AI for HR and recruitment
In recruitment AI takes on the part nobody enjoys doing by hand: reading hundreds of CVs. Semantic models work through thousands of applications in minutes, pulling out skills, sector experience and fit against the requirements, and companies using them cut time-to-hire by 60% with better retention at twelve months. The second use is less obvious: predicting attrition from data HR already holds. On screening we hold one line - the system orders the pile, it does not reject on a human's behalf, because that is exactly where discrimination gets built into a process.
Three uses of AI in HR and recruitment
CV screening and ranking
An NLP system reads CVs and covering letters, pulls out skills, experience, technologies and certifications, and ranks candidates against the role. What counts is not keyword overlap but semantic similarity of experience, and room to grow.
Recruitment chatbot
An AI bot runs the first conversation with candidates, checking availability, salary expectations and the key competencies. It schedules interviews, sends reminders and collects feedback after each stage. The candidate hears back immediately, at any hour, instead of waiting a week to learn it was a no.
Attrition prediction and engagement analysis
The model reads HR data - absence, review outcomes, tenure, promotion history, pulse surveys - and flags employees at high risk of leaving. For each one it suggests what could be done: a raise, a change of project, training, with an estimate of how likely it is to work.
Recommended stack
Return on investment
25 h
Hours saved weekly
€20
Hourly rate
€25,000
Annual saving
The maths: 25 h/week × €20/h × 48 weeks = €25,000 a year
Figures are quoted in euro, converted from Polish złoty at a fixed rate of 4.30 PLN to 1 EUR and rounded. Contracts are settled in either currency.
What makes it hard
Algorithmic bias - models can reproduce historical prejudice around gender, age and origin
GDPR compliance when profiling candidates and deciding automatically (article 22), which requires a right of appeal
Data quality in the ATS - incomplete profiles, duplicates and non-standard CV formats make extraction harder
Hiring managers' reluctance to trust an algorithm's ranking, which makes transparency part of the build
Frequently asked questions
Does AI in recruitment discriminate?
The risk is real and has to be watched. We audit models for fairness on a schedule, check outcomes across demographic groups and apply debiasing. The hiring manager can see why a candidate ranked where they did. Built properly, the system also removes some of the bias a human brings to the process.
How long does it take to deploy?
A recruitment chatbot: two to three weeks. CV screening: three to four weeks. An attrition model needs six to eight weeks and at least twelve months of history. We start by going through your current process and finding where the return comes fastest.
Does it work for technical hiring?
Particularly well - developer CVs are rich in structured data: technologies, projects, GitHub. The system reads GitHub and LinkedIn alongside the CV, so it assesses real technical ground. On engineering roles we cut time-to-hire from 30 working days to 10.
AI in HR and recruitment
Tell us what gets done by hand at your company, and how often. Within 24 hours you get back where to start and how long it takes.
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AI readiness audit
Ten questions about processes, data and people. The result appears on screen.