Transform your hiring process with AI that screens thousands of resumes in seconds, predicts candidate success, and identifies top talent while eliminating unconscious bias from recruitment decisions.
Average time-to-hire is 42 days, with recruiters spending 23 hours manually screening resumes for each position. Top candidates accept competing offers while you're still reviewing applications.
Average cost-per-hire is €4,700 including recruiter time, advertising, and interviewing. Bad hires cost 30% of annual salary when they leave within 12 months.
Resume screening is influenced by name bias, university prestige, and employment gaps. Studies show identical resumes get different responses based on candidate name alone.
46% of new hires fail within 18 months. Traditional screening doesn't predict job success - resumes show credentials, not actual performance potential or cultural fit.
Our machine learning platform automates resume screening, predicts candidate job success, and eliminates bias - reducing time-to-hire by 50% while improving candidate quality.
NLP models automatically parse resumes, extract skills, experience, education, and projects. Semantic matching understands synonyms and skill relationships - "React developer" matches "JavaScript frontend engineer." Screens 1,000 resumes in 5 minutes vs. 23 hours manually. Ranks candidates by job fit score, not keyword matching.
ML models trained on historical hiring outcomes predict which candidates will succeed in role. Analyzes resume patterns, skill combinations, experience trajectory, and company backgrounds of your top performers. Identifies transferable skills and non-obvious candidate potential beyond traditional credentials.
AI removes identifying information (name, gender, age, ethnicity, university) before screening, focusing only on skills and experience. Bias auditing ensures hiring rates are consistent across demographic groups. Explainable AI shows why candidates were selected, ensuring compliance and fairness.
Ready to transform your recruitment process? Our AI platform reduces time-to-hire by 50% and improves candidate quality by 35%.
AI assistants handle candidate communication - sending personalized status updates, scheduling interviews based on team availability, answering common questions. Chatbots qualify candidates with pre-screening questions, assess communication skills, and provide candidate experience tracking.
ML analyzes where your best hires come from - job boards, LinkedIn, referrals, agencies. Predicts which sourcing channels will yield highest quality for each role type. Recommends when to use recruiters vs. internal sourcing based on ROI analysis. Maintains searchable talent database for future positions.
AI recruitment tools fall into several categories: Resume screening (automatic parsing and ranking), Candidate sourcing (finding passive candidates), Interview assistance (scheduling, video analysis), Assessment automation (skills testing, personality evaluation), and Predictive analytics (job success forecasting).
The business case is compelling: Organizations using AI recruitment report 50% reduction in time-to-hire (from 42 to 21 days), 70% reduction in screening time, 35% improvement in candidate quality (measured by 90-day performance reviews), and 40% cost reduction in recruiting operations. For a company hiring 100 employees annually, this translates to €200K in recruiting cost savings plus productivity gains from faster hiring.
Traditional resume parsing used keyword matching and regex patterns - brittle and inaccurate. Modern NLP approaches: Named Entity Recognition (NER) identifies skills, companies, job titles, degrees, locations. Dependency parsing understands sentence structure to extract experience duration and responsibilities. Embeddings (Word2Vec, BERT) capture semantic relationships - understanding that "Python developer" relates to "machine learning engineer."
Instead of keyword matching, transformer models create vector representations of job descriptions and resumes in shared semantic space. Cosine similarity measures job-candidate fit. This handles synonyms (React = React.js), skill hierarchies (Python includes Django, Flask), and transferable skills (Java to C# transition). Results: candidates matched to jobs they didn't explicitly mention but have transferable experience for.
Not all experience is equal. ML models assess experience quality by: company reputation (working at Google vs. unknown startup), role progression (individual contributor to manager), project complexity (described responsibilities), skill recency (5 years Python experience but last used 3 years ago is less valuable). Models learn these patterns from historical successful hires.
AI identifies concerning patterns: frequent job hopping (5 jobs in 3 years), employment gaps without explanation, skill mismatch (applying for senior role with junior experience), credential inconsistencies. Flags these for human review rather than automatic rejection - context matters for fair evaluation.
The most advanced AI recruitment predicts not just job fit but job success. Train ML models on historical hiring data:
These models identify non-obvious patterns: candidates from certain industries transition better, specific skill combinations predict success, optimal years of experience (not just "more is better"), career trajectory patterns that indicate high potential. Example finding: for data science roles, candidates with 3-5 years experience + advanced degree + open source contributions outperform those with 10+ years industry experience alone.
Successful AI recruitment isn't replacing recruiters - it's augmenting their effectiveness. Recommended workflow: (1) AI screens all applications and ranks by job fit + predicted success, (2) Recruiter reviews top 20% of AI-ranked candidates plus random sample of lower-ranked for quality check, (3) AI schedules phone screens with top candidates, (4) Recruiter conducts interviews with AI-generated candidate insights (strengths, potential concerns, skill gaps), (5) AI provides hiring recommendations with explanations, (6) Hiring manager makes final decision, (7) Track outcomes to retrain models.
This hybrid approach achieves 90% of AI efficiency gains while maintaining human oversight for fairness, context, and final judgment. Recruiters shift from manual screening to relationship-building, employer branding, and candidate experience.
A rapidly growing SaaS company in Stockholm was hiring 80-120 engineers annually. Their 3-person recruiting team spent 60% of time manually screening 200+ applications per role, resulting in 38-day average time-to-hire. Top candidates frequently accepted competing offers before final interviews could be scheduled. First-year attrition was 28%, suggesting poor candidate-role fit.
We implemented an AI recruitment platform integrated with their ATS (Greenhouse). NLP models automatically parsed and scored all incoming applications, ranking by technical skills match, experience quality, and predicted job success. The predictive model was trained on 3 years of hiring data - 240 past hires with 90-day performance reviews, 1-year retention outcomes, and hiring manager satisfaction scores. BERT-based semantic matching understood skill relationships and transferable experience. Bias auditing ensured fair treatment across demographic groups.
Results after 12 months: Time-to-hire reduced from 38 to 18 days (53% improvement). Recruiters now review only top 15% of AI-ranked candidates instead of all applications - 75% time savings. Quality of hire improved 35% measured by 90-day performance ratings. 1-year retention increased from 72% to 87% (41% attrition reduction). Cost-per-hire dropped from €5,200 to €3,100 - €380K annual savings for 120 hires. Diversity hiring increased 22% due to bias-free screening. Recruiter satisfaction improved dramatically - shifting from resume screening to relationship building and candidate experience.
AI can reduce bias when designed correctly, but can amplify it if poorly implemented. Risk: models trained on historical hiring data inherit past discrimination. Mitigation: (1) Remove protected characteristics (name, gender, age) from screening, (2) Audit model decisions across demographic groups to detect disparate impact, (3) Use fairness-aware ML algorithms with demographic parity constraints, (4) Employ explainable AI to ensure decisions based on job-relevant factors, (5) Conduct adverse impact analysis per EEOC guidelines. When implemented with these safeguards, AI typically reduces bias vs. unstructured human screening. We provide bias auditing reports and fairness monitoring as standard.
Minimum for basic job-fit scoring: 50+ past hires with resume data. Ideal for predictive success models: 200+ hires with performance outcomes tracked. For early-stage companies with limited data: (1) Start with semantic matching and skill extraction (no historical data required), (2) Use industry benchmarks and transfer learning from similar companies, (3) Begin collecting performance tracking now for future predictive models, (4) Implement simpler rule-based scoring while data accumulates. Models improve continuously as more hiring data with outcomes becomes available. Even basic resume parsing and ranking provides value immediately.
Modern NLP models handle diverse formats: PDFs, Word docs, LinkedIn profiles, even handwritten resumes (OCR). More importantly, semantic understanding identifies transferable skills and non-linear career paths. For example, a candidate transitioning from data analyst to machine learning engineer might lack exact job title match but have relevant Python, statistics, and modeling experience. Transformer models recognize these skill relationships. We also build custom models for your industry - understanding that a consultant at McKinsey transitioning to operations has valuable experience even without direct operations title. The key is training on your successful hires to learn which non-standard paths work.
AI augments recruiters rather than replacing them. Time previously spent on manual resume review (60-70% of recruiting time) shifts to higher-value activities: building relationships with top candidates, improving employer branding, developing sourcing strategies, conducting better interviews, negotiating offers, and enhancing candidate experience. Most organizations report the same number of recruiters make 2-3x more quality hires. Some companies redeploy recruiting capacity to new roles or strategic talent projects. The human judgment, empathy, and relationship-building skills of great recruiters become more valuable, not less, when administrative screening is automated.
Timeline varies by scope: Basic resume parsing and ranking: 2-4 weeks (integrate with ATS, configure job matching). Predictive hiring models: 6-8 weeks (collect historical data, train models, validate accuracy, integrate). Full platform with bias auditing and analytics: 8-12 weeks. The process: (1) ATS integration and data extraction, (2) Historical data collection and cleaning, (3) Model training and validation, (4) Pilot with 3-5 roles, (5) Recruiter training, (6) Full rollout with monitoring. We typically start with 1-2 high-volume roles for proof of concept, then expand. Most clients see ROI within first 10 hires as screening time savings and faster time-to-hire compound.
Reduce time-to-hire by 50%, improve candidate quality by 35%, and eliminate screening bias. Our team will assess your recruitment process and design a custom AI solution.
Based in Lund, Sweden • Serving businesses globally