., "Python + Django" mapped to web-framework knowledge). Then an LLM (GPT-4o, Claude 3.5, or Llama 3) compares the profile against the vacancy text along multiple axes: technical match, experience relevance, education.
class ResumeScreeningResult(BaseModel): candidate_name: str match_score: float # 0-1 technical_match: float # fit to technical requirements experience_match: float # fit to experience education_match: float # fit to education strengths: list[str] # candidate strengths gaps: list[str] # gaps relative to requirements highlight_skills: list[str] # key skills from resume recommendation: Literal["strong_yes", "yes", "maybe", "no"] reasoning: str # 3-5 sentence justification suggested_interview_questions: list[str] def screen_resume(resume_text: str, job_description: str) -> ResumeScreeningResult: return llm.parse( build_screening_prompt(resume_text, job_description), response_format=ResumeScreeningResult ) Resume parsing
Resumes arrive in various formats: PDF, DOCX, hh.ru API. Extraction of structured data:
- hh.ru API: resumes are already structured (JSON)
- PDF/DOCX:
unstructured.ioor custom vision-based parser - LinkedIn: LinkedIn Talent Solutions API (paid)
Normalization: experience dates → working months, skills → standard dictionary.
Why AI screening beats manual
| Parameter | Manual screening | AI screening |
|---|---|---|
| Time per resume | 6–10 min | <1 min |
| Candidate miss rate | 20–30% | <5% |
| Subjectivity | high | low |
Model comparison for screening
| Model | Accuracy (F1) | p99 latency | Cost per 1K tokens |
|---|---|---|---|
| GPT-4o | 0.94 | 2.1s | $5 |
| Claude 3.5 Sonnet | 0.92 | 1.8s | $3 |
| Llama 3 70B (local) | 0.88 | 4.5s | $0 (on own infra) |
Model selection depends on privacy and budget. For sensitive data, we use Llama 3 on on-premise GPUs — inference savings can exceed $2,000/month.
Preventing discrimination
AI can replicate discriminatory patterns from historical data. Our mitigations:
- Removal of demographic data before evaluation (name, age, photo)
- Regular audit: no systematic bias by gender, age, or university
- Transparency: every rejection must be justified by professional criteria
Under employment law (e.g., 64-FZ): discrimination based on non-professional traits is prohibited.
ATS integrations
- hh.ru for employers: API for bulk screening of responses
- Huntflow: REST API for pipeline automation
- 1C:Salary and HR: candidate integration into HR system
- Potok.io / Talantix: native integrations via Webhook
Technical pipeline details
The system is built on microservices: parser (Python + FastAPI), ranker (vLLM + Triton), cache (Redis). All components scale horizontally.
Metrics: time-to-screen (from hours to minutes), quality-of-hire (% of AI-recommended hires still in company after 6 months), recruiter satisfaction score.
Development process
- Analysis — study your current hiring process, resume volume, accuracy requirements.
- Design — select model (GPT-4o, LLaMA 3, Mistral), define skill vector representation.
- Implementation — build parsing pipeline, evaluation prompt, result caching system.
- Testing — calibrate on historical data: accuracy, speed, false positive rate.
- Deployment — deploy on your infrastructure (AWS, on-premise), connect ATS.
- Monitoring — track model drift, update prompts.
What's included
- Research of your HR process and specification preparation
- LLM selection and calibration to your screening criteria
- Development of parsers for non-standard resume formats
- Integration with ATS and HR systems (API, Webhook)
- Documentation and training for the recruiting team
- 1 month of warranty support after launch
We have 10+ years of experience in AI/ML, 40+ completed projects, and have been automating recruitment for companies from startups to enterprise for years. Our systems process up to 10,000 resumes per day without quality loss. Contact us for a case evaluation. Get a consultation on implementation — prototype within 2 weeks.