GLOSSARY

AI Matching (candidate-mission)

AI matching uses machine learning models to evaluate a candidate's relevance to a mission by crossing skills, experience, availability and cultural fit. It replaces Boolean search.

IN DEPTH

AI matching uses machine learning and LLM models to calculate a candidate's relevance to a mission. It crosses: required technical skills, experience (years, sectors, stack), availability, target day rate, location, remote preferences, candidate history, client cultural fit. The engine returns a 0-100 score with readable justification. AI matching is bidirectional: from mission to pool (which candidates for this mission?) and from candidate to missions (which opportunities for this candidate?). It advantageously replaces Boolean search which becomes ineffective beyond 10,000 profiles. Measured impact: +25% matching quality, -60% search time. Cobalt offers native AI matching with score explanation.

Frequently asked questions

Yes. AI matching understands context and industry synonyms (e.g., "fullstack JS dev" ≈ "React/Node engineer"). Boolean search treats these profiles as different. Average accuracy: Boolean 65%, AI 92%. Plus, AI scales beyond 10,000 profiles where Boolean becomes unusable.

AI systems can reflect training data biases. Serious AI-first platforms (Cobalt) include: (1) regular bias audit, (2) score transparency, (3) final human control, (4) anonymization options for initial screening. Always complement AI with human judgment on soft skills.

Related terms

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