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HR & Recruitment

Automation for hiring teams that need faster screening, stronger structure, and better early-stage signal.

Recruitment processes slow down when screening is inconsistent, evaluation notes are unstructured, and recruiters spend time repeating low-leverage tasks. Here the objective is a more disciplined pipeline with better decision support.

Typical friction points
Large applicant volumes creating inconsistent screening quality and slow response times.
Interview notes and candidate evaluations living in fragmented documents and inboxes.
Recruiters spending too much time on repetitive first-round tasks instead of higher-value decisions.
Suggested system layers
Candidate profile parsing, tagging, and structured scoring support
First-round interview assistance and evaluation summaries
Candidate communication and scheduling coordination
Recruitment dashboards for hiring velocity and funnel quality
Relevant proof direction: Relevant case-study direction: AI-assisted candidate screening that materially reduced early-stage workload and accelerated hiring decisions.
Relevant completed work

Matching case studies from the wider portfolio.

Each card below is selected for this route so buyers can see real completed work that reflects similar operating pressure, system complexity, and commercial outcomes.

Recruitment workflow platform

AI Candidate Screening & Document Processing

Result achieved: First-round screening time dropped by more than 80 percent without adding headcount.

UNIDEX automated interview intake, transcription, evaluation structuring, and recruiter comparison so early-stage hiring no longer relied on fragmented notes and repeated manual calls.

Stack
Python, OpenAI APIs, Whisper API, Lovable, Claude Code
Specialist
UNIDEX delivery team
Screenlink

AI platform for first-stage candidate screening

Result achieved: Recruiters received more consistent structured assessments at scale.

The attached project materials describe AI agents that conduct initial interviews, extract candidate answers, summarize results, and reduce bias through more reliable structured scoring.

Stack
LLM APIs, Lovable, Claude Code, Python
Specialist
Кай Оморов
JobPulse

AI job aggregation and recommendation bot

Result achieved: Vacancy collection, candidate-fit analysis, and candidate-job matching were automated into one Telegram workflow.

The full portfolio shows a bot aggregating openings from multiple sources, scoring fit with AI, and processing more than one hundred vacancies per cycle with recurring parsing and recommendation logic.

Stack
Django, DRF, FastAPI, Aiogram, PostgreSQL, Redis, Celery Beat, Docker
Specialist
Ислам Дуйшобаев