Reducing time-to-hire by 65% with AI-powered candidate screening and matching.
A mid-sized US recruitment firm handling 800+ job openings monthly was spending an average of 14 hours per role on manual CV screening — reading thousands of resumes to identify qualified candidates. Hiring managers complained that top candidates were being missed due to inconsistent manual evaluation. With competitors moving to AI, the firm was losing both clients and candidates to faster, smarter hiring processes.
Fine-tuned a domain-specific LLM on 50,000 historical CV-to-outcome pairs, teaching the model to score candidates against role requirements with the judgment accuracy of a senior recruiter.
Built a vector embedding pipeline using Hugging Face sentence transformers and Pinecone vector DB to semantically match candidate profiles to open roles — surfacing candidates who use different terminology but have equivalent skills.
Integrated GPT-4 to generate personalized, role-specific outreach messages and automate interview scheduling via calendar APIs — cutting recruiter time-per-candidate from 45 minutes to under 5.
Implemented an AI bias detection layer that monitors scoring distributions across demographic proxies and flags anomalies — ensuring the system promotes skills-based evaluation and compliance with EEOC guidelines.
TalentAI's platform now handles initial CV screening, candidate scoring, semantic matching, and personalized outreach automatically. Recruiters focus only on shortlisted candidates who have passed AI evaluation. Time-to-hire dropped 65%, cost-per-hire fell 40%, and the quality-of-hire score — measured by 90-day retention — improved by 28% compared to manual screening.
TalentAI changed everything about how we operate. Our recruiters used to spend half their day reading CVs — now they spend that time actually talking to qualified candidates. The quality of hires has gone up noticeably, and our clients are renewing contracts because we place people faster than anyone else in the market.